Devfest Harare 2025 Slides [GDG Harare] - Combined Slide Deck
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Oct 29, 2025
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About This Presentation
These are the slides from the speakers that presented in the 2025 edition of Devfest Harare hosted by Google Developers Group Harare.
Slide Content
01
02
03
The Modern
Developer's
Security Toolkit
The Modern Developer's
Security Toolkit
Building Safer Backends in
the Cloud
Presented by Boitumelo N.
Ngwenya
[email protected]
02
03
The Modern
Developer's
Security Toolkit
The Modern Developer's
Security Toolkit
Building Safer Backends in
the Cloud
Presented by Boitumelo N.
Ngwenya
[email protected]
The Leaky Bucket: Nightmare
●Production downtime is a common
nightmare
○Improper configuration can lead
to vulnerabilities
●Public buckets pose significant
security risks
●Data exposure from misconfigured
cloud storage
●Production downtime is a common
nightmare
○Improper configuration can lead
to vulnerabilities
●Public buckets pose significant
security risks
●Data exposure from misconfigured
cloud storage
01
02
03
04
Debunking Security Myths
Cloud
provider
handles
security is a
myth
Shared
Responsibility
Model is the
reality
You own your
application's
security in the
cloud
Security in the
cloud is your
responsibility
02
03
04
Debunking Security Myths
Cloud
provider
handles
security is a
myth
Shared
Responsibility
Model is the
reality
You own your
application's
security in the
cloud
Security in the
cloud is your
responsibility
Cloud Security
Toolkit: 4
Pillars
●Identity and Access
Management is crucial
●Secrets Management
protects sensitive data
●Network and
Infrastructure security
is vital
●Monitoring and Response
detects threats
Toolkit: 4
Pillars
●Identity and Access
Management is crucial
●Secrets Management
protects sensitive data
●Network and
Infrastructure security
is vital
●Monitoring and Response
detects threats
Pillar 1: IAM
Best Practices
●Use service accounts for
secure access
●Implement the principle
of least privilege
●Prefer IAM roles over
API keys
●Secure your cloud
environment effectively
Best Practices
●Use service accounts for
secure access
●Implement the principle
of least privilege
●Prefer IAM roles over
API keys
●Secure your cloud
environment effectively
IAM: Best Practices
●Avoid hardcoding credentials
directlyinyourcode
●Hardcoded credentials pose a
serioussecurityrisk
●UseGoogleCloudserviceaccounts
forrobustsecurity
●Service accounts enhance your
overallcloudsecurity
●Avoid hardcoding credentials
directlyinyourcode
●Hardcoded credentials pose a
serioussecurityrisk
●UseGoogleCloudserviceaccounts
forrobustsecurity
●Service accounts enhance your
overallcloudsecurity
# Bad: Hardcoded credentials in your app
# config.js
const dbConnection ={
host:'db-ip-address',
user:'admin',
password:'SuperSecret123!'
}
# Good: Cloud Run with IAM -based auth
# Cloud Run service account has cloudsql.client role
# No credentials in code!
# app.yaml
service_account:[email protected]
# Terraform: Grant minimal permissions
resource "google_project_iam_member ""cloud_sql"{
project=var.project_id
role ="roles/cloudsql.client"
member="serviceAccount:${google_service_account.app.email} "
}
# config.js
const dbConnection ={
host:'db-ip-address',
user:'admin',
password:'SuperSecret123!'
}
# Good: Cloud Run with IAM -based auth
# Cloud Run service account has cloudsql.client role
# No credentials in code!
# app.yaml
service_account:[email protected]
# Terraform: Grant minimal permissions
resource "google_project_iam_member ""cloud_sql"{
project=var.project_id
role ="roles/cloudsql.client"
member="serviceAccount:${google_service_account.app.email} "
}
Pillar 2: Secrets Management: Do's &
Don'ts
●Never store secrets in Git
repositories
●Never store secrets in Container
images
●Never store secrets in Config files
●Use Cloud Secret Manager:
Encrypted, Audited, Rotation
Don'ts
●Never store secrets in Git
repositories
●Never store secrets in Container
images
●Never store secrets in Config files
●Use Cloud Secret Manager:
Encrypted, Audited, Rotation
// NEVER: Baked into container
FROM node:18
ENV API_KEY="sk_live_abc123xyz "
ENV DB_PASSWORD="password123 "
// Still risky: Runtime env vars
docker run -e API_KEY=sk_live_abc123xyz my -app
// Secret Manager with auto -injection
// Cloud Run/Cloud Functions : Mount as volume
# cloud-run.yaml
apiVersion: serving.knative.dev/v1
kind: Service
spec:
template:
spec:
containers:
-image: gcr.io/project/app
volumeMounts:
-name: api-key
mountPath: /secrets
volumes:
-name: api-key
secret:
secretName: api-key
// In your code: Read from mounted file
const apiKey = fs.readFileSync('/secrets/api -key', 'utf8');
```
FROM node:18
ENV API_KEY="sk_live_abc123xyz "
ENV DB_PASSWORD="password123 "
// Still risky: Runtime env vars
docker run -e API_KEY=sk_live_abc123xyz my -app
// Secret Manager with auto -injection
// Cloud Run/Cloud Functions : Mount as volume
# cloud-run.yaml
apiVersion: serving.knative.dev/v1
kind: Service
spec:
template:
spec:
containers:
-image: gcr.io/project/app
volumeMounts:
-name: api-key
mountPath: /secrets
volumes:
-name: api-key
secret:
secretName: api-key
// In your code: Read from mounted file
const apiKey = fs.readFileSync('/secrets/api -key', 'utf8');
```
Pillar 3: Network
& Infrastructure
Security
●Cloud Armor (WAF)
●VPC & Private Services
●Cloud Load Balancer + SSL
●Identity-Aware Proxy (IAP)
& Infrastructure
Security
●Cloud Armor (WAF)
●VPC & Private Services
●Cloud Load Balancer + SSL
●Identity-Aware Proxy (IAP)
Secure Cloud
Backend
Architecture
Backend
Architecture
Pillar 4:
Monitoring &
Response
●Cloud Armor provides Web
Application Firewall
protection
●VPC ensures secure network
isolation for services
●Cloud Load Balancer with
SSL for traffic
distribution
●Identity-Aware Proxy
secures access to your
applications
Monitoring &
Response
●Cloud Armor provides Web
Application Firewall
protection
●VPC ensures secure network
isolation for services
●Cloud Load Balancer with
SSL for traffic
distribution
●Identity-Aware Proxy
secures access to your
applications
Monitoring & Response
●Centralized logging is essential
for security
●Proactive alerting identifies
threats quickly
●Automated responses mitigate
security incidents
●Implement robust monitoring and
response strategies
●Centralized logging is essential
for security
●Proactive alerting identifies
threats quickly
●Automated responses mitigate
security incidents
●Implement robust monitoring and
response strategies
Key Takeaways
●Embrace a Zero Trust
security model
●Centralize your secrets
for enhanced security
●Build a strong security
moat around your assets
●Monitor everything to
protect your systems
●Embrace a Zero Trust
security model
●Centralize your secrets
for enhanced security
●Build a strong security
moat around your assets
●Monitor everything to
protect your systems
Performance Testing in the
Cloud with Google Cloud
CASPER MOYO
CHANTEL CHIKANYA
Cloud with Google Cloud
CASPER MOYO
CHANTEL CHIKANYA
Introduction
Performance testing ensures that
cloud-based applications deliver
fast, reliable, and scalable
performance under varying loads. In
the cloud era, performance testing
helps identify bottlenecks early and
ensures cost-effective scalability.
Performance testing ensures that
cloud-based applications deliver
fast, reliable, and scalable
performance under varying loads. In
the cloud era, performance testing
helps identify bottlenecks early and
ensures cost-effective scalability.
Why Cloud Performance
Testing
Dynamic scalability -test how well apps handle varying
workloads
Cost optimization -measure resource usage and
spending
Reliability -validate response under real-world traffic
Global reach -test from different geographic regions
easily
Testing
Dynamic scalability -test how well apps handle varying
workloads
Cost optimization -measure resource usage and
spending
Reliability -validate response under real-world traffic
Global reach -test from different geographic regions
easily
GoogleCloudOverview
Google Cloud provides a suite of services for performance testing,
leveraging its scalable infrastructure.
Key tools include
Compute Engine -run scalable test environments
Cloud Storage -host test data
Cloud Monitoring -observe resource metrics
Cloud Load Balancing -distribute simulated traffic efficiently
Google Cloud provides a suite of services for performance testing,
leveraging its scalable infrastructure.
Key tools include
Compute Engine -run scalable test environments
Cloud Storage -host test data
Cloud Monitoring -observe resource metrics
Cloud Load Balancing -distribute simulated traffic efficiently
Performance Testing
Approaches
Load Testing -measure system behavior under
expected load
Stress Testing -find system limits by applying high load
Soak Testing -check performance over prolonged periods
Spike Testing -test response to sudden traffic surges
Approaches
Load Testing -measure system behavior under
expected load
Stress Testing -find system limits by applying high load
Soak Testing -check performance over prolonged periods
Spike Testing -test response to sudden traffic surges
Google Cloud Tools for
Testing
Cloud Test Lab (Firebase Test Lab) -automated
app testing
Cloud Trace -measures latency between services
Cloud Profiler -identifies CPU and memory
inefficiencies
Cloud Monitoring & Logging -provides real-time
insights
Testing
Cloud Test Lab (Firebase Test Lab) -automated
app testing
Cloud Trace -measures latency between services
Cloud Profiler -identifies CPU and memory
inefficiencies
Cloud Monitoring & Logging -provides real-time
insights
Architecture Example
In a Google Cloud setup
Load generators run on Compute Engine instances
Target application deployed on Kubernetes Engine
or App Engine
Load Balancer distributes test requests
Metrics collected via Cloud Monitoring
Reports analyzed using BigQuery or Data Studio
In a Google Cloud setup
Load generators run on Compute Engine instances
Target application deployed on Kubernetes Engine
or App Engine
Load Balancer distributes test requests
Metrics collected via Cloud Monitoring
Reports analyzed using BigQuery or Data Studio
Best Practices
Always define clear KPIs (response time,
throughput, error rate)
Test early and continuously
Use autoscaling for dynamic environments
Analyze both application and infrastructure layers
Simulate realistic user scenarios
Always define clear KPIs (response time,
throughput, error rate)
Test early and continuously
Use autoscaling for dynamic environments
Analyze both application and infrastructure layers
Simulate realistic user scenarios
Challenges
Managing cost of large-scale tests
Handling distributed systems and network latency
Data privacy and compliance concerns
Ensuring test reproducibility across regions
Managing cost of large-scale tests
Handling distributed systems and network latency
Data privacy and compliance concerns
Ensuring test reproducibility across regions
Case Study: Restaurant
System on Google Cloud
Cloud-based POS and restaurant management system
with multiple branches.
