Project Planning — Man-Month Breakdown & Team Assignment
Detailed workload allocation by man-month, specific dev assignments
Timeline: 9 months (6 cycles × 6 weeks) | Team: 5 engineers | AI 2x multiplier
1. Team Roster
| ID |
Role |
Seniority |
Primary Domain |
Secondary Domain |
| D1 |
Tech Lead / Fullstack |
Senior (8y+) |
Architecture, Gateway, ACL |
Travel Booking |
| D2 |
Backend Engineer |
Senior (5y+) |
Travel Booking |
Event Management |
| D3 |
Backend Engineer |
Mid (3y+) |
Event Management |
Communications |
| D4 |
Fullstack Engineer |
Mid (3y+) |
Workforce, Comms |
Frontend (React) |
| D5 |
Frontend / DevOps |
Mid (3y+) |
React Frontend, CI/CD |
Reporting |
Actual capacity:
- Raw: 5 engineers × 9 months = 45 man-months
- Minus overhead (meetings, review, learning): ~60% productive → 27 productive man-months
- AI 2x multiplier → 44 effective man-months (≈ output of 10 engineers)
2. Phase Overview
Month: 1 2 3 4 5 6 7 8 9
├────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┤
Phase 0: █████████ AI Foundation
Phase 1: ████████████████████ Core Services
Phase 2: ████████████████████ Scale Out
Phase 3: ████████████████████ Optimize
├────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┤
Cycle: |--C0---|---C1--|---C2--|---C3--|---C4--|---C5--|
| Phase |
Duration |
Focus |
Output |
| Phase 0 |
Month 1 |
AI Foundation + Infra |
AI toolchain, CI/CD, scaffold, Docker, IaC |
| Phase 1 |
Month 2–4 |
Core Services |
Travel Booking + Event Management go-live |
| Phase 2 |
Month 5–7 |
Scale Out |
Workforce + Comms + Reporting go-live |
| Phase 3 |
Month 8–9 |
Hardening |
Performance, monitoring, docs, Payment ACL v2 |
3. Man-Month Breakdown (Detailed)
3.1 Summary Table
| Work Package |
Man-Months (effective) |
Assigned |
Phase |
| AI Foundation & Tooling |
3.0 |
D1, D5 |
0 |
| Infrastructure / IaC / CI/CD |
4.0 |
D5, D1 |
0–1 |
| API Gateway (YARP) |
2.0 |
D1 |
0–1 |
| Shared Kernel & Contracts |
1.5 |
D1 |
0 |
| Travel Booking Service |
6.0 |
D2, D1 |
1 |
| Event Management Service |
5.0 |
D3, D2 |
1–2 |
| Workforce Service |
4.0 |
D4 |
2 |
| Communications Service |
3.5 |
D4, D3 |
2 |
| Reporting Service |
3.5 |
D5, D3 |
2–3 |
| Payment ACL |
2.0 |
D1, D2 |
1 |
| React Frontend |
5.0 |
D5, D4 |
1–3 |
| Testing & QA |
2.5 |
All |
1–3 |
| Performance & Hardening |
2.0 |
D1, D2 |
3 |
| Documentation & Handoff |
1.0 |
All |
3 |
| TOTAL |
45.0 ≈ 44 effective |
— |
— |
3.2 Breakdown by Service
Travel Booking Service — 6.0 man-months
| Task |
Effort |
Owner |
Support |
| Legacy analysis (AI-assisted) |
0.5 |
D2 |
D1 |
| Domain modeling + DB schema |
0.5 |
D2 |
D1 |
| API implementation (CRUD + search) |
1.5 |
D2 |
— |
| Business logic migration |
1.5 |
D2 |
D1 review |
| Contract tests (Pact) |
0.5 |
D2 |
— |
| Integration tests + E2E |
0.5 |
D2 |
D5 |
| CDC setup (legacy → new DB) |
0.5 |
D1 |
D2 |
| Strangler Fig cutover |
0.5 |
