Proof of Concept (POC) Template
Purpose
A Proof of Concept validates a technology, approach, or solution through a limited-scope, time-boxed experiment before committing to full implementation.
When to Use
- Evaluating new technologies before major adoption
- Validating architectural approaches in practice
- Testing solutions to address specific problems
- De-risking assumptions before full investment
- Evaluating vendor products or SaaS solutions
- Exploring feasibility of novel approaches
POC Charter
Project Title: [Name of POC]
Sponsor: [Executive sponsor]
Owner: [POC lead/architect]
Duration: [Start date - End date] (typically 2-8 weeks)
Status: [Planning/Active/Complete]
Section 1: Problem Statement
Articulate the problem the POC addresses:
Business Context:
- What problem are we trying to solve?
- Why is it important?
- What would happen if we don't solve it?
Current Approach:
- How are we solving this today?
- What are the limitations?
- Why do we need something different?
Technical Challenges:
- What are we uncertain about?
- What assumptions need validation?
- What could prevent success?
Example:
Business Context:
- Customers complain about 30-second order checkout times
- Competitor processes orders in 3 seconds
- We're losing 15% of carts to timeout abandonment
- Estimated revenue impact: $500k/year
Current Approach:
- Synchronous payment processing: Order → Payment gateway → Response
- Database query for inventory check on every request
- No caching for product information
Current Limitations:
- Network latency: 200ms to payment gateway
- Database latency: 100ms per query
- No parallelization of independent operations
Technical Challenges:
- Can we process in < 5 seconds reliably?
- Will asynchronous payment processing cause issues?
- Can caching reduce database load?
- Will service mesh help with latency?
Section 2: POC Objectives
Define what you want to learn:
Primary Objective:
[One specific question/validation needed]
Example: "Determine if asynchronous payment processing
can reduce order processing time from 30s to <5s"
Secondary Objectives:
- Objective 1: [Learning goal]
- Objective 2: [Learning goal]
- Objective 3: [Learning goal]
Example:
1. Determine API latency improvements with service mesh
2. Validate saga pattern for eventual consistency
3. Evaluate operational complexity of new architecture
Outcome Type:
☐ Go/No-Go: "Should we use this technology?"
☐ Comparison: "Which technology is better?"
☐ Feasibility: "Is this technically possible?"
☐ Qualification: "Does this meet our requirements?"
Section 3: Scope & Success Criteria
Define POC boundaries and success:
In Scope:
- Specific technologies/approaches to test
- Specific use cases to validate
- Specific metrics to measure
Out of Scope:
- What won't be tested
- What's explicitly excluded
- What can wait for production implementation
Example In Scope:
- Async payment processing via Kafka
- Caching layer (Redis) for product data
- Service mesh (Istio) for latency reduction
- Response time < 5 seconds
- No data loss during processing
Example Out of Scope:
- Full customer API implementation
- Production-grade monitoring/alerting
- Payment compliance (PCI-DSS) - assume handled
- Mobile app integration
- Multi-region deployment
Success Criteria:
1. Technical: [Measurable requirement]
Threshold: [Acceptable performance/result]
2. Business: [Business impact]
Threshold: [Acceptable improvement]
Example:
1. Technical: API response time P95
Threshold: < 5 seconds (current: 30s)
Measurement: APM tool (DataDog)
2. Technical: Order processing reliability
Threshold: 99.9% success (no failed orders)
Measurement: Application logs
3. Business: Reduction in cart abandonment
Threshold: At least 5% improvement
Measurement: Analytics platform
Section 4: Approach & Methodology
Describe how you’ll conduct the POC:
Architecture:
Current State Diagram:
[ASCII diagram of current system]
POC Architecture Diagram:
[ASCII diagram of what you're testing]
Example:
CURRENT:
Customer → API (sync) → Payment Gateway (200ms)
→ DB Query (100ms)
→ Inventory Check (150ms)
Total: ~450ms, but sequential = 30s timeout
POC:
Customer → API → Async Queue (Kafka)
├→ Payment Service (async)
├→ Inventory Service (cached)
├→ Order Service (async)
↓
Cache Layer (Redis) - Product data
Service Mesh (Istio) - Latency optimization
Approach Steps:
1. [Step 1]: [What we'll do]
Expected learning: [What we'll learn]
Risk: [What could go wrong]
2. [Step 2]: [What we'll do]
Expected learning: [What we'll learn]
Risk: [What could go wrong]
Example:
1. Implement Redis caching for product catalog
Learning: How much latency reduction from caching?