All branches share one codebase hosted on GCP
backend (Django REST + PostgreSQL).
High concurrency on busy days (hundreds of cashiers
using POS simultaneously).
Objective: Demonstrate cloud testing types to ensure
reliability, scalability, and performance.
System on Google Cloud
Cloud-based POS and restaurant management system
with multiple branches.
All branches share one codebase hosted on GCP
backend (Django REST + PostgreSQL).
High concurrency on busy days (hundreds of cashiers
using POS simultaneously).
Objective: Demonstrate cloud testing types to ensure
reliability, scalability, and performance.
Architecture Overview
Frontend: POS Web App hosted on Cloud Run or App
Engine.
Backend: Django REST API deployed on Google
Kubernetes Engine (GKE).
Database: Cloud SQL (PostgreSQL) for transactional
data.
Load Generators: Compute Engine VMs running Locust
for concurrent simulation.
Monitoring & Tracing: Cloud Monitoring, Cloud Trace,
Cloud Logging for visibility.
Frontend: POS Web App hosted on Cloud Run or App
Engine.
Backend: Django REST API deployed on Google
Kubernetes Engine (GKE).
Database: Cloud SQL (PostgreSQL) for transactional
data.
Load Generators: Compute Engine VMs running Locust
for concurrent simulation.
Monitoring & Tracing: Cloud Monitoring, Cloud Trace,
Cloud Logging for visibility.
Load Testing
Demonstration
Goal: Validate system performance under expected
cashier load.
Setup: 50 cashiers performing order, payment, and
report operations simultaneously.
Tools: Locust on Compute Engine simulating realistic
cashier workflows.
Metrics Tracked: Response time < 1s, error rate < 2%,
throughput consistency.
Outcome: System handled concurrent load efficiently
with stable response times.
Demonstration
Goal: Validate system performance under expected
cashier load.
Setup: 50 cashiers performing order, payment, and
report operations simultaneously.
Tools: Locust on Compute Engine simulating realistic
cashier workflows.
Metrics Tracked: Response time < 1s, error rate < 2%,
throughput consistency.
Outcome: System handled concurrent load efficiently
with stable response times.
Stress Testing
Demonstration
Goal: Identify breaking points under extreme
conditions.
Scenario: Gradually ramp cashier traffic from 50 to 500
concurrent users.
Observation: Increasing API latency after 450 requests
per second, DB saturation point identified.
Insights: Revealed bottleneck in the order processing
service and need for connection pooling optimization.
Outcome: Established upper performance limit and
defined scaling thresholds.
Demonstration
Goal: Identify breaking points under extreme
conditions.
Scenario: Gradually ramp cashier traffic from 50 to 500
concurrent users.
Observation: Increasing API latency after 450 requests
per second, DB saturation point identified.
Insights: Revealed bottleneck in the order processing
service and need for connection pooling optimization.
Outcome: Established upper performance limit and
defined scaling thresholds.
Scalability Testing
Demonstration
Goal: Ensure automatic scaling policies are effective.
Setup: Horizontal Pod Autoscaler configured on GKE
backend pods.
Test: Traffic increased dynamically from 20 to 300
cashiers.
Observation: New pods created automatically;
response times stabilized.
Outcome: Verified that auto-scaling maintained
service availability with minimal latency fluctuations.
Demonstration
Goal: Ensure automatic scaling policies are effective.
Setup: Horizontal Pod Autoscaler configured on GKE
backend pods.
Test: Traffic increased dynamically from 20 to 300
cashiers.
Observation: New pods created automatically;
response times stabilized.
Outcome: Verified that auto-scaling maintained
service availability with minimal latency fluctuations.
Failover Testing
Demonstration
Goal: Validate system resilience during component
failure.
Scenario: Simulated Compute Engine node and Cloud
SQL failover.
Observation: Cloud Load Balancer rerouted traffic to
healthy nodes instantly.
Outcome: Minimal disruption (<5 seconds downtime),
automatic Cloud SQL failover executed successfully.
Insight: Demonstrated fault tolerance and redundancy
in GCP environment.
Demonstration
Goal: Validate system resilience during component
failure.
Scenario: Simulated Compute Engine node and Cloud
SQL failover.
Observation: Cloud Load Balancer rerouted traffic to
healthy nodes instantly.
Outcome: Minimal disruption (<5 seconds downtime),
automatic Cloud SQL failover executed successfully.
Insight: Demonstrated fault tolerance and redundancy
in GCP environment.
GCP Tools Used in Testing
Compute Engine: Hosts Locust load generators.
Google Kubernetes Engine (GKE): Manages containerized
backend services.
Cloud Run: Runs stateless frontend POS web app.
Cloud SQL: Provides reliable, scalable database for
transactions.
Cloud Monitoring & Logging: Tracks metrics, logs, and
anomalies.
Cloud Trace: Visualizes latency and performance
bottlenecks.
BigQuery: Aggregates and analyzes test reports for insights.
Compute Engine: Hosts Locust load generators.
Google Kubernetes Engine (GKE): Manages containerized
backend services.
Cloud Run: Runs stateless frontend POS web app.
Cloud SQL: Provides reliable, scalable database for
transactions.
Cloud Monitoring & Logging: Tracks metrics, logs, and
anomalies.
Cloud Trace: Visualizes latency and performance
bottlenecks.
BigQuery: Aggregates and analyzes test reports for insights.
Detailed Testing Metrics &
Results
Load Testing: Stable response time under 150
concurrent cashiers.
stress Testing: Identified peak throughput at 480 RPS
before latency spikes.
Scalability Testing: Autoscaling improved performance
stability by 40%.
Failover Testing: Achieved recovery time <10 seconds
with zero data loss.
Result: System demonstrated production-grade
reliability at cloud scale.
Results
Load Testing: Stable response time under 150
concurrent cashiers.
stress Testing: Identified peak throughput at 480 RPS
before latency spikes.
Scalability Testing: Autoscaling improved performance
stability by 40%.
Failover Testing: Achieved recovery time <10 seconds
with zero data loss.
Result: System demonstrated production-grade
reliability at cloud scale.
Integrating Testing into
CI/CD
Cloud Build automates build, test, and deploy
pipelines.
Performance test step integrated before staging
deployment.
Test reports stored in Cloud Storage and analyzed via
BigQuery.
Deployment triggered only if all tests pass quality
thresholds.
Ensures continuous performance assurance throughout
release cycles.
CI/CD
Cloud Build automates build, test, and deploy
pipelines.
Performance test step integrated before staging
deployment.
Test reports stored in Cloud Storage and analyzed via
BigQuery.
Deployment triggered only if all tests pass quality
thresholds.
Ensures continuous performance assurance throughout
release cycles.
Best Practices &
Recommendations
Automate all test scenarios using Cloud Build and
Cloud Scheduler.
Use synthetic monitoring in production for early
detection of latency issues.
Establish KPIs (response time, throughput, cost
efficiency).
Use realistic cashier workloads and seasonal peaks in
test design.
Continuously review test reports and optimize
application components.
Recommendations
Automate all test scenarios using Cloud Build and
Cloud Scheduler.
Use synthetic monitoring in production for early
detection of latency issues.
Establish KPIs (response time, throughput, cost
efficiency).
Use realistic cashier workloads and seasonal peaks in
test design.
Continuously review test reports and optimize
application components.
Conclusion: Cloud Testing
for the Restaurant System
GCP enables real-world, large-scale performance
testing at low cost.
Provides scalability, fault tolerance, and global test
coverage.
Demonstrations proved stability under concurrent
cashier operations.
Cloud-native testing ensures smooth business
operations even on peak days.
Next Step: Expand automated tests to include finance,
inventory, and kitchen modules.
for the Restaurant System
GCP enables real-world, large-scale performance
testing at low cost.
Provides scalability, fault tolerance, and global test
coverage.
Demonstrations proved stability under concurrent
cashier operations.
Cloud-native testing ensures smooth business
operations even on peak days.
Next Step: Expand automated tests to include finance,
inventory, and kitchen modules.
preencoded. png
Optimizing Business
Performance Through Data-
Driven Frameworks
Harare DevFest 2025 | By Tendai Kachidza -EPM Consultant
Optimizing Business
Performance Through Data-
Driven Frameworks
Harare DevFest 2025 | By Tendai Kachidza -EPM Consultant
preencoded. png
The Developer's Dilemma
Critical Business Challenges
Data fragmentation-Information trapped in silos across departments, preventing holistic insights
Disconnected workflows-Manual processes that waste time and introduce errors
Inconsistent decision-making-Lack of real-time insights leading to reactive rather than proactive strategies
Legacy system integration difficulties-Older systems struggle to connect with modern solutions, hindering seamless operations
Lack of real-time performance monitoring-Inability to track and respond to system performance issues promptly
Poor data quality and accuracy issues-Unreliable data leading to flawed analysis and poor decisions
Limited cross-departmental collaboration-Barriers between teams preventing shared knowledge and synergistic efforts
Inadequate cybersecurity measures-Vulnerabilities that expose sensitive data and disrupt business continuity
Difficulty measuring ROI on technology investments-Struggling to quantify the financial return of new technological solutions
Developers build solutions every day, but why?Understanding the business context transforms code from a technical exercise into a strategic
asset that drives real organizational value.
Our mission is clear: Create scalable, secure, and intelligent solutionsthat directly improve business performance and deliver measurable
outcomes.
The Developer's Dilemma
Critical Business Challenges
Data fragmentation-Information trapped in silos across departments, preventing holistic insights
Disconnected workflows-Manual processes that waste time and introduce errors
Inconsistent decision-making-Lack of real-time insights leading to reactive rather than proactive strategies
Legacy system integration difficulties-Older systems struggle to connect with modern solutions, hindering seamless operations
Lack of real-time performance monitoring-Inability to track and respond to system performance issues promptly
Poor data quality and accuracy issues-Unreliable data leading to flawed analysis and poor decisions
Limited cross-departmental collaboration-Barriers between teams preventing shared knowledge and synergistic efforts
Inadequate cybersecurity measures-Vulnerabilities that expose sensitive data and disrupt business continuity
Difficulty measuring ROI on technology investments-Struggling to quantify the financial return of new technological solutions
Developers build solutions every day, but why?Understanding the business context transforms code from a technical exercise into a strategic
asset that drives real organizational value.
Our mission is clear: Create scalable, secure, and intelligent solutionsthat directly improve business performance and deliver measurable
outcomes.
preencoded. png
Why Business Optimization Matters for Developers
Businesses invest in technology solutions to reduce costs, improve agility, and drive growth. If solutions don't connect to these outcomes, they fail—
no matter how technically advanced or elegantly coded they may be.
Impact
How does this improve planning, reporting,
and operations? Every line of code should
contribute to tangible business
improvements.
Longevity
Will this scale as the business grows?