D1 |
D2 |
Event Management Service — 5.0 man-months
| Task |
Effort |
Owner |
Support |
| Legacy analysis (AI-assisted) |
0.5 |
D3 |
D1 |
| Domain modeling + DB schema |
0.5 |
D3 |
D1 |
| API implementation |
1.0 |
D3 |
— |
| Business logic migration |
1.0 |
D3 |
D2 review |
| Event workflows (create, cancel, modify) |
0.5 |
D3 |
— |
| Contract tests + integration |
0.5 |
D3 |
— |
| CDC setup + cutover |
0.5 |
D1 |
D3 |
| Calendar integration |
0.5 |
D3 |
D4 |
Workforce Service — 4.0 man-months
| Task |
Effort |
Owner |
Support |
| Legacy analysis |
0.5 |
D4 |
D1 |
| Domain modeling + schema |
0.5 |
D4 |
D1 |
| API implementation (schedules, leave, assignments) |
1.0 |
D4 |
— |
| Business logic migration |
1.0 |
D4 |
D1 review |
| Contract tests + integration |
0.5 |
D4 |
— |
| CDC + cutover |
0.5 |
D1 |
D4 |
Communications Service — 3.5 man-months
| Task |
Effort |
Owner |
Support |
| Legacy analysis |
0.3 |
D4 |
D3 |
| Domain modeling |
0.2 |
D4 |
— |
| Email / SMS / Push notification APIs |
1.0 |
D4 |
— |
| Template engine migration |
0.5 |
D4 |
— |
| Event-driven triggers (subscribe to other services) |
0.5 |
D3 |
D4 |
| Contract tests + integration |
0.5 |
D4 |
— |
| CDC + cutover |
0.5 |
D1 |
D4 |
Reporting Service — 3.5 man-months
| Task |
Effort |
Owner |
Support |
| Legacy analysis |
0.3 |
D5 |
D1 |
| Domain modeling (CQRS read models) |
0.5 |
D5 |
D1 |
| Report APIs + query optimization |
1.0 |
D5 |
D3 |
| Dashboard data aggregation |
0.5 |
D5 |
— |
| Scheduled report generation |
0.5 |
D3 |
D5 |
| Contract tests + integration |
0.5 |
D5 |
— |
| Cutover |
0.2 |
D1 |
D5 |
Payment ACL — 2.0 man-months
| Task |
Effort |
Owner |
Support |
| Legacy Payment API analysis |
0.3 |
D1 |
D2 |
| ACL interface design |
0.2 |
D1 |
— |
| ACL implementation (adapter pattern) |
0.5 |
D1 |
D2 |
| Error handling + retry + idempotency |
0.5 |
D2 |
D1 |
| Contract tests (new service ↔ legacy) |
0.3 |
D2 |
— |
| Circuit breaker + monitoring |
0.2 |
D1 |
— |
4. Timeline — Dev Assignment theo Month
Month 1 — Phase 0: AI Foundation
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
AI toolchain setup (Cursor, Claude Code, prompts) |
Shared Kernel, API Gateway scaffold |
AI ready, Gateway MVP |
| D2 |
AI tool training, legacy Travel analysis |
Travel domain modeling |
Travel spec ready |
| D3 |
AI tool training, legacy Event analysis |
Event domain modeling |
Event spec ready |
| D4 |
AI tool training, legacy Workforce analysis |
React design system POC |
Design system base |
| D5 |
CI/CD pipelines (GitHub Actions) |
Docker Compose, IaC (Bicep), ACR setup |
Infra ready |
Week 4 — AI Pipeline Validation: D3 + D4 pilot-migrate Communications module using agentic AI.
Output: Comms service deployed to staging. Validates AI migration pipeline before applying to Travel.
(Comms production go-live will be in Phase 2 — this is only a pilot validation.)
