We build production-grade AI features into real products: agents that complete workflows, RAG systems that answer from your data, and generative UX that feels native — with evals, guardrails, and cost control built in from day one.
Starts at $499 · Typical range $499–$2,999 · Model-agnostic (OpenAI, Anthropic, open-source)
What you get
Every engagement pairs senior engineering, product design, and AI expertise on a single small team. No account managers, no sub-contracting — the people writing the code are on your calls.
How it works
Outcomes
Every feature ships behind a metric — time saved per user, task completion, deflection rate. If it doesn't move the number, it doesn't ship.
Cost per request tracked from day one. We choose model + prompt + caching so unit economics work at scale.
Your fine-tunes, your evals, your retrieval index — proprietary artifacts that stay yours, not the model provider's.
Process
Where AI actually earns its keep in your product — and where it's a $0 ROI distraction.
Build the sharpest feature. Set up eval harness before shipping. Measure real accuracy, not vibes.
Guardrails, cost controls, observability, fallbacks. Load-test the real user path.
Ship behind a flag. Instrument. Iterate on the eval set weekly until the metric moves.
The difference
Stack we ship on
Deliverables
You own the repo, the model configs, the evals, the dashboards. No lock-in, no repo held hostage, no black-box handoff.
They built a private RAG stack on our own infra. Legal signed off in a week — that alone was worth the engagement.
FAQ
We're model-agnostic — GPT-5.x, Claude, Gemini, or open-source (Llama, Mistral) depending on the task, cost profile, and privacy requirements. Architecture supports swapping without a rewrite.
Yes — but honestly. Agents work when the task is bounded, tools are well-defined, and there's a human-in-the-loop for the last 10%. We'll tell you when 'workflow' beats 'agent'.
Every production feature ships with evals, retrieval grounding, and guardrails proportional to the risk. Financial, medical, and legal contexts get stricter treatment.
Cost per request is instrumented from day one — model choice, caching, prompt compression, batching. We regularly cut AI bills by 60–80% on inherited codebases.
When it earns its keep — typically for style, structured output, or domain vocabulary. Fine-tuning is a last resort, not a first move. Retrieval + good prompting solves 80% of cases.
30-min intro call. No slides. We diagnose the shortest path from where you are to shipped.
Book a free intro call