Design with tomorrow's challenges in mind,
not just today's requirements.
Security
Is data protected while enabling insights?
Balance accessibility with robust security
measures and compliance.
"Technical excellence without business alignment is just expensive experimentation. Developers must bridge the gap between code and
commerce."
Why Business Optimization Matters for Developers
Businesses invest in technology solutions to reduce costs, improve agility, and drive growth. If solutions don't connect to these outcomes, they fail—
no matter how technically advanced or elegantly coded they may be.
Impact
How does this improve planning, reporting,
and operations? Every line of code should
contribute to tangible business
improvements.
Longevity
Will this scale as the business grows?
Design with tomorrow's challenges in mind,
not just today's requirements.
Security
Is data protected while enabling insights?
Balance accessibility with robust security
measures and compliance.
"Technical excellence without business alignment is just expensive experimentation. Developers must bridge the gap between code and
commerce."
preencoded. png
The Data-Driven Framework Lifecycle
A comprehensive approach to transforming raw data into actionable business intelligence requires a structured, repeatable process. Each stage builds upon the previous one, creating a robust foundation for
data-driven decision-making.
Data Extraction
Pulling data from multiple sources—ERP systems, CRM platforms,
IoT devices, and external APIs.
AI Enhancement:Machine learning algorithms automatically
identify new data sources and optimize extraction schedules based
on usage patterns and data freshness requirements.
Data Cleaning & Integration
Standardize formats, remove duplicates, resolve inconsistencies
across disparate systems.
AI Enhancement:Natural language processing detects anomalies
and suggests data quality improvements. AI models learn from
historical corrections to automate future cleaning tasks.
Modeling & Governance
Define KPIs, apply access rules, ensure compliance with
regulations like GDPR and industry standards.
AI Enhancement:AI-powered governance tools automatically
classify sensitive data, recommend access policies, and
monitor compliance in real-time, flagging potential violations
before they occur.
Analysis & Insights
Create dashboards, generate AI-driven predictions, perform trend
analysis to uncover hidden patterns.
AI Enhancement:Advanced analytics engines use predictive
modeling to forecast trends, detect anomalies, and provide natural
language explanations of complex data relationships.
Action & Automation
Trigger workflows, optimize processes, enable rapid decision-
making based on real-time insights.
AI Enhancement:Intelligent automation systems recommend
actions, trigger workflows based on predictive insights, and
continuously optimize business processes through reinforcement
learning.
The Data-Driven Framework Lifecycle
A comprehensive approach to transforming raw data into actionable business intelligence requires a structured, repeatable process. Each stage builds upon the previous one, creating a robust foundation for
data-driven decision-making.
Data Extraction
Pulling data from multiple sources—ERP systems, CRM platforms,
IoT devices, and external APIs.
AI Enhancement:Machine learning algorithms automatically
identify new data sources and optimize extraction schedules based
on usage patterns and data freshness requirements.
Data Cleaning & Integration
Standardize formats, remove duplicates, resolve inconsistencies
across disparate systems.
AI Enhancement:Natural language processing detects anomalies
and suggests data quality improvements. AI models learn from
historical corrections to automate future cleaning tasks.
Modeling & Governance
Define KPIs, apply access rules, ensure compliance with
regulations like GDPR and industry standards.
AI Enhancement:AI-powered governance tools automatically
classify sensitive data, recommend access policies, and
monitor compliance in real-time, flagging potential violations
before they occur.
Analysis & Insights
Create dashboards, generate AI-driven predictions, perform trend
analysis to uncover hidden patterns.
AI Enhancement:Advanced analytics engines use predictive
modeling to forecast trends, detect anomalies, and provide natural
language explanations of complex data relationships.
Action & Automation
Trigger workflows, optimize processes, enable rapid decision-
making based on real-time insights.
AI Enhancement:Intelligent automation systems recommend
actions, trigger workflows based on predictive insights, and
continuously optimize business processes through reinforcement
learning.
preencoded. png
From Code to Business Value
The most impactful developers think beyond APIs, databases, and dashboards. They ask fundamental questions that connect technology to business
outcomes and demonstrate clear value to stakeholders.
Reduce Manual Work
How does this solution eliminate repetitive
tasks and free up human resources for
strategic activities?
Automation should target high-volume,
low-value processes first.
Enable Real-Time Visibility
How does this give executives and
managers immediate access to critical
business metrics?
Decision-makers need current data, not
last week's reports.
Protect Sensitive Data
How does this ensure compliance and
security while maintaining accessibility?
Data protection isn't optional , it's a
fundamental requirement.
The Mindset Shift
Code-First Thinking
"I built a dashboard with real-time data visualization using React and
Power Bi connections."
Business-First Thinking
"I built a financial reporting solution that reduced month-end close time by
40%, saving the finance team 32 hours per month."
From Code to Business Value
The most impactful developers think beyond APIs, databases, and dashboards. They ask fundamental questions that connect technology to business
outcomes and demonstrate clear value to stakeholders.
Reduce Manual Work
How does this solution eliminate repetitive
tasks and free up human resources for
strategic activities?
Automation should target high-volume,
low-value processes first.
Enable Real-Time Visibility
How does this give executives and
managers immediate access to critical
business metrics?
Decision-makers need current data, not
last week's reports.
Protect Sensitive Data
How does this ensure compliance and
security while maintaining accessibility?
Data protection isn't optional , it's a
fundamental requirement.
The Mindset Shift
Code-First Thinking
"I built a dashboard with real-time data visualization using React and
Power Bi connections."
Business-First Thinking
"I built a financial reporting solution that reduced month-end close time by
40%, saving the finance team 32 hours per month."
preencoded. png
The Business Reality Check
When pitching solutions to major organizations like Coca-Cola or Telecel, developers face scrutiny from multiple perspectives. Each stakeholder
evaluates technology through their unique lens of business priorities and concerns.
CFO (Chief Financial
Officer)
"How will this help us forecast revenue
more accurately and improve our financial
planning processes?"
COO (Chief Operating Officer)
"Will this reduce bottlenecks in production
and supply chain? Can we measure
efficiency gains?"
CTO (Chief Technology Officer)
"Is this solution secure, scalable, and
maintainable? Can it handle our projected
growth over the next 5 years?"
CEO (Chief Executive Officer)
"How does this impact profitability and decision-making speed?
What's the ROI and strategic advantage?"
Head of Marketing
"Can I see consumer insights faster than before? Will this help us
respond to market trends in real-time?"
Understanding these perspectives transforms how developers design, present, and implementsolutions. Every technical decision should have a
clear answer to at least one of these questions.
The Business Reality Check
When pitching solutions to major organizations like Coca-Cola or Telecel, developers face scrutiny from multiple perspectives. Each stakeholder
evaluates technology through their unique lens of business priorities and concerns.
CFO (Chief Financial
Officer)
"How will this help us forecast revenue
more accurately and improve our financial
planning processes?"
COO (Chief Operating Officer)
"Will this reduce bottlenecks in production
and supply chain? Can we measure
efficiency gains?"
CTO (Chief Technology Officer)
"Is this solution secure, scalable, and
maintainable? Can it handle our projected
growth over the next 5 years?"
CEO (Chief Executive Officer)
"How does this impact profitability and decision-making speed?
What's the ROI and strategic advantage?"
Head of Marketing
"Can I see consumer insights faster than before? Will this help us
respond to market trends in real-time?"
Understanding these perspectives transforms how developers design, present, and implementsolutions. Every technical decision should have a
clear answer to at least one of these questions.
preencoded. png
Case Study : A Telecom Company in Crisis
A major telecommunications operator struggled with fragmented customer data scattered across legacy systems, countless spreadsheets maintained manually by different
departments, and zero visibility into customer behavior patterns. Decision-making was reactive, slow, and often based on outdated information.
Before: The Problems
•Customer data fragmented across 12+ systems
•Manual spreadsheet processes prone to errors
•No predictive insights into customer behavior
•Reporting took 10+ days to compile
•High customer churn with no early warning
•Compliance risks from inconsistent data handling
After: The Solution
•Unified cloud-based data hub integrating all sources
•AI-powered customer churn prediction models
•Automated reporting workflows with real-time updates
•Secure governance framework with role-based access
•Self-service analytics for business users
•Compliance monitoring and audit trails
12%
Churn Reduction
Customer retention improved through predictive
interventions
80%
Faster Reporting
From 10 days to 2 days, enabling agile decision-making
95%
Real Time Dashboard Reports
Near real-time insights supporting strategic decisions
Case Study : A Telecom Company in Crisis
A major telecommunications operator struggled with fragmented customer data scattered across legacy systems, countless spreadsheets maintained manually by different
departments, and zero visibility into customer behavior patterns. Decision-making was reactive, slow, and often based on outdated information.
Before: The Problems
•Customer data fragmented across 12+ systems
•Manual spreadsheet processes prone to errors
•No predictive insights into customer behavior
•Reporting took 10+ days to compile
•High customer churn with no early warning
•Compliance risks from inconsistent data handling
After: The Solution
•Unified cloud-based data hub integrating all sources
•AI-powered customer churn prediction models
•Automated reporting workflows with real-time updates
•Secure governance framework with role-based access
•Self-service analytics for business users
•Compliance monitoring and audit trails
12%
Churn Reduction
Customer retention improved through predictive
interventions
80%
Faster Reporting
From 10 days to 2 days, enabling agile decision-making
95%
Real Time Dashboard Reports
Near real-time insights supporting strategic decisions
preencoded. png
Practical Tips for Developers
Building business-focused solutions requires a shift in approach , from technology-first to outcome-first thinking. These practical guidelines help developers create solutions that deliver lasting business value.
1
Start with the Business Pain Point
Always begin by understanding the business problem before writing a single line of code. Interview stakeholders, observe workflows, and identify the root cause, not just symptoms. The best technical
solution to the wrong problem is still a failure.
2
Design for Scalability
Build cloud-native, modular architectures that grow with the business. Use microservices, containerization, and serverless technologies where appropriate. Today's solution should handle tomorrow's volume
without complete rewrites.
3
Prioritize Security from Day One
Implement role-based access control, data encryption at rest and in transit, and comprehensive audit logging. Security isn't a feature to add later , it's a foundational requirement that must be baked into
every layer.
4
Make Insights Understandable
Create clear dashboards with intuitive visualizations. Avoid black-box AI, provide explanations for predictions and recommendations. Business users should trust and understand the insights they're acting
upon.
5
Collaborate with both IT and Business ventures
Build solutions with input from technical teams and business stakeholders. Regular feedback loops ensure alignment with both technical standards and business requirements throughout development.
"Simplicity scales better than over-engineering."The most elegant solution is often the simplest one that fully addresses the business need.
Practical Tips for Developers
Building business-focused solutions requires a shift in approach , from technology-first to outcome-first thinking. These practical guidelines help developers create solutions that deliver lasting business value.