Month 1 man-months: 5.0 raw → 2.0 effective (heavy learning overhead, AI setup investment)
Month 2 — Phase 1: Travel Booking Sprint 1
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
YARP Gateway routing, ACL scaffold |
Payment ACL implementation |
Gateway + ACL MVP |
| D2 |
Travel API (CRUD endpoints) |
Travel business logic migration |
Travel service 60% |
| D3 |
Event API scaffold |
Event CRUD implementation |
Event service 30% |
| D4 |
Workforce domain modeling |
React: Travel module UI |
Frontend Travel 40% |
| D5 |
IaC environments (Dev, Staging) |
Travel CDC setup |
Dev/Staging live |
Month 2 man-months: 5.0 raw → 5.0 effective (AI kicking in)
Month 3 — Phase 1: Travel Go-Live + Event Sprint
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
Travel cutover (Strangler Fig, 5% → 100%) |
Code review + architecture guidance |
✅ Travel LIVE |
| D2 |
Travel contract tests, bug fixes |
Event business logic support (D3) |
Travel stable |
| D3 |
Event business logic migration |
Event workflows (create/cancel/modify) |
Event service 70% |
| D4 |
React: Travel UI polish |
React: Event module UI |
Frontend Event 40% |
| D5 |
Travel monitoring dashboard |
Event CDC setup |
Monitoring live |
Month 3 man-months: 5.0 raw → 5.5 effective (AI mastery improving)
Month 4 — Phase 1→2: Event Go-Live + Workforce Start
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
Event cutover (5% → 100%) |
Workforce architecture review |
✅ Event LIVE |
| D2 |
Event contract tests, stabilize |
Payment ACL hardening |
ACL production-ready |
| D3 |
Event integration tests |
Comms service scaffold + analysis |
Event stable |
| D4 |
Workforce API implementation |
Workforce business logic |
Workforce 40% |
| D5 |
React: Event UI polish |
React: Workforce module start |
Frontend Workforce 20% |
Month 4 man-months: 5.0 raw → 5.5 effective
Month 5 — Phase 2: Workforce + Comms Sprint
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
Architecture review, tech debt cleanup |
Comms architecture, ACL improvements |
Tech debt resolved |
| D2 |
Workforce business logic review |
Comms event triggers implementation |
Comms 30% |
| D3 |
Comms API (email/SMS/push) |
Comms template engine migration |
Comms 60% |
| D4 |
Workforce business logic + tests |
Workforce CDC + cutover prep |
Workforce 80% |
| D5 |
React: Workforce UI |
Reporting service analysis + scaffold |
Frontend Workforce done |
Month 5 man-months: 5.0 raw → 5.5 effective
Month 6 — Phase 2: Workforce Go-Live + Comms + Reporting
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
Workforce cutover (5% → 100%) |
Reporting CQRS architecture design |
✅ Workforce LIVE |
| D2 |
Comms event-driven triggers |
Reporting query APIs |
Reporting 30% |
| D3 |
Comms integration tests |
Comms CDC + cutover prep |
Comms 90% |
| D4 |
React: Comms UI |
React: Reporting dashboards start |
Frontend Comms done |
| D5 |
Reporting data aggregation |
Reporting scheduled jobs |
Reporting 50% |
Month 6 man-months: 5.0 raw → 5.5 effective
Month 7 — Phase 2→3: Comms + Reporting Go-Live
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
Comms cutover (5% → 100%) |
Reporting cutover (5% → 100%) |
✅ Comms LIVE, ✅ Reporting LIVE |
| D2 |
Cross-service integration tests |
Performance baseline measurement |
All services connected |
