Product
Adding AI to a product that already has customers
Very different from building one from scratch. There are paying users, a data model you cannot casually change, and a support team who will field whatever ships. That shapes the whole approach.
Symptoms
You'll recognize at least three of these
- A prototype impressed internally but nobody can say what it should cost to run
- Usage varies wildly per customer and pricing has not caught up
- Prompt and model changes ship with no way to tell if quality moved
- The feature works for one tenant's data shape and not others
- Support has no tooling to explain why the AI answered as it did
Levers
What we change, in order of payback
Assess before adding
Two weeks on the existing codebase and data model first, so the plan accounts for what is already there.
Gate every release
A regression suite ships with the feature. A change either clears the bar or does not go out.
Meter from day one
Per-tenant usage is instrumented in the first release, so pricing is set against real consumption rather than a guess.
Tool the support team
Support needs to see what the system did and why, without escalating to engineering every time.
A shipped feature your support team can explain, your finance team can price, and your engineers can change without holding their breath.
Engagements
How we deliver it
- 01Core AI
AI Product Development
AI-native products, and AI features inside products that already have customers.
- 02Core AI
End-to-End AI Development
First workshop to an operated system, one accountable team. Agents, retrieval, custom models.
- 03Operate
Managed Edge & Model Fleet
Drift, retraining, OTA rollout, device health and uptime reporting, under an agreed SLA.
FAQ
Questions we get
Yes — that is the normal case. We start with a two-week assessment and give you a written read on what to build on and what to replace, with the cost of each.
We instrument usage per tenant so you can see actual consumption by customer. The pricing decision is yours; the data to make it is a deliverable.
Start with a call, then a costed plan
Thirty minutes on the problem, the site and the constraints. If it looks like a fit, the next step is a four-week AI Pilot at a fixed price, with acceptance criteria signed before any code is written.