1
Start with the Business Pain Point
Always begin by understanding the business problem before writing a single line of code. Interview stakeholders, observe workflows, and identify the root cause, not just symptoms. The best technical
solution to the wrong problem is still a failure.
2
Design for Scalability
Build cloud-native, modular architectures that grow with the business. Use microservices, containerization, and serverless technologies where appropriate. Today's solution should handle tomorrow's volume
without complete rewrites.
3
Prioritize Security from Day One
Implement role-based access control, data encryption at rest and in transit, and comprehensive audit logging. Security isn't a feature to add later , it's a foundational requirement that must be baked into
every layer.
4
Make Insights Understandable
Create clear dashboards with intuitive visualizations. Avoid black-box AI, provide explanations for predictions and recommendations. Business users should trust and understand the insights they're acting
upon.
5
Collaborate with both IT and Business ventures
Build solutions with input from technical teams and business stakeholders. Regular feedback loops ensure alignment with both technical standards and business requirements throughout development.
"Simplicity scales better than over-engineering."The most elegant solution is often the simplest one that fully addresses the business need.
preencoded. png
We are Business Enablers
As developers we are not just coders , we are business enablerswho transform organizational capabilities through technology. AI and cloud platforms make us faster and more
powerful, but we must always anchor our work in business outcomesthat matter to stakeholders.
A well-implemented data-driven framework ensures that every solution delivers on three critical dimensions:
Safe, Secure, Scalable
Built on robust foundations that protect data, ensure
compliance, and grow with business needs
Valuable to Business
Directly contributes to cost reduction, revenue growth,
or competitive advantage
Built with Purpose
Every feature and function serves a clear business
objective
Your Call to Action
Next time you design a solution, challenge yourself with these three essential questions:
01
What business problem am I solving?
Be specific about the pain point and the stakeholders
affected
02
How do I make it scalable and secure?
Design for growth and protection from the foundation
03
How do I ensure it drives measurable
business performance?
Define success metrics and track impact over time
That's how as developers we can truly optimize business performance with data-driven frameworks.
We are Business Enablers
As developers we are not just coders , we are business enablerswho transform organizational capabilities through technology. AI and cloud platforms make us faster and more
powerful, but we must always anchor our work in business outcomesthat matter to stakeholders.
A well-implemented data-driven framework ensures that every solution delivers on three critical dimensions:
Safe, Secure, Scalable
Built on robust foundations that protect data, ensure
compliance, and grow with business needs
Valuable to Business
Directly contributes to cost reduction, revenue growth,
or competitive advantage
Built with Purpose
Every feature and function serves a clear business
objective
Your Call to Action
Next time you design a solution, challenge yourself with these three essential questions:
01
What business problem am I solving?
Be specific about the pain point and the stakeholders
affected
02
How do I make it scalable and secure?
Design for growth and protection from the foundation
03
How do I ensure it drives measurable
business performance?
Define success metrics and track impact over time
That's how as developers we can truly optimize business performance with data-driven frameworks.
VINCENT MARAZANYE | HEAD-
SOLUTION ARCHITECTURE
AI ENHANCED
COMMUNICATION
SOLUTION ARCHITECTURE
AI ENHANCED
COMMUNICATION
OVERVIEW
Key Highlights:
●Demonstrate how Model Context Protocol (MCP) can drive AI
enhanced communication
●Focus on Customer Experience (CX)
●Drive Business Growth and Productivity
Key Highlights:
●Demonstrate how Model Context Protocol (MCP) can drive AI
enhanced communication
●Focus on Customer Experience (CX)
●Drive Business Growth and Productivity
CUSTOMER CENTRIC COMMS
Strategic Priorities:
●Creating engaging communication
●Ensuring scalability in terms of comms audience reach
●Branch into different communication channels-SMS, WA, Email, etc
●Ensure platform security and safety
Strategic Priorities:
●Creating engaging communication
●Ensuring scalability in terms of comms audience reach
●Branch into different communication channels-SMS, WA, Email, etc
●Ensure platform security and safety
MCP IN COMMS
MCP IN COMMS
MCP IN COMMS-WHATSAPP
MCP IN COMMS
Conclusion
Using MCP and AI Agent frameworks :
●Can drive business revenue
●Enhance Customer Experience
●Quicken implementation of comms over different channels
●Automate tasks
Using MCP and AI Agent frameworks :
●Can drive business revenue
●Enhance Customer Experience
●Quicken implementation of comms over different channels
●Automate tasks
THE END
Designing
Experiences for :
Scalable, Ethical, and Practical
Human-Centered AI
Developers
Beyond Bechani
Senior Product Designer | Championing Human-Centered Experiences
Experiences for :
Scalable, Ethical, and Practical
Human-Centered AI
Developers
Beyond Bechani
Senior Product Designer | Championing Human-Centered Experiences
7+ Beyond Bechani | DevFest 2025 | Harare
The Great Designer–Developer War️
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
Why Human-Centered AI Matters
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
70% of AI projects fail due to
adoption issues
AI and cloud solutions can be incredibly powerful, but adoption is where most projects fail. Users often don’t trust,
understand, or engage with AI outputs, even if the system is technically perfect.
Human-centered design ensures your hard work in AI actually reaches and benefits real people. It’s not just about
models and code; it’s about people interacting with your product.
Beyond Bechani | DevFest 2025 | Harare
adoption issues
AI and cloud solutions can be incredibly powerful, but adoption is where most projects fail. Users often don’t trust,
understand, or engage with AI outputs, even if the system is technically perfect.
Human-centered design ensures your hard work in AI actually reaches and benefits real people. It’s not just about
models and code; it’s about people interacting with your product.
Beyond Bechani | DevFest 2025 | Harare
Developers’ Challenges
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
Technical Complexity Maintenance & Iteration User Adoption & Trust Scalability Ethics & bias Beyond Bechani | DevFest 2025 | Harare
Core Principles of
Human-Centered AI (HCAI)
Beyond Bechani | DevFest 2025 | Harare
Human-Centered AI (HCAI)
Beyond Bechani | DevFest 2025 | Harare
Usability Transparency Ethics &
Inclusion Collaborative Beyond Bechani | DevFest 2025 | Harare
Inclusion Collaborative Beyond Bechani | DevFest 2025 | Harare
UX Patterns Developers
Can Reuse
Beyond Bechani | DevFest 2025 | Harare
Can Reuse
Beyond Bechani | DevFest 2025 | Harare
How do you currently explain AI
outputs to users?
Beyond Bechani | DevFest 2025 | Harare
outputs to users?
Beyond Bechani | DevFest 2025 | Harare
You don’t need a designer to make AI usable. Key patterns developers
can implement:Clear feedback
loops for AI
outputs Onboarding
flows to set
expectations Graceful error
handling with
helpful guidance
Beyond Bechani | DevFest 2025 | Harare
can implement:Clear feedback
loops for AI
outputs Onboarding
flows to set
expectations Graceful error
handling with
helpful guidance
Beyond Bechani | DevFest 2025 | Harare
Scalable AI Workflows: Code, Data, and
Interaction Patterns Growing Together
Beyond Bechani | DevFest 2025 | Harare
Interaction Patterns Growing Together
Beyond Bechani | DevFest 2025 | Harare
Scalability isn’t just about servers or AI models..........
it’s about making systems that grow technically and
remain human-centered.
Beyond Bechani | DevFest 2025 | Harare
it’s about making systems that grow technically and
remain human-centered.
Beyond Bechani | DevFest 2025 | Harare
Ethical & Safe AI Interactions
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
Ethics is product quality:Trust Bias Privacy
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
Collaboration Blueprint
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
Practical tips for collaboration:
Share design systems early
Iterate with small prototypes
Align on performance and accessibility
Continuous feedback loops
Building human-centered AI isn’t a solo effort,
it’s a partnership. Developers and designers
must work together from the start.
Beyond Bechani | DevFest 2025 | Harare
Share design systems early
Iterate with small prototypes
Align on performance and accessibility
Continuous feedback loops
Building human-centered AI isn’t a solo effort,
it’s a partnership. Developers and designers
must work together from the start.
Beyond Bechani | DevFest 2025 | Harare
Quick Wins for Developers
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
Even small improvements in your
AI workflows make a big difference:
Add clear explanations for AI outputs
helps users trust the system.
Implement consistent interaction patterns
reduces errors and confusion.
Add lightweight feedback loops
capture user input to improve AI over time.
Test with a few real users
5–10 minutes can uncover big adoption blockers.
Beyond Bechani | DevFest 2025 | Harare
AI workflows make a big difference:
Add clear explanations for AI outputs
helps users trust the system.
Implement consistent interaction patterns
reduces errors and confusion.
Add lightweight feedback loops
capture user input to improve AI over time.
Test with a few real users
5–10 minutes can uncover big adoption blockers.
Beyond Bechani | DevFest 2025 | Harare
Adoption | Ethics | Collaboration
Beyond Bechani | DevFest 2025 | Harare
Beyond Bechani | DevFest 2025 | Harare
Thank you, !
Let’s keep building AI
that’s , ,
and — together.
DevFest
scalableethical
practical
Let’s keep building AI
that’s , ,
and — together.
DevFest
scalableethical
practical
Optimizing Business
Performance Through Data-
Driven Frameworks
Harare DevFest 2025 | By Tendai Kachidza - EPM Consultant
Performance Through Data-
Driven Frameworks
Harare DevFest 2025 | By Tendai Kachidza - EPM Consultant
The Developer's Dilemma
Critical Business Challenges
Data fragmentation - Information trapped in silos across departments, preventing holistic insights
Disconnected workflows - Manual processes that waste time and introduce errors
Inconsistent decision-making - Lack of real-time insights leading to reactive rather than proactive strategies
Legacy system integration difficulties - Older systems struggle to connect with modern solutions, hindering seamless operations
Lack of real-time performance monitoring - Inability to track and respond to system performance issues promptly
Poor data quality and accuracy issues - Unreliable data leading to flawed analysis and poor decisions
Limited cross-departmental collaboration - Barriers between teams preventing shared knowledge and synergistic efforts
Inadequate cybersecurity measures - Vulnerabilities that expose sensitive data and disrupt business continuity
Difficulty measuring ROI on technology investments - Struggling to quantify the financial return of new technological solutions
Developers build solutions every day, but why? Understanding the business context transforms code from a technical exercise into a strategic asset
that drives real organizational value.
Our mission is clear: Create scalable, secure, and intelligent solutions that directly improve business performance and deliver measurable outcomes.