| D3 |
Reporting scheduled reports |
Bug fixes across services |
Reporting stable |
| D4 |
React: Reporting dashboards |
React: cross-module integration |
Frontend 90% |
| D5 |
Production monitoring setup |
Load testing preparation |
Monitoring complete |
Month 7 man-months: 5.0 raw → 5.0 effective
Month 8 — Phase 3: Hardening
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
Performance optimization (hotspots) |
Security audit + pen test fixes |
Production-grade |
| D2 |
Load testing (40K users simulation) |
Performance fixes from load test |
Benchmarks met |
| D3 |
Bug fixes, edge cases |
Operational runbooks |
Stable services |
| D4 |
React: performance optimization |
React: accessibility (a11y) |
Frontend polished |
| D5 |
Alerting rules tuning |
Disaster recovery testing |
DR validated |
Month 8 man-months: 5.0 raw → 4.5 effective (perf work = less AI leverage)
Month 9 — Phase 3: Polish + Handoff
| Dev |
Week 1–2 |
Week 3–4 |
Output |
| D1 |
Architecture documentation |
Knowledge transfer, ADR finalization |
Docs complete |
| D2 |
API documentation (OpenAPI) |
Legacy decommission plan (Phase 2 roadmap) |
API docs live |
| D3 |
Runbooks, incident playbooks |
End-to-end regression suite |
Ops ready |
| D4 |
React: design system documentation |
User training materials |
Training done |
| D5 |
CI/CD optimization, monitoring docs |
Capacity planning for next phase |
DevOps mature |
Month 9 man-months: 5.0 raw → 4.0 effective (docs = less AI leverage)
5. Capacity Summary
5.1 Man-Months by Dev
| Dev |
M1 |
M2 |
M3 |
M4 |
M5 |
M6 |
M7 |
M8 |
M9 |
Total Raw |
Role Load |
| D1 |
AI + GW |
GW + ACL |
Cutover |
Cutover |
Review |
Cutover |
Cutover |
Perf |
Docs |
9.0 |
60% arch, 40% code |
| D2 |
Analysis |
Travel |
Travel |
ACL |
Comms |
Report |
Test |
Load test |
Docs |
9.0 |
80% code, 20% review |
| D3 |
Analysis |
Event |
Event |
Event |
Comms |
Comms |
Report |
Bugfix |
Runbook |
9.0 |
85% code, 15% review |
| D4 |
Analysis |
React |
React |
Workforce |
Workforce |
React |
React |
React |
Docs |
9.0 |
50% backend, 50% FE |
| D5 |
Infra |
IaC |
Monitor |
React |
React+Report |
Report |
Monitor |
DR |
DevOps |
9.0 |
40% infra, 30% FE, 30% BE |
5.2 Effective Output (with AI 2x)
Baseline (Analysis v2 — conservative, phase-by-phase):
Gross capacity: 45 man-months (5 eng × 9 months)
Overhead (40%): -18 man-months (meetings, review, learning)
Phase-by-phase (variable AI multiplier):
P0 (M1): 5.0 raw - 3.0 overhead = 2.0 net × 1.0 = 2.0
P1 (M2-4): 15.0 raw - 6.0 overhead = 9.0 net × 2.0 = 18.0
P2 (M5-7): 15.0 raw - 5.5 overhead = 9.5 net × 2.0 = 19.0
P3 (M8-9): 10.0 raw - 3.5 overhead = 6.5 net × 1.0 = 6.5
────────────────────────────────────────────────────────────────
Effective capacity: 45.5 ≈ ~44 man-months (conservative)
Per-phase breakdown (variable multiplier):
Raw Overhead Net Multiplier Effective
──── ──────── ─── ────────── ─────────
Phase 0 (Month 1): 5.0 -3.0 2.0 ×1.0* 2.0
Phase 1 (Month 2–4): 15.0 -6.0 9.0 ×2.0 18.0
Phase 2 (Month 5–7): 15.0 -5.5 9.5 ×2.0 19.0
Phase 3 (Month 8–9): 10.0 -3.5 6.5 ×1.0** 6.5
──── ────── ─────
TOTAL: 45.0 -18.0 45.5
* Phase 0: AI not yet set up → multiplier = 1.0
** Phase 3: Performance/docs → low AI leverage → 1.0x
~44 effective man-months (conservative baseline from Analysis v2).
