Service · 01 of 05
AI that earns
its place in production.
AI features embedded in your real product. Automation that takes hours off your team's calendar. No chatbot demos.
What you get
Pragmatic AI. Real software.
Most AI projects die between the demo and the production deploy. The demo runs on a happy-path prompt and a hand-curated dataset. Production gets weird inputs, latency budgets, cost ceilings, and edge cases the demo never tested.
We build for production from day one: evals before features, retrieval before raw prompts, fallbacks before launch, and an honest answer about whether the use-case justifies AI at all. Sometimes the right answer is “this is a SQL query, not a model call.”
01
Use-case audit
Score each candidate by user value, feasibility, and cost-per-call. Cut anything a SQL query could solve.
02
Eval harness
Automated regression tests that catch prompt and model drift before users do.
03
Production wiring
Streaming, retries, fallbacks, telemetry, cost guards. The plumbing that makes AI survive contact with users.
04
Agent design
Tool-use, structured output, RAG. Agents that don't hallucinate the schema.
Sample timeline
3-phase engagement.
| Week | Phase | What happens |
|---|---|---|
| 1 | Audit | Map candidate use-cases. Score by user value, feasibility, and cost. Cut what doesn't earn its place. |
| 2–4 | Build & eval | End-to-end spike in your stack. Eval harness from day one. Iterate prompts and retrieval until quality holds. |
| 5–8 | Productionize | Streaming, fallbacks, telemetry, cost guards. Behind a feature flag. Watch the metrics. Then roll out. |
Frequently asked
The honest answers.
Are you tied to a specific model?
No. We've shipped Claude, GPT, Gemini, Llama, and open-weights via vLLM/Ollama. Choice depends on your data, latency, cost, and compliance, not on our preference.
What's a realistic budget?
Spike + eval engagements from $25k. Full productionization $75k–$200k. Ongoing eval/improvement retainers from $5k/mo. We don't quote without an audit first.
Is this just chatbots?
Almost never. Most of our AI work is invisible: better search, structured extraction, intent classification, draft generation. Chatbots are usually the wrong UX.
How do you handle hallucinations?
Constrained generation, retrieval grounding, eval harnesses with regression tests, and explicit failure modes in the UI. You can't 'eliminate' hallucinations; you engineer around them.
Have an AI use-case in mind?
Let’s see if
it ships.
Most AI ideas don’t survive an audit. Tell us yours and we’ll tell you honestly whether it’s worth building.
Book a call →