Critical Business Challenges
Data fragmentation - Information trapped in silos across departments, preventing holistic insights
Disconnected workflows - Manual processes that waste time and introduce errors
Inconsistent decision-making - Lack of real-time insights leading to reactive rather than proactive strategies
Legacy system integration difficulties - Older systems struggle to connect with modern solutions, hindering seamless operations
Lack of real-time performance monitoring - Inability to track and respond to system performance issues promptly
Poor data quality and accuracy issues - Unreliable data leading to flawed analysis and poor decisions
Limited cross-departmental collaboration - Barriers between teams preventing shared knowledge and synergistic efforts
Inadequate cybersecurity measures - Vulnerabilities that expose sensitive data and disrupt business continuity
Difficulty measuring ROI on technology investments - Struggling to quantify the financial return of new technological solutions
Developers build solutions every day, but why? Understanding the business context transforms code from a technical exercise into a strategic asset
that drives real organizational value.
Our mission is clear: Create scalable, secure, and intelligent solutions that directly improve business performance and deliver measurable outcomes.
Why Business Optimization Matters for Developers
Businesses invest in technology solutions to reduce costs, improve agility, and drive growth. If solutions don't connect to these outcomes, they fail4no
matter how technically advanced or elegantly coded they may be.
Impact
How does this improve planning, reporting,
and operations? Every line of code should
contribute to tangible business
improvements.
Longevity
Will this scale as the business grows? Design
with tomorrow's challenges in mind, not just
today's requirements.
Security
Is data protected while enabling insights?
Balance accessibility with robust security
measures and compliance.
"Technical excellence without business alignment is just expensive experimentation. Developers must bridge the gap between code and commerce."
Businesses invest in technology solutions to reduce costs, improve agility, and drive growth. If solutions don't connect to these outcomes, they fail4no
matter how technically advanced or elegantly coded they may be.
Impact
How does this improve planning, reporting,
and operations? Every line of code should
contribute to tangible business
improvements.
Longevity
Will this scale as the business grows? Design
with tomorrow's challenges in mind, not just
today's requirements.
Security
Is data protected while enabling insights?
Balance accessibility with robust security
measures and compliance.
"Technical excellence without business alignment is just expensive experimentation. Developers must bridge the gap between code and commerce."
The Data-Driven Framework Lifecycle
A comprehensive approach to transforming raw data into actionable business intelligence requires a structured, repeatable process. Each stage builds
upon the previous one, creating a robust foundation for data-driven decision-making.
Data Extraction
Pulling data from multiple sources4ERP systems,
CRM platforms, IoT devices, and external APIs.
AI Enhancement: Machine learning algorithms
automatically identify new data sources and
optimize extraction schedules based on usage
patterns and data freshness requirements.
Data Cleaning & Integration
Standardize formats, remove duplicates, resolve
inconsistencies across disparate systems.
AI Enhancement: Natural language processing
detects anomalies and suggests data quality
improvements. AI models learn from historical
corrections to automate future cleaning tasks.
Modeling & Governance
Define KPIs, apply access rules, ensure
compliance with regulations like GDPR and
industry standards.
AI Enhancement: AI-powered governance
tools automatically classify sensitive data,
recommend access policies, and monitor
compliance in real-time, flagging potential
violations before they occur.
Analysis & Insights
Create dashboards, generate AI-driven
predictions, perform trend analysis to uncover
hidden patterns.
AI Enhancement: Advanced analytics engines use
predictive modeling to forecast trends, detect
anomalies, and provide natural language
explanations of complex data relationships.
Action & Automation
Trigger workflows, optimize processes, enable
rapid decision-making based on real-time
insights.
AI Enhancement: Intelligent automation systems
recommend actions, trigger workflows based on
predictive insights, and continuously optimize
business processes through reinforcement
learning.
A comprehensive approach to transforming raw data into actionable business intelligence requires a structured, repeatable process. Each stage builds
upon the previous one, creating a robust foundation for data-driven decision-making.
Data Extraction
Pulling data from multiple sources4ERP systems,
CRM platforms, IoT devices, and external APIs.
AI Enhancement: Machine learning algorithms
automatically identify new data sources and
optimize extraction schedules based on usage
patterns and data freshness requirements.
Data Cleaning & Integration
Standardize formats, remove duplicates, resolve
inconsistencies across disparate systems.
AI Enhancement: Natural language processing
detects anomalies and suggests data quality
improvements. AI models learn from historical
corrections to automate future cleaning tasks.
Modeling & Governance
Define KPIs, apply access rules, ensure
compliance with regulations like GDPR and
industry standards.
AI Enhancement: AI-powered governance
tools automatically classify sensitive data,
recommend access policies, and monitor
compliance in real-time, flagging potential
violations before they occur.
Analysis & Insights
Create dashboards, generate AI-driven
predictions, perform trend analysis to uncover
hidden patterns.
AI Enhancement: Advanced analytics engines use
predictive modeling to forecast trends, detect
anomalies, and provide natural language
explanations of complex data relationships.
Action & Automation
Trigger workflows, optimize processes, enable
rapid decision-making based on real-time
insights.
AI Enhancement: Intelligent automation systems
recommend actions, trigger workflows based on
predictive insights, and continuously optimize
business processes through reinforcement
learning.
From Code to Business Value
The most impactful developers think beyond APIs, databases, and dashboards. They ask fundamental questions that connect technology to business
outcomes and demonstrate clear value to stakeholders.
Reduce Manual Work
How does this solution eliminate repetitive
tasks and free up human resources for
strategic activities?
Automation should target high-volume,
low-value processes first.
Enable Real-Time Visibility
How does this give executives and
managers immediate access to critical
business metrics?
Decision-makers need current data, not
last week's reports.
Protect Sensitive Data
How does this ensure compliance and
security while maintaining accessibility?
Data protection isn't optional , it's a
fundamental requirement.
The Mindset Shift
o Code-First Thinking
"I built a dashboard with real-time data visualization using React and
Power Bi connections."
' Business-First Thinking
"I built a financial reporting solution that reduced month-end close time by
40%, saving the finance team 32 hours per month."
The most impactful developers think beyond APIs, databases, and dashboards. They ask fundamental questions that connect technology to business
outcomes and demonstrate clear value to stakeholders.
Reduce Manual Work
How does this solution eliminate repetitive
tasks and free up human resources for
strategic activities?
Automation should target high-volume,
low-value processes first.
Enable Real-Time Visibility
How does this give executives and
managers immediate access to critical
business metrics?
Decision-makers need current data, not
last week's reports.
Protect Sensitive Data
How does this ensure compliance and
security while maintaining accessibility?
Data protection isn't optional , it's a
fundamental requirement.
The Mindset Shift
o Code-First Thinking
"I built a dashboard with real-time data visualization using React and
Power Bi connections."
' Business-First Thinking
"I built a financial reporting solution that reduced month-end close time by
40%, saving the finance team 32 hours per month."
The Business Reality Check
When pitching solutions to major organizations like Coca-Cola or Telecel, developers face scrutiny from multiple perspectives. Each stakeholder evaluates
technology through their unique lens of business priorities and concerns.
Understanding these perspectives transforms how developers design, present, and implement solutions. Every technical decision should have a clear
answer to at least one of these questions.
CFO (Chief Financial Officer)
"How will this help us forecast revenue more
accurately and improve our financial
planning processes?"
COO (Chief Operating Officer)
"Will this reduce bottlenecks in production
and supply chain? Can we measure
efficiency gains?"
CTO (Chief Technology Officer)
"Is this solution secure, scalable, and
maintainable? Can it handle our projected
growth over the next 5 years?"
CEO (Chief Executive Officer)
"How does this impact profitability and decision-making speed? What's
the ROI and strategic advantage?"
Head of Marketing
"Can I see consumer insights faster than before? Will this help us
respond to market trends in real-time?"
When pitching solutions to major organizations like Coca-Cola or Telecel, developers face scrutiny from multiple perspectives. Each stakeholder evaluates
technology through their unique lens of business priorities and concerns.
Understanding these perspectives transforms how developers design, present, and implement solutions. Every technical decision should have a clear
answer to at least one of these questions.
CFO (Chief Financial Officer)
"How will this help us forecast revenue more
accurately and improve our financial
planning processes?"
COO (Chief Operating Officer)
"Will this reduce bottlenecks in production
and supply chain? Can we measure
efficiency gains?"
CTO (Chief Technology Officer)
"Is this solution secure, scalable, and
maintainable? Can it handle our projected
growth over the next 5 years?"
CEO (Chief Executive Officer)
"How does this impact profitability and decision-making speed? What's
the ROI and strategic advantage?"
Head of Marketing
"Can I see consumer insights faster than before? Will this help us
respond to market trends in real-time?"
Case Study : A Telecom Company in Crisis
A major telecommunications operator struggled with fragmented customer data scattered across legacy systems, countless spreadsheets maintained
manually by different departments, and zero visibility into customer behavior patterns. Decision-making was reactive, slow, and often based on outdated
information.
Before: The Problems
Customer data fragmented across 12+ systems
Manual spreadsheet processes prone to errors
No predictive insights into customer behavior
Reporting took 10+ days to compile
High customer churn with no early warning
Compliance risks from inconsistent data handling
After: The Solution
Unified cloud-based data hub integrating all sources
AI-powered customer churn prediction models
Automated reporting workflows with real-time updates
Secure governance framework with role-based access
Self-service analytics for business users
Compliance monitoring and audit trails
12%
Churn Reduction
Customer retention improved through predictive
interventions
80%
Faster Reporting
From 10 days to 2 days, enabling agile decision-
making
95%
Real Time Dashboard Reports
Near real-time insights supporting strategic
decisions
A major telecommunications operator struggled with fragmented customer data scattered across legacy systems, countless spreadsheets maintained
manually by different departments, and zero visibility into customer behavior patterns. Decision-making was reactive, slow, and often based on outdated
information.
Before: The Problems
Customer data fragmented across 12+ systems
Manual spreadsheet processes prone to errors
No predictive insights into customer behavior
Reporting took 10+ days to compile
High customer churn with no early warning
Compliance risks from inconsistent data handling
After: The Solution
Unified cloud-based data hub integrating all sources
AI-powered customer churn prediction models
Automated reporting workflows with real-time updates
Secure governance framework with role-based access
Self-service analytics for business users
Compliance monitoring and audit trails
12%
Churn Reduction
Customer retention improved through predictive
interventions
80%
Faster Reporting
From 10 days to 2 days, enabling agile decision-
making
95%
Real Time Dashboard Reports
Near real-time insights supporting strategic
decisions
Practical Tips for Developers
Building business-focused solutions requires a shift in approach , from technology-first to outcome-first thinking. These practical guidelines help
developers create solutions that deliver lasting business value.
1
Start with the Business Pain Point
Always begin by understanding the business problem before writing a single line of code. Interview stakeholders, observe workflows, and
identify the root cause, not just symptoms. The best technical solution to the wrong problem is still a failure.
2
Design for Scalability
Build cloud-native, modular architectures that grow with the business. Use microservices, containerization, and serverless technologies where
appropriate. Today's solution should handle tomorrow's volume without complete rewrites.
