DONE FOR YOU · OUTSOURCED AI ENGINEERING
Ship production AI for your startup in 4 weeks.
For seed/Series-A founders without an in-house AI team. RAG, agents, ingestion pipelines, evals. One scoping week, then a 4-week build sprint to production.
Outcomes
What you get
RAG-in-prod in 4 weeks
Discovery + spec + a working pipeline behind your data.
Agents with eval-CI guardrails
Tool-using agents with eval CI, red-team, and observability.
Document ingestion pipelines
OpenSearch / Bedrock ingestion that stays inside your AWS account.
Receipts
Proof of capability
A working RAG + agent stack
Full pipeline: ingest, retrieve, agent, eval CI. AI-coded, walked through live on the scoping call.
Eval CI as a merge gate
Recorded judge cassettes gate every PR, so no model regression ships to production.
Observability from day one
OpenTelemetry + Prometheus + structlog wired in, not bolted on later.
Architecture you own
ADRs, a chunked implementation spec, and the code transferred to you — no lock-in.
Laws we live by · LLMs in production
10 laws for shipping AI to production without burning cash
Every sprint we run for a founder applies these laws. They're why we ship RAG-in-prod in 4 weeks, not a quarter.
- 01
Spec hard, code soft.
A page of working spec is worth a week of throwaway code. LLMs accelerate the wrong thing if the spec is wrong.
- 02
Evals before the model call.
Write the failing eval first. Without a passing bar you don't have a product, you have a forever-prototype.
- 03
Tools beat prompts.
A 20-line tool with a strict schema beats a 2,000-token system prompt. The model recovers from a wrong tool call; it doesn't recover from a vague instruction.
- 04
Cache aggressively, route ruthlessly.
Prompt cache, embedding cache, response cache. Cheap model routes; expensive model produces. 80% of the bill is the wrong model on the wrong call.
- 05
Monolith for LLMs, microservices for humans.
LLMs read a monolith faster than a 12-service mesh. Split early and you lose the one thing they're best at: holding the whole system at once.
- 06
Curate the context, don't polish the prompt.
The model is only as good as what's in the window. Cut every token that isn't a fact, an example, or a constraint.
- 07
Receipts, not claims.
Every “it works” is a test run, an eval row, or a git log line. Vibes don't ship.
- 08
Subagent review = 2 stages.
Finder + adversarial verifier. One agent gets confidently wrong; two disagreeing agents force the truth.
- 09
Manager mode: 5 → 1.
One operator orchestrating 5 parallel agents ships what used to need a team. Lanes, async, documented handoffs.
- 10
Player-coach.
Ship code and review agents in the same day. The skill is reading whether today needs hands-on-keyboard or orchestration.
Fit
Who this is for
- ✓ Pre-seed – Series A
- ✓ No in-house AI team
- ✓ Regulated or proprietary data
- ✓ Wants to own the code
- — Needs a 24/7 support contract
- — Wants commodity per-hour body-shop pricing
- — No clear data or problem yet