For the product team adding AI to a live product
How to add AI features to a live product with evaluation, cost control and a way back if something goes wrong.
The problems we hear most
- The demo worked and production is a different animal
- Model costs and latency are unpredictable
- Nobody agrees what "good" means for an AI feature
- Your engineers are busy keeping the product running
What we build for you
- AI features behind a service layer, shipped in stages to a small group first
- Evaluation on every prompt or model change, with a release gate
- Cost per user and per feature, with budgets and alerts
- Pair-working with your engineers so the knowledge stays with you
What the first four weeks look like.
Week 1
Choose the feature, set the quality bar and build the evaluation set from real cases
Week 2
First working version behind a feature flag
Weeks 3 to 4
Limited release, cost and quality dashboards, and a decision on wider rollout
Timelines depend on data access and approval speed. We confirm them on the strategy call.
Services most relevant to you
AI Product and Platform Development
AI-native web, mobile and API products designed around what the model can and cannot do, shipped and maintained by one team.
Learn more →Generative AI Application Development
Customer-facing and internal LLM products, from document drafting to AI copilots inside your SaaS.
Learn more →AI Evals, Red-Teaming and Guardrails
Know whether your AI works before you ship it, and keep knowing after: evaluation suites, red-teaming, guardrails and live monitoring.
Learn more →Which of these sounds like your week?
Pick the closest problem. An engineer replies within one business day with how we would start and what the first month would leave you with.
- A first-month plan for your role
- What we would build and what we would leave out
- An honest view on whether now is the time