3
Prioritize Security from Day One
Implement role-based access control, data encryption at rest and in transit, and comprehensive audit logging. Security isn't a feature to add
later , it's a foundational requirement that must be baked into every layer.
4
Make Insights Understandable
Create clear dashboards with intuitive visualizations. Avoid black-box AI, provide explanations for predictions and recommendations. Business
users should trust and understand the insights they're acting upon.
5
Collaborate with both IT and Business ventures
Build solutions with input from technical teams and business stakeholders. Regular feedback loops ensure alignment with both technical
standards and business requirements throughout development.
"Simplicity scales better than over-engineering." The most elegant solution is often the simplest one that fully addresses the business need.
Building business-focused solutions requires a shift in approach , from technology-first to outcome-first thinking. These practical guidelines help
developers create solutions that deliver lasting business value.
1
Start with the Business Pain Point
Always begin by understanding the business problem before writing a single line of code. Interview stakeholders, observe workflows, and
identify the root cause, not just symptoms. The best technical solution to the wrong problem is still a failure.
2
Design for Scalability
Build cloud-native, modular architectures that grow with the business. Use microservices, containerization, and serverless technologies where
appropriate. Today's solution should handle tomorrow's volume without complete rewrites.
3
Prioritize Security from Day One
Implement role-based access control, data encryption at rest and in transit, and comprehensive audit logging. Security isn't a feature to add
later , it's a foundational requirement that must be baked into every layer.
4
Make Insights Understandable
Create clear dashboards with intuitive visualizations. Avoid black-box AI, provide explanations for predictions and recommendations. Business
users should trust and understand the insights they're acting upon.
5
Collaborate with both IT and Business ventures
Build solutions with input from technical teams and business stakeholders. Regular feedback loops ensure alignment with both technical
standards and business requirements throughout development.
"Simplicity scales better than over-engineering." The most elegant solution is often the simplest one that fully addresses the business need.
We are Business Enablers
As developers we are not just coders , we are business enablers who transform organizational capabilities through technology. AI and cloud platforms
make us faster and more powerful, but we must always anchor our work in business outcomes that matter to stakeholders.
A well-implemented data-driven framework ensures that every solution delivers on three critical dimensions:
Safe, Secure, Scalable
Built on robust foundations that protect data,
ensure compliance, and grow with business
needs
Valuable to Business
Directly contributes to cost reduction,
revenue growth, or competitive advantage
Built with Purpose
Every feature and function serves a clear
business objective
Your Call to Action
Next time you design a solution, challenge yourself with these three essential questions:
01
What business problem am I solving?
Be specific about the pain point and the
stakeholders affected
02
How do I make it scalable and
secure?
Design for growth and protection from the
foundation
03
How do I ensure it drives measurable
business performance?
Define success metrics and track impact over
time
That's how as developers we can truly optimize business performance with data-driven frameworks.
As developers we are not just coders , we are business enablers who transform organizational capabilities through technology. AI and cloud platforms
make us faster and more powerful, but we must always anchor our work in business outcomes that matter to stakeholders.
A well-implemented data-driven framework ensures that every solution delivers on three critical dimensions:
Safe, Secure, Scalable
Built on robust foundations that protect data,
ensure compliance, and grow with business
needs
Valuable to Business
Directly contributes to cost reduction,
revenue growth, or competitive advantage
Built with Purpose
Every feature and function serves a clear
business objective
Your Call to Action
Next time you design a solution, challenge yourself with these three essential questions:
01
What business problem am I solving?
Be specific about the pain point and the
stakeholders affected
02
How do I make it scalable and
secure?
Design for growth and protection from the
foundation
03
How do I ensure it drives measurable
business performance?
Define success metrics and track impact over
time
That's how as developers we can truly optimize business performance with data-driven frameworks.
Build with AI , #BuildwithAI , Build WITH AI
Just Google it
25 /10/ 2025
Brandon T. DevFest 2025 Harare
Just Google it
25 /10/ 2025
Brandon T. DevFest 2025 Harare
25 /10/ 2025
Brandon T. DevFest 2025 Harare
Who is that guy?
Brandon T. DevFest 2025 Harare
Who is that guy?
Some traditions get passed ……………………
JUST GOOGLE IT
JUST GOOGLE IT
JUST GOOGLE IT
JUST GOOGLE IT
When you can’t just build ……………………
JUST GOOGLE IT
JUST GOOGLE IT
Good devs get paid something before writing code ……………………
JUST GOOGLE IT
JUST GOOGLE IT
For your solution to solve actual problems ……………………
JUST GOOGLE IT
JUST GOOGLE IT
To create clean UI/UX using AI ……………………
JUST GOOGLE IT
JUST GOOGLE IT
To integrate AI in your solutions ……………………
JUST GOOGLE IT
JUST GOOGLE IT
For an AI assistant in code ……………………
JUST GOOGLE IT
JUST GOOGLE IT
Prototype to production using AI ……………………
JUST GOOGLE IT
JUST GOOGLE IT
RODNEY MUNODAWAFA | VP SALES
Scaling Customer
Engagement in the Digital
Age
Scaling Customer
Engagement in the Digital
Age
THE CONTEXT -THE DIGITAL SHIFT
0.1s
FASTER
8%
CONVERSIONS
“We live in the age of instant everything.
Customers expect responses in seconds, not
hours.”
0.1s
FASTER
8%
CONVERSIONS
“We live in the age of instant everything.
Customers expect responses in seconds, not
hours.”
THE CHALLENGE -SCALING WITH EMPATHY
REAL WORLD ISSUES
How do we automate
without dehumanizing?
How do we scale without
losing personalization?
“Automation without empathy is like sending a
birthday card from
[email protected]”
REAL WORLD ISSUES
How do we automate
without dehumanizing?
How do we scale without
losing personalization?
“Automation without empathy is like sending a
birthday card from
[email protected]”
THE EVOLUTION -FROM BROADCAST TO
CONVERSATION
2020s2000s
2010s
CONVERSATION
2020s2000s
2010s
THE TOOLS -TECH BEHIND SCALABLE
ENGAGEMENT
AI personalizes at
Scale
Analytics close the
Loop
APIs power the
Connection
ENGAGEMENT
AI personalizes at
Scale
Analytics close the
Loop
APIs power the
Connection
AFRICA’S OPPORTUNITY -LOCAL CONTEXT
Mobile-first
Users:600M
Growing Fintech
Adoption
WhatsApp Penetration
Mobile-first
Users:600M
Growing Fintech
Adoption
WhatsApp Penetration
FUTURE OUTLOOK -AGE OF INTELLIGENT
ENGAGEMENT
Your favorite airline texts
you before you even realize
your flight is delayed.
Your bank reminds you
that your electricity
tokens are running low.
Your insurer reaches out
when it rains in your area
to offer safety cover.
ENGAGEMENT
Your favorite airline texts
you before you even realize
your flight is delayed.
Your bank reminds you
that your electricity
tokens are running low.
Your insurer reaches out
when it rains in your area
to offer safety cover.
TAKEAWAY -THE HUMAN CONNECTION
Smarter Connections More Human, Not Less
Not a Tech Problem, a
Human One
Smarter Connections More Human, Not Less
Not a Tech Problem, a
Human One
CUSTOMERS VALUE
EFFECTIVE MESSAGING
AND SUPPORT
EFFECTIVE MESSAGING
AND SUPPORT
CONFIDANT AI: YOUR INTELLIGENT
PRESENTATION COACH
FLUTTER & ON-DEVICE AI
TATENDA-ALICIA
PRESENTATION COACH
FLUTTER & ON-DEVICE AI
TATENDA-ALICIA
WHAT IT IS
•Real-time speech recognition & analysis
•AI-powered semantic understanding
•Discreet feedback via wearables
•100% offline - Your secrets stay with youYOUR DIGITAL WINGMAN
•Real-time speech recognition & analysis
•AI-powered semantic understanding
•Discreet feedback via wearables
•100% offline - Your secrets stay with youYOUR DIGITAL WINGMAN
TECHNICALS
ARCHITECTURE
STATE
MANAGEMENT -
BLOC PATTERN
SpeechEvent
SpeechBloc
SpeecState
UI Upddates
flutter_bloc: ^9.1.1
MANAGEMENT -
BLOC PATTERN
SpeechEvent
SpeechBloc
SpeecState
UI Upddates
flutter_bloc: ^9.1.1
SPEECH RECOGNITION -REAL
TIME VOICE CAPTURE
speech_to_text: ^7.3.0
TIME VOICE CAPTURE
speech_to_text: ^7.3.0
THEEE AI-BERT POWERED UNDERSTANDING
sentence_bert.tflite
Your words
Tokenisation
BERT
Embeddings
Cosine
Similarity
Condidence
score
sentence_bert.tflite
Your words
Tokenisation
BERT
Embeddings
Cosine
Similarity
Condidence
score
AI SERVICE CODE
ai_service.dart
ai_service.dart
WORDPIECE TOKENIZATION
SMART
TOKENIZATION
Why it matters: Understands meaning, not just
keywords
SMART
TOKENIZATION
Why it matters: Understands meaning, not just
keywords
MODEL LOADING AND CACHING
PERFORMANCE
OPTIMIZATION
Results: 75% size reduction, instant subsequent loads
PERFORMANCE
OPTIMIZATION
Results: 75% size reduction, instant subsequent loads
FROM SPEECH TO FEEDBACK IN 150ms
REAL-TIME
PIPELINE
Thresholds:
•0.8: You're crushing it!
•< 0.5: Need help!
REAL-TIME
PIPELINE
Thresholds:
•0.8: You're crushing it!
•< 0.5: Need help!
When AI takes a break
FALLBACK
SYSTEM
Redundancy matters: App never crashes, always
helps
FALLBACK
SYSTEM
Redundancy matters: App never crashes, always
helps
WEARABLE INTEGRATION
SECRET MESSAGES TO YOUR WATCH
Scan Devices
Connect
Haptic
Patterns
flutter_blue_plus: ^1.32.7
Scan Devices
Connect
Haptic
Patterns
flutter_blue_plus: ^1.32.7
AUDIO COACHING
flutter_tts: ^4.0.2
Private voice guidance
flutter_tts: ^4.0.2
Private voice guidance
ANALYTICS AND INSIGHTS
https: //g ithub. com/ta tenda -murwira
https://github.com/tatenda-murwira
https://github.com/tatenda-murwira
Building Scalable
AI Agents with
Next.js & Google
Cloud
Production-Ready AI Solutions
Learn how to build, deploy, and scale AI-powered applications using modern
web technologies and cloud infrastructure. From architecture design to security
best practices.
Presented by Nyasha Ushewokunze
Google Developer Groups Harare DevFest 2025
Saturday, 25 October 2025
AI Agents with
Next.js & Google
Cloud
Production-Ready AI Solutions
Learn how to build, deploy, and scale AI-powered applications using modern
web technologies and cloud infrastructure. From architecture design to security
best practices.