Risk: Cache invalidation issues
Mitigation: Monitor hit rate, test invalidation scenarios
2. Implement async payment processing via Kafka
Learning: Can eventual consistency work for payments?
Risk: Race conditions, orphaned orders
Mitigation: Implement saga pattern, thorough testing
3. Deploy service mesh (Istio) to test services
Learning: Does service mesh reduce latency?
Risk: Added complexity, performance overhead
Mitigation: Load test before/after, monitor closely
4. Load testing with realistic scenarios
Learning: Can we handle peak load (<5s)?
Risk: Test environment different from production
Mitigation: Use production-like data, gradual ramp
Testing Plan:
- Unit tests: [Technology-specific testing]
- Integration tests: [Interaction between components]
- Performance tests: [Load, latency, throughput]
- Chaos tests: [Failure scenarios]
Section 5: Resource Requirements
Specify team and infrastructure needs:
Team:
- [Role]: [Effort] - [Responsibility]
- Backend Engineer (100% × 3 weeks)
- Async payment processing implementation
- DevOps Engineer (50% × 3 weeks)
- Kafka/Redis infrastructure setup
- QA Engineer (50% × 2 weeks)
- Testing, load testing, validation
- Architect (25% × 3 weeks)
- Design, review, decision support
Budget:
- Engineering time: 3 engineers × 3 weeks = $15k
- Infrastructure (cloud): ~$5k
- Tools/licenses: $2k
- Total: ~$22k
Infrastructure:
- AWS account for testing
- Kafka cluster (3-node)
- Redis instance
- Load testing tool (k6/Locust)
- Monitoring (DataDog trial)
- Estimated: $5k cloud costs
Section 6: Timeline & Milestones
Phase 1: Setup (Week 1)
- Milestone 1a: Infrastructure provisioned
Deliverable: Kafka cluster running, Redis available
- Milestone 1b: Development environment ready
Deliverable: Code repo, CI/CD pipeline, monitoring
Phase 2: Implementation (Weeks 2-3)
- Milestone 2a: Redis caching layer working
Deliverable: Product data cached, hit rate >80%
Success criteria: Latency reduced by 100ms
- Milestone 2b: Async payment processing implemented
Deliverable: Saga pattern working, no data loss
Success criteria: Zero failed orders in testing
- Milestone 2c: Service mesh integrated
Deliverable: Istio deployed to test environment
Success criteria: Service communication via mTLS
Phase 3: Testing (Week 4)
- Milestone 3a: Load testing executed
Deliverable: Performance results documented
Success criteria: P95 response time < 5 seconds
- Milestone 3b: Chaos testing completed
Deliverable: Failure recovery validated
Success criteria: System recovers from failures
Phase 4: Analysis & Decisions (Week 4-5)
- Milestone 4a: Results analyzed and documented
Deliverable: POC report with findings
- Milestone 4b: Go/no-go decision made
Deliverable: Recommendation to ARB
Section 7: Assumptions & Risks
Key Assumptions:
1. Assumption: [What we're assuming to be true]
Validation: [How we'll validate this assumption]
Impact if wrong: [If assumption is false]
2. Assumption: Payment gateway supports async callbacks
Validation: Confirm with vendor documentation
Impact: If not supported, async pattern won't work
3. Assumption: Team has Kafka expertise
Validation: Review team skills, provide training if needed
Impact: If not, project timeline extends
Risks:
┌────────────────┬────────┬────────┬──────────────┐
│ Risk │Likeli │ Impact │ Mitigation │
│ │hood │ │ │
├────────────────┼────────┼────────┼──────────────┤
│ Cache │ High │ Medium │ Implement │
│ invalidation │ │ │ proper │
│ issues │ │ │ invalidation │
├────────────────┼────────┼────────┼──────────────┤
│ Data loss │ Medium │ High │ Implement │
│ during async │ │ │ saga pattern │
│ processing │ │ │ + audit logs │
├────────────────┼────────┼────────┼──────────────┤
│ Performance │ Medium │ High │ Load test │
│ worse than │ │ │ early, plan │
│ expected │ │ │ alternatives │
└────────────────┴────────┴────────┴──────────────┘
Section 8: POC Execution
Track progress during POC:
Progress Tracking:
Week 1 (Setup):
- [ ] Infrastructure setup complete
- [ ] Team oriented on technologies
- [ ] Code repo and CI/CD ready
- Blockers: [Any issues?]
- On track: Yes/No/At risk
Week 2 (Implementation):
- [ ] Redis caching operational
- [ ] Async payment service working
- [ ] Service mesh deployed
- Test results:
- Cache hit rate: X%
- Payment success rate: X%
- Latency improvement: Xms
- Blockers: [Any issues?]
