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Team Enablement & AI Delivery Coaching

Client
Seed Stage SaaS
Role
Fractional Head of AI
Timeline
5 Weeks
Tech Stack
Next.jsFastAPIPostgreSQLLangChainOpenAISupabase

TL;DR

The founding team had a working prototype but zero confidence shipping AI features to production. I embedded with their five engineers, rebuilt their delivery rituals, and co-implemented the first three AI-powered workflows. By the time I rolled off, they were shipping without me.

Key Results

5
Engineers Coached
3
AI Features Launched
2x
Release Velocity

What Was Broken

⚠️

The Problems

  • The team shipped to staging only once every two weeks
  • No one could explain how prompts were evaluated—QA literally eyeballed random outputs
  • Webhooks failed silently because no one owned observability
  • Knowledge was stuck in the CTO's head

The Playbook

📐

1. Architecture Guardrails

Documented how data flows between Supabase, FastAPI, and OpenAI. Added sequence diagrams inside Notion so onboarding took hours, not weeks.

2. Evaluation Harnesses

Built a LangChain evaluation notebook + Supabase table to score prompts deterministically. Engineers finally had a red/green signal.

👥

3. Pair Programming Sprints

Sat with each engineer to implement one AI workflow end to end (ingestion → vector store → API → UI).

🔧

4. Ops Hardening

Added retry/backoff policies, structured logging, and a "pager" rotation so production incidents weren't guesswork.

📚

5. Playbooks

Captured seven SOPs (deploying embeddings, rotating keys, debugging prompts, etc.) so the team had references after I left.

The Architecture Flow

Data Enrichment Workflow

UI
Submit data for enrichment
API
Persist payload + job id → Return 202 Accepted
Worker
Queue job (Redis) → Call LLM with guarded prompt
LLM
Return structured JSON result
Worker
Store result + evaluation score → Emit webhook
API
SSE pushes progress updates to UI

Outcome

Results

  • Team ships twice a week with automated evaluation gates
  • Founders have visibility through dashboards + alerts
  • Engineers own the system; I'm on a lightweight advisory retainer only
2x
Faster Releases
100%
Team Confidence
7
SOPs Created

The Lesson

Most AI teams don't need more models—they need better processes. Shipping AI reliably is 30% prompts, 70% engineering discipline.

💭

Key Takeaways

  • Documentation beats tribal knowledge: Write it down or it doesn't exist
  • Evaluation is non-negotiable: You can't improve what you don't measure
  • Pair programming transfers knowledge: Better than any workshop
  • Observability prevents firefighting: See problems before users do

Need an embedded lead to unblock your internal team?

I can help your engineers ship AI features confidently through hands-on coaching, architecture reviews, and process improvements.

💳 No payment required to book • 📅 Free 15-min discovery call