Presented by Nyasha Ushewokunze
Google Developer Groups Harare DevFest 2025
Saturday, 25 October 2025
The AI Scalability Challenge
AI agents are transforming industries like fintech and agriculture, but many
startups and enterprises struggle with critical challenges when building
production-ready AI solutions.
The gap between proof-of-concept and production deployment is significant.
Organizations need robust infrastructure, security measures, and scalable
architecture to succeed.
Without proper planning and the right technology stack, AI projects fail to
scale, incur excessive costs, and expose sensitive data to security risks.
Scalability Issues
Handling increased user demand and API calls without degrading performance
Security Concerns
Protecting API keys, user data, and ensuring compliance with regulations
Infrastructure Complexity
Managing databases, authentication, and cloud deployment efficiently
Cost Management
Optimizing cloud spending while maintaining performance and reliability
Model Integration
Seamlessly integrating advanced AI models into existing applications
AI agents are transforming industries like fintech and agriculture, but many
startups and enterprises struggle with critical challenges when building
production-ready AI solutions.
The gap between proof-of-concept and production deployment is significant.
Organizations need robust infrastructure, security measures, and scalable
architecture to succeed.
Without proper planning and the right technology stack, AI projects fail to
scale, incur excessive costs, and expose sensitive data to security risks.
Scalability Issues
Handling increased user demand and API calls without degrading performance
Security Concerns
Protecting API keys, user data, and ensuring compliance with regulations
Infrastructure Complexity
Managing databases, authentication, and cloud deployment efficiently
Cost Management
Optimizing cloud spending while maintaining performance and reliability
Model Integration
Seamlessly integrating advanced AI models into existing applications
Our Technology Stack
A powerful combination of modern technologies designed to build, secure, and scale AI agents efficiently
Next.js
Full-stack JavaScript framework for building fast,
production-ready web applications
API routes for backend logic
Server-side rendering
Rapid development
Built-in optimization
Supabase
Open-source Firebase alternative with PostgreSQL
database and built-in authentication
Secure database
Row-level security
Authentication
Real-time capabilities
Google Cloud
Enterprise-grade cloud platform with advanced AI models
and infrastructure services
Gemini AI models
Cloud Run deployment
Scalable infrastructure
Advanced analytics
A powerful combination of modern technologies designed to build, secure, and scale AI agents efficiently
Next.js
Full-stack JavaScript framework for building fast,
production-ready web applications
API routes for backend logic
Server-side rendering
Rapid development
Built-in optimization
Supabase
Open-source Firebase alternative with PostgreSQL
database and built-in authentication
Secure database
Row-level security
Authentication
Real-time capabilities
Google Cloud
Enterprise-grade cloud platform with advanced AI models
and infrastructure services
Gemini AI models
Cloud Run deployment
Scalable infrastructure
Advanced analytics
Next.js: Building Full-Stack AI Applications
Next.js is a powerful React framework that enables developers to build full-
stack applications with minimal configuration. It's ideal for AI-powered
applications because it bridges the gap between frontend and backend
seamlessly.
With Next.js, you can create API endpoints alongside your frontend code,
making it easy to integrate AI models, handle authentication, and manage
data flows in a single codebase.
The framework handles optimization automatically, including code splitting,
image optimization, and performance monitoring—critical for production AI
applications.
API Routes
Create backend endpoints directly in your Next.js project to handle AI model calls,
database queries, and authentication
Server-Side Rendering
Render pages on the server for better SEO, security, and performance when
displaying AI-generated content
Middleware & Authentication
Implement request validation, API key management, and user authentication at the
edge
Built-in Optimization
Automatic image optimization, code splitting, and caching strategies reduce load
times and costs
Rapid development with hot reloading
Unified codebase reduces complexity
Easy deployment to cloud platforms
Next.js is a powerful React framework that enables developers to build full-
stack applications with minimal configuration. It's ideal for AI-powered
applications because it bridges the gap between frontend and backend
seamlessly.
With Next.js, you can create API endpoints alongside your frontend code,
making it easy to integrate AI models, handle authentication, and manage
data flows in a single codebase.
The framework handles optimization automatically, including code splitting,
image optimization, and performance monitoring—critical for production AI
applications.
API Routes
Create backend endpoints directly in your Next.js project to handle AI model calls,
database queries, and authentication
Server-Side Rendering
Render pages on the server for better SEO, security, and performance when
displaying AI-generated content
Middleware & Authentication
Implement request validation, API key management, and user authentication at the
edge
Built-in Optimization
Automatic image optimization, code splitting, and caching strategies reduce load
times and costs
Rapid development with hot reloading
Unified codebase reduces complexity
Easy deployment to cloud platforms
Supabase: Database Security & Scalability
Supabase provides a secure, scalable PostgreSQL database with built-in
authentication and real-time capabilities. It's the perfect foundation for AI
applications that require robust data management and user protection.
With row-level security (RLS), you can ensure that users only access data
they're authorized to view. This is critical for multi-tenant AI applications
and compliance with data protection regulations.
Supabase handles the infrastructure complexity, allowing developers to focus
on building features. Automatic backups, monitoring, and scaling ensure your
application remains reliable as user demand grows.
Authentication
JWT-based authentication
OAuth integration (Google, GitHub)
Email/password management
Multi-factor authentication support
Row-Level Security
Fine-grained access control
Policy-based permissions
User isolation in multi-tenant apps
Automatic enforcement at database level
Scalability
Auto-scaling infrastructure
Real-time subscriptions
Automatic backups & recovery
Connection pooling for efficiency
Supabase provides a secure, scalable PostgreSQL database with built-in
authentication and real-time capabilities. It's the perfect foundation for AI
applications that require robust data management and user protection.
With row-level security (RLS), you can ensure that users only access data
they're authorized to view. This is critical for multi-tenant AI applications
and compliance with data protection regulations.
Supabase handles the infrastructure complexity, allowing developers to focus
on building features. Automatic backups, monitoring, and scaling ensure your
application remains reliable as user demand grows.
Authentication
JWT-based authentication
OAuth integration (Google, GitHub)
Email/password management
Multi-factor authentication support
Row-Level Security
Fine-grained access control
Policy-based permissions
User isolation in multi-tenant apps
Automatic enforcement at database level
Scalability
Auto-scaling infrastructure
Real-time subscriptions
Automatic backups & recovery
Connection pooling for efficiency
Google Cloud AI Tools: Gemini & Beyond
Introducing Gemini
Google's most advanced AI model family, Gemini represents the cutting edge
of natural language understanding and generation. Available in multiple sizes
(Nano, Pro, Ultra) to suit different performance and cost requirements.
Gemini excels at understanding context, generating code, analyzing
documents, and processing multimodal inputs including text, images, and
video. It's designed for production use with enterprise-grade reliability.
Integrated seamlessly into Google Cloud's ecosystem, Gemini can be
accessed via APIs, making it perfect for building AI agents that scale with your
application.
Key Advantage:Gemini's multimodal capabilities enable building richer, more
intelligent AI agents that can understand and respond to diverse input types.
Core Capabilities
Natural language understanding and generation
Code generation and analysis
Image and document understanding
Multimodal processing
Reasoning and problem-solving
Production Features
Scalable API infrastructure
Content filtering and safety
Caching for cost optimization
Batch processing support
Monitoring and logging
Use Cases
Chatbots and customer support
Content creation and summarization
Data analysis and insights
Code assistance tools
Introducing Gemini
Google's most advanced AI model family, Gemini represents the cutting edge
of natural language understanding and generation. Available in multiple sizes
(Nano, Pro, Ultra) to suit different performance and cost requirements.
Gemini excels at understanding context, generating code, analyzing
documents, and processing multimodal inputs including text, images, and
video. It's designed for production use with enterprise-grade reliability.
Integrated seamlessly into Google Cloud's ecosystem, Gemini can be
accessed via APIs, making it perfect for building AI agents that scale with your
application.
Key Advantage:Gemini's multimodal capabilities enable building richer, more
intelligent AI agents that can understand and respond to diverse input types.
Core Capabilities
Natural language understanding and generation
Code generation and analysis
Image and document understanding
Multimodal processing
Reasoning and problem-solving
Production Features
Scalable API infrastructure
Content filtering and safety
Caching for cost optimization
Batch processing support
Monitoring and logging
Use Cases
Chatbots and customer support
Content creation and summarization
Data analysis and insights
Code assistance tools
Scalable Architecture Patterns
Design your AI agents for growth with proven architectural patterns that ensure reliability, performance, and cost efficiencyat scale
Microservices Architecture
Break AI agents into independent, deployable services that can scale independently
Independent scaling
Fault isolation
Technology flexibility
Async Processing
Use message queues and background jobs for long-running AI operations
Improved responsiveness
Better resource utilization
Reliable processing
Intelligent Caching
Cache AI model responses and frequently accessed data to reduce latency
Reduced API calls
Lower costs
Faster responses
Load Balancing
Distribute traffic across multiple instances for optimal performance
High availability
Better performance
Automatic failover
Cloud-Native Deployment
Leverage containerization and serverless for automatic scaling
Auto-scaling
Cost optimization
Easy management
Monitoring & Observability
Track performance metrics and logs to identify bottlenecks
Proactive debugging
Performance insights
User experience tracking
Design your AI agents for growth with proven architectural patterns that ensure reliability, performance, and cost efficiencyat scale
Microservices Architecture
Break AI agents into independent, deployable services that can scale independently
Independent scaling
Fault isolation
Technology flexibility
Async Processing
Use message queues and background jobs for long-running AI operations
Improved responsiveness
Better resource utilization
Reliable processing
Intelligent Caching
Cache AI model responses and frequently accessed data to reduce latency
Reduced API calls
Lower costs
Faster responses
Load Balancing
Distribute traffic across multiple instances for optimal performance
High availability
Better performance
Automatic failover
Cloud-Native Deployment
Leverage containerization and serverless for automatic scaling
Auto-scaling
Cost optimization
Easy management
Monitoring & Observability
Track performance metrics and logs to identify bottlenecks
Proactive debugging
Performance insights
User experience tracking
Practical Implementation Insights
API Integration
const response = await fetch('/api/ai-agent', { method: 'POST', headers: { 'Content-Type':
'application/json' }, body: JSON.stringify({ prompt: userInput }) }); const result = await
response.json();
Environment Config
// .env.local NEXT_PUBLIC_SUPABASE_URL=... NEXT_PUBLIC_SUPABASE_KEY=...
GOOGLE_CLOUD_API_KEY=... GEMINI_MODEL_ID=gemini -pro
Database Query
const { data } = await supabase .from('conversations') .select('*') .eq('user_id', userId)
.order('created_at', { ascending: false });
Error Handling
Implement comprehensive try-catch blocks and graceful fallbacks. Log errors to monitoring
services for production debugging.