- On track: Yes/No/At risk
Week 3 (Implementation):
- [ ] Full integration tested
- [ ] Chaos tests planned
- Test results:
- End-to-end response time: X seconds
- Error rate: X%
- Blockers: [Any issues?]
- On track: Yes/No/At risk
Week 4 (Testing & Analysis):
- [ ] Load testing complete
- [ ] Results documented
- [ ] Recommendation drafted
- Final Results:
- P50 latency: X seconds
- P95 latency: X seconds
- P99 latency: X seconds
- Success rate: X%
- Blockers: [Any issues?]
- On track: Yes/No/At risk
Section 9: Results & Findings
Document POC outcomes:
Executive Summary:
[One paragraph summary of findings and recommendation]
Key Findings:
1. Finding: [What we learned]
Evidence: [Data supporting this]
Impact: [Why does this matter?]
2. Finding: Async payment processing reduced response time by 80%
Evidence: P95 latency: 30s → 6s (load test results)
Impact: Solves customer abandon rate issue
3. Finding: Redis caching improved product lookup by 75%
Evidence: DB latency: 100ms → 25ms (monitoring data)
Impact: Significant throughput improvement
4. Finding: Service mesh added 5% overhead but operational benefits
Evidence: Baseline latency: 100ms → 105ms (controlled test)
Impact: Trade-off acceptable for mTLS security
Technical Metrics:
- Response time: 30s → 6s (80% reduction) ✓
- Success rate: 99.92% (exceeds 99.9% target) ✓
- Cache hit rate: 87% (exceeds 80% target) ✓
- Order reliability: 100% (zero data loss) ✓
Operational Metrics:
- Kafka operational complexity: Medium (manageable)
- Service mesh learning curve: 1 week per engineer
- Monitoring coverage: Comprehensive with DataDog
Issues Encountered:
1. Issue: Cache invalidation race condition
Resolution: Implemented event-driven invalidation
Learning: Test cache edge cases thoroughly
2. Issue: Saga pattern complexity higher than expected
Resolution: Used framework (Axon) instead of custom code
Learning: Use existing patterns, don't reinvent
3. Issue: Service mesh mTLS certificate rotation
Resolution: Automated via cert-manager
Learning: Automation critical for operational viability
Unanswered Questions:
- How well does this scale to 10x current load?
→ Recommend extended load testing
- What's the long-term operational cost?
→ Recommend cost analysis with tools and FTE
Section 10: Recommendations
Primary Recommendation:
☐ GO: Proceed with full implementation
☐ GO WITH CONDITIONS: Proceed after addressing [items]
☐ INVESTIGATE FURTHER: More research needed on [topics]
☐ NO-GO: Do not proceed, explore alternatives
Recommendation: GO - Proceed with full implementation
Rationale:
- POC successfully validated all success criteria
- Business impact (80% response time reduction) is significant
- Operational complexity is manageable
- Risk mitigation strategies are in place
Next Steps:
1. Obtain ARB approval for full implementation (Week 1)
2. Secure development resources (5 engineers, 8 weeks) (Week 2)
3. Develop production deployment plan (Week 3)
4. Execute full implementation (Weeks 4-12)
5. Production cutover with canary deployment (Week 12-13)
Timeline to Production: 14 weeks
Estimated Cost: $250k (engineering + infrastructure)
Risks to Monitor:
1. Production complexity may exceed test environment
Mitigation: Close collaboration with ops team
2. Scaling to 10x may reveal issues
Mitigation: Extended load testing in production
Success Metrics for Production:
- Response time P95: < 5 seconds
- Error rate: < 0.1%
- Cart abandonment: < 10% (down from current 15%)
POC Completion Checklist
Documentation:
☐ All sections completed
☐ Results documented with data
☐ Findings clear and actionable
☐ Recommendation clear
Learning:
☐ Success criteria met (Go/No-Go clear)
☐ Key assumptions validated
☐ Technical risks identified
☐ Operational readiness assessed
Stakeholder Communication:
☐ Results presented to sponsors
☐ ARB submission prepared (if go decision)
☐ Team feedback collected
☐ Lessons learned documented
Knowledge Transfer:
☐ Technology expertise transferred to team
☐ Runbooks/documentation created
☐ Code review and cleanup done
☐ Team training scheduled (if proceeding)
Related Resources
Status: Complete
Last Updated: November 2025
Version: 1.0
Contributors: Architecture Practice Team