Per-phase calc yields ~45.5, confirming 44 is achievable and defensible.
5.3 Utilization Heatmap
Dev: M1 M2 M3 M4 M5 M6 M7 M8 M9
D1: ████ █████ █████ ████ ███ ████ █████ ████ ███
D2: ██ █████ █████ █████ ████ ████ ████ █████ ███
D3: ██ ████ █████ █████ █████ █████ ████ ███ ███
D4: ███ ████ ████ █████ █████ ████ ████ ████ ███
D5: █████ █████ ████ ████ ████ ████ ████ ████ ███
Legend: █ = ~20% utilization █████ = 100%
6. Milestones & Go/No-Go Gates
| Milestone |
Date (End of) |
Go/No-Go Criteria |
Decision Owner |
| M0: AI Foundation Ready |
Month 1 |
CI/CD green, Docker local works, AI tools deployed |
D1 (Tech Lead) |
| M1: Travel Booking Live |
Month 3 |
100% traffic via new service, error rate < 0.5%, latency < 200ms p95 |
D1 + Stakeholder |
| M2: Event Management Live |
Month 4 |
Same metrics + calendar integration working |
D1 + Stakeholder |
| M3: Workforce Live |
Month 6 |
Same metrics + schedule/leave APIs verified |
D1 + Stakeholder |
| M4: Comms + Reporting Live |
Month 7 |
All 5 services live, cross-service flows verified |
D1 + Stakeholder |
| M5: Production Hardened |
Month 9 |
Load test passed (40K users), DR tested, docs complete |
D1 + Management |
Go/No-Go Checklist (per service cutover)
□ All contract tests passing (Pact verified)
□ Integration tests green
□ CDC data integrity verified (new DB vs legacy = match)
□ Error rate < 0.5% on canary (5% traffic, 48h soak)
□ Latency p95 < 200ms (or within 10% of legacy)
□ Rollback tested and documented
□ Monitoring + alerting configured
□ Stakeholder sign-off
7. Risk Buffer
| Risk |
Impact |
Buffer |
Mitigation |
| Travel Booking more complex than expected |
M1 delay 2 weeks |
2 weeks Phase 1 buffer |
D1 pair-programs with D2, deprioritize Event |
| AI tools don’t reach 2x |
Total capacity reduced 25% |
Reduce scope: defer Reporting |
Measure velocity monthly, adjust |
| Team member out for 1 month |
Lose 1 man-month |
Cross-training (secondary domain) |
D2↔D3 can swap, D4↔D5 swap frontend |
| Legacy codebase undocumented |
Analysis takes twice as long |
Phase 0 analysis + AI ingestion |
Gemini 2.5 Pro 1M context for legacy scan |
| Azure Service Bus learning curve |
Event-driven flows delayed |
1 week buffer per phase |
POC in Phase 0, D1 mentors team |
Scope Lever (if behind schedule)
Priority: MUST HAVE SHOULD HAVE NICE TO HAVE
───────── ─────────── ────────────
Services: Travel + Event Mgmt Workforce + Comms Reporting (CQRS)
Frontend: Travel + Event UI Workforce UI Dashboard (Reporting)
Infra: CI/CD + Docker + ACR Full IaC (Bicep) DR automation
Quality: Contract tests Load testing Security pen test
Docs: ADRs + runbooks Full API docs Training materials
Lever rule: If by month 4 there are not 2 services live → reduce Phase 2 scope to 1 service (Workforce only), defer Comms + Reporting to next phase.
8. Weekly Cadence
Monday: Sprint planning (30 min) — what each dev does this week
Tuesday-Thu: Heads-down development (async standups via Slack)
Friday AM: Code review session (60 min) — cross-service PRs
Friday PM: Demo + retrospective (every 2 weeks)
Cycle (6-week):
Week 1-4: Build (heads down)
Week 5: Integration + testing
Week 6: Cutover / cooldown / tech debt