Services / AI product features
AI product features.
You have a product. Your users want it to do something AI-shaped — summarize, generate, personalize, classify. We build the feature, evaluate it honestly, ship it to your production, and budget cost and latency before the first model call.
When this is the right fit
- You have an existing product and an existing engineering team.
- You've identified a feature that should use a model — and you want it shipped, not researched.
- You care about cost-per-request and p95 latency, not just demo quality.
- You want the option to swap models later without rewriting half the app.
What we ship
- Feature implementation — frontend + backend, built into your existing app.
- Model selection — chosen by offline evals on your data and your acceptance criteria. Open and closed models compared.
- Prompt strategy — including the system prompt, few-shot examples, and structured output schema. Versioned and tested.
- Evaluation suite — automated grading for the feature's outputs, with thresholds tied to your acceptance criteria.
- Cost and latency budget — measured in production, with alerts.
- Fallback strategy — what happens when the model is down, slow, or wrong.
- Documentation — how to change the prompt, swap the model, run the evals.
Typical timeline
| Week | What ships |
|---|---|
| 1 | Scope, acceptance criteria, eval dataset, model shortlist. |
| 2 | Eval harness + first end-to-end prototype on staging. |
| 3–5 | Feature integration, UI, error handling, observability. |
| 6–8 | Hardening, cost/latency tuning, production deploy, handoff. |
Range: 4–8 weeks.
FAQ
Will you use GPT, Claude, or open-source?
We benchmark on your data. The decision lives in a written model selection memo, not in our preference.
How do you measure quality for a generation task?
A mix: deterministic rules where they apply (schema validation, length, refusal patterns), LLM-as-judge graders for subjective quality, and a small human-labeled set we periodically refresh.
We already have a prototype. Can you take it from there?
Yes — that's a common starting point. Week one is a code review and an eval baseline. We keep what works and rewrite what doesn't.