AI that ships into your actual product, not a proof-of-concept that stalls
Most "AI integration" conversations stall at a demo because nobody scoped what happens after — data readiness, cost per call at real volume, guardrails, and what a human does when the model is wrong. We scope against a specific business outcome first, then build the surrounding system that makes it reliable in production.
What’s included
- 01Use-case audit & feasibility scoping
- 02Model / vendor selection (build vs. API)
- 03Data pipeline & readiness assessment
- 04Integration into existing product or workflow
- 05Evaluation & guardrail design
- 06Cost monitoring at scale
Frequently asked
How do you decide between an API-based model and a custom-trained one?→
Cost per call at your actual expected volume, latency requirements, and how proprietary your data is. Most use cases don't justify custom training — we'll say so if that's true for yours.