ai engineering
for production
Custom LLM features, retrieval pipelines, and evals — built by an engineer who trained a language model from scratch, not just called an API.
response within 1 business day · no obligation
tell me about your project
what you get
Custom LLM features
Chat, extraction, classification, and generation built into your product — prompts, tools, and fallbacks engineered, not improvised.
Retrieval pipelines (RAG)
Your documents indexed with vector search so answers cite your data instead of hallucinating — Pinecone-style retrieval done properly.
Evals before launch
Every AI feature ships with an evaluation harness — measured accuracy on your real cases, not a demo that worked once.
Production-grade operations
Token budgets, latency targets, monitoring, and cost controls — AI features that survive contact with real traffic.
from idea to production
use case & feasibility
A working session on your data, workflow, and what AI should actually do. You get a written scope, an eval plan, and a fixed quote.
prototype & iterate
A working prototype against your real data in weeks. We tune retrieval, prompts, and models against the eval set together.
ship behind metrics
The feature lands with monitoring, rate limits, and rollback — measured against the eval baseline before real users touch it.
measure & improve
Ongoing evals as models change, cost tracking, and iteration. AI systems drift; the process accounts for it.
scoped to the job
Every project is priced custom for the job — discovery settles feasibility and price together, with a fixed quote before any work begins.
pilot
- One AI feature, production-ready
- Eval harness included
- Your stack or mine
product
most common- RAG over your documents
- Multiple integrated features
- Monitoring & cost controls
platform
- Custom model training
- Kubernetes-grade serving
- Compliance-ready ops
general notes
Usually an API model with good retrieval and evals gets you there — and it's the cheapest place to start. I've trained a language model from scratch, so when a custom or fine-tuned model is genuinely justified, I can build that too. The discovery session settles it with numbers, not vibes.
Every AI project is priced custom for the job — model choice, data readiness, and integration surface set the number. Discovery settles feasibility and price together: you get a fixed quote, including expected model and infrastructure costs, before any work begins.
Your data stays in your accounts wherever possible, with least-privilege access, encryption in transit and at rest, and written data-handling terms in the scope. I led a SOC 2 Type II certification as CTO, and the same discipline applies here.
That's what the eval gate is for. We agree on an accuracy bar during discovery, and the feature doesn't launch below it. If the numbers say it isn't viable, you find out at prototype stage — not after a full build.
have a workflow ai should be doing?
Free 30-minute consult. I'll tell you honestly whether AI fits — and what it will cost to find out.