Rate Limiting
Implement rate limiting on API endpoints to prevent abuse. Use Redis for distributed rate limiting
across multiple instances.
Caching Strategy
Cache frequently used AI model responses and database queries. Use appropriate TTLs based on
data freshness requirements.
API Key Management
Never expose API keys in client-side code. Use environment variables and rotate keys regularly
for security.
Performance Optimization
Use streaming responses for long-running AI operations. Implement pagination for large
datasets and optimize database indexes.
Testing & Monitoring
Write unit tests for API routes. Monitor response times, error rates, and resource usage in
production.
API Integration
const response = await fetch('/api/ai-agent', { method: 'POST', headers: { 'Content-Type':
'application/json' }, body: JSON.stringify({ prompt: userInput }) }); const result = await
response.json();
Environment Config
// .env.local NEXT_PUBLIC_SUPABASE_URL=... NEXT_PUBLIC_SUPABASE_KEY=...
GOOGLE_CLOUD_API_KEY=... GEMINI_MODEL_ID=gemini -pro
Database Query
const { data } = await supabase .from('conversations') .select('*') .eq('user_id', userId)
.order('created_at', { ascending: false });
Error Handling
Implement comprehensive try-catch blocks and graceful fallbacks. Log errors to monitoring
services for production debugging.
Rate Limiting
Implement rate limiting on API endpoints to prevent abuse. Use Redis for distributed rate limiting
across multiple instances.
Caching Strategy
Cache frequently used AI model responses and database queries. Use appropriate TTLs based on
data freshness requirements.
API Key Management
Never expose API keys in client-side code. Use environment variables and rotate keys regularly
for security.
Performance Optimization
Use streaming responses for long-running AI operations. Implement pagination for large
datasets and optimize database indexes.
Testing & Monitoring
Write unit tests for API routes. Monitor response times, error rates, and resource usage in
production.
Security & Compliance Considerations
Building secure AI applications requires a multi-layered approach. Protect your infrastructure, user data, and comply with regulations to build trust.
API Key Management
Environment Variables
Store all API keys and secrets in environment variables,
never in code or version control
Key Rotation
Implement regular key rotation schedules and retire
compromised keys immediately
Least Privilege
Use API keys with minimal required permissions for specific
operations
Monitoring
Track API key usage and set up alerts for unusual activity
patterns
Server-Side Calls
Always call external APIs from your backend, never directly
from client code
Critical:Never commit .env files to version control. Use
.env.local for local development.
Data Protection
Encryption in Transit
Use HTTPS/TLS for all communications between client,
server, and external services
Encryption at Rest
Encrypt sensitive data stored in databases using strong
encryption algorithms
Row-Level Security
Implement RLS policies in Supabase to ensure users only
access their own data
Data Minimization
Collect and store only the minimum data necessary for
your application
Secure Deletion
Implement proper data deletion procedures and securely
wipe sensitive information
Best Practice:Use database encryption and separate
encryption keys from application code.
Compliance & Governance
GDPR Compliance
Implement user consent, data access, and deletion rights
for EU users
Data Residency
Store data in appropriate regions to comply with local data
protection laws
Audit Logging
Maintain comprehensive logs of data access and
modifications for compliance audits
Security Testing
Conduct regular penetration testing and security audits of
your application
Incident Response
Develop and test incident response plans for security
breaches
Important:Consult legal experts for compliance
requirements in your target markets.
Building secure AI applications requires a multi-layered approach. Protect your infrastructure, user data, and comply with regulations to build trust.
API Key Management
Environment Variables
Store all API keys and secrets in environment variables,
never in code or version control
Key Rotation
Implement regular key rotation schedules and retire
compromised keys immediately
Least Privilege
Use API keys with minimal required permissions for specific
operations
Monitoring
Track API key usage and set up alerts for unusual activity
patterns
Server-Side Calls
Always call external APIs from your backend, never directly
from client code
Critical:Never commit .env files to version control. Use
.env.local for local development.
Data Protection
Encryption in Transit
Use HTTPS/TLS for all communications between client,
server, and external services
Encryption at Rest
Encrypt sensitive data stored in databases using strong
encryption algorithms
Row-Level Security
Implement RLS policies in Supabase to ensure users only
access their own data
Data Minimization
Collect and store only the minimum data necessary for
your application
Secure Deletion
Implement proper data deletion procedures and securely
wipe sensitive information
Best Practice:Use database encryption and separate
encryption keys from application code.
Compliance & Governance
GDPR Compliance
Implement user consent, data access, and deletion rights
for EU users
Data Residency
Store data in appropriate regions to comply with local data
protection laws
Audit Logging
Maintain comprehensive logs of data access and
modifications for compliance audits
Security Testing
Conduct regular penetration testing and security audits of
your application
Incident Response
Develop and test incident response plans for security
breaches
Important:Consult legal experts for compliance
requirements in your target markets.
Deployment & Monitoring
Development
Local testing with Next.js dev server
Test with mock AI models
Validate database connections
Run automated tests
Staging
Deploy to staging environment
Test with real API keys
Performance testing
Security scanning
Production
Deploy to Cloud Run
Enable auto-scaling
Set up monitoring alerts
Enable backup & recovery
Performance Monitoring
Response time metrics for API endpoints
AI model inference latency
Database query performance
Error rates and exceptions
Resource utilization (CPU, memory)
API rate limit tracking
Scaling & Reliability
Auto-scaling based on CPU/memory
Load balancing across instances
Circuit breaker for API failures
Automatic retry with exponential backoff
Health checks and readiness probes
Graceful degradation strategies
Development
Local testing with Next.js dev server
Test with mock AI models
Validate database connections
Run automated tests
Staging
Deploy to staging environment
Test with real API keys
Performance testing
Security scanning
Production
Deploy to Cloud Run
Enable auto-scaling
Set up monitoring alerts
Enable backup & recovery
Performance Monitoring
Response time metrics for API endpoints
AI model inference latency
Database query performance
Error rates and exceptions
Resource utilization (CPU, memory)
API rate limit tracking
Scaling & Reliability
Auto-scaling based on CPU/memory
Load balancing across instances
Circuit breaker for API failures
Automatic retry with exponential backoff
Health checks and readiness probes
Graceful degradation strategies
Real-World Applications
Fintech Solutions
AI-powered financial services leveraging mobile
money and digital banking infrastructure across Africa
Key Applications
Fraud Detection
Real-time transaction monitoring using Gemini to
identify suspicious patterns
Credit Scoring
Alternative credit assessment for unbanked
populations using transaction history
Customer Support
AI chatbots handling inquiries in local languages with
Next.js frontend
Loan Processing
Automated application review and approval with
Supabase data management
AgriTech
Intelligent farming solutions helping smallholder
farmers optimize yields and reduce waste
Key Applications
Crop Monitoring
Image analysis of crops to detect diseases and pest
infestations early
Weather Insights
AI analysis of weather patterns for planting and
harvesting recommendations
Market Intelligence
Price predictions and market trend analysis for better
selling decisions
Resource Optimization
AI-driven irrigation and fertilizer recommendations to
reduce costs
Healthcare
AI-enabled health services improving access and
quality of care in underserved regions
Key Applications
Diagnostic Support
AI analysis of medical images and symptoms to assist
healthcare workers
Telemedicine
Scalable virtual consultations with AI-powered triage
and documentation
Drug Information
Chatbots providing medication guidance and health
education in local languages
Patient Records
Secure, scalable patient data management with
Supabase and row-level security
Fintech Solutions
AI-powered financial services leveraging mobile
money and digital banking infrastructure across Africa
Key Applications
Fraud Detection
Real-time transaction monitoring using Gemini to
identify suspicious patterns
Credit Scoring
Alternative credit assessment for unbanked
populations using transaction history
Customer Support
AI chatbots handling inquiries in local languages with
Next.js frontend
Loan Processing
Automated application review and approval with
Supabase data management
AgriTech
Intelligent farming solutions helping smallholder
farmers optimize yields and reduce waste
Key Applications
Crop Monitoring
Image analysis of crops to detect diseases and pest
infestations early
Weather Insights
AI analysis of weather patterns for planting and
harvesting recommendations
Market Intelligence
Price predictions and market trend analysis for better
selling decisions
Resource Optimization
AI-driven irrigation and fertilizer recommendations to
reduce costs
Healthcare
AI-enabled health services improving access and
quality of care in underserved regions
Key Applications
Diagnostic Support
AI analysis of medical images and symptoms to assist
healthcare workers
Telemedicine
Scalable virtual consultations with AI-powered triage
and documentation
Drug Information
Chatbots providing medication guidance and health
education in local languages
Patient Records
Secure, scalable patient data management with
Supabase and row-level security
Building the Future of AI in Africa
Key Takeaways
Next.js, Supabase, and Google Cloud form a powerful, production-ready
technology stack for AI agents
Security and scalability must be built in from the start, not added later as an
afterthought
Proper architecture patterns enable applications to grow from prototype to
production seamlessly
Leverage managed services to focus on innovation rather than infrastructure
management
The African tech ecosystem has immense potential for AI-driven solutions in
fintech, agriculture, and healthcare
Resources & Next Steps
Documentation
Next.js, Supabase, and Google Cloud official docs with examples and guides
Sample Projects
GitHub repositories with starter templates and reference implementations
Community
Join developer communities, forums, and local meetups for support and collaboration
Training & Courses
Online courses and workshops covering full-stack AI development and cloud deployment
Google Cloud Credits
Startup programs and educational credits to reduce development and deployment costs
Mentorship
Connect with experienced developers and architects for guidance on your projects
The Path Forward
You now have the knowledge and tools to build scalable, secure AI agents that can transform industries across Africa. Start small with a proof-of-concept, apply the
architectural patterns you've learned, and scale confidently. The future of AI innovation in Africa is in your hands. Build something remarkable.
Key Takeaways
Next.js, Supabase, and Google Cloud form a powerful, production-ready
technology stack for AI agents
Security and scalability must be built in from the start, not added later as an
afterthought
Proper architecture patterns enable applications to grow from prototype to
production seamlessly
Leverage managed services to focus on innovation rather than infrastructure
management
The African tech ecosystem has immense potential for AI-driven solutions in
fintech, agriculture, and healthcare
Resources & Next Steps
Documentation
Next.js, Supabase, and Google Cloud official docs with examples and guides
Sample Projects
GitHub repositories with starter templates and reference implementations
Community
Join developer communities, forums, and local meetups for support and collaboration
Training & Courses
Online courses and workshops covering full-stack AI development and cloud deployment
Google Cloud Credits
Startup programs and educational credits to reduce development and deployment costs
Mentorship
Connect with experienced developers and architects for guidance on your projects
The Path Forward
You now have the knowledge and tools to build scalable, secure AI agents that can transform industries across Africa. Start small with a proof-of-concept, apply the
architectural patterns you've learned, and scale confidently. The future of AI innovation in Africa is in your hands. Build something remarkable.
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