AIESCU
Production AI, shipped in 4 weeks.
We build it for your startup, or teach you to build it yourself with Claude Code. One engineer running agents in parallel, on the hook for the result.
99.9%
API availability on a client's production platform
10×
cut in a client's infrastructure costs
93%
retrieval accuracy on a client's production document AI (CI-gated)
15+ yrs
shipping production software
Need it shipped? Start on the left. Want to learn? Start on the right.
DONE FOR YOU
We build your production AI.
- RAG-in-prod in 4 weeks
- Agents with eval CI + guardrails
- Document ingestion pipelines
DO IT YOURSELF
Learn to ship it with Claude Code.
- 2-day intensive
- 6-week AI-engineer program
- Lifetime access + skills library
Choose Done-for-you if…
- You need it in production fast, without hiring an AI team
- You’d rather own the finished system than build it
- You have real data and a real problem right now
Choose Do-it-yourself if…
- You’re an engineer who wants to build it yourself
- You want the skills for the long term, not just a deliverable
- You prefer self-paced, with lifetime access
- RAG + agent stack live in 4 weeks
- Eval CI gates every PR
- Observability from day one
- Brief open for inspection
Laws we live by · LLMs in production
10 laws for shipping AI to production without burning cash
What we've learned building with LLMs. Every build and every course follows them.
- 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.