AI for product teams. AI for product teams means shipping an AI feature inside your own product surfaces that users adopt and keep using. Not a demo. A feature with a retention number and a support-ticket number behind it.
Most product teams already have a prototype. You built it in a weekend with Lovable, Bolt, or v0. It looked great in the standup. Then it stalled on the way to production.
The gap is not the model. The gap is everything around it. Streaming, evals, audit logging, and a place in the product where the user already is. That is the work between a v0 screen and a feature your users keep.
#Why does the prototype stall before launch?
A prototype answers one question: can the model do the thing. A production feature answers four more. Does it stream so the user does not stare at a spinner. Does it stay correct when the prompt changes. Can you prove what it told a customer six weeks ago. And does it live where the user already works, not behind a separate tab.
What is the difference between an AI prototype and a production feature?
A prototype proves the model can do the task in a demo. A production feature adds streaming so responses feel instant, an eval suite so quality holds when prompts change, audit logging so you can prove what was said, and in-product UX so users adopt it. The prototype is the easy 20 percent.
#Should you bolt on a chat box or build inside your surfaces?
The default move is a floating chat bubble in the corner. It ships fast. It also sits unused, because your user did not come to chat. They came to send a payment, reconcile a statement, or open a dispute.
The better move is to put the AI inside the surface where that job happens. A smart default on the form. A one-click explanation next to the transaction. An answer rendered in the panel the user already had open.
We are
Stennir builds AI into the product surfaces your users already use, with adoption and support-ticket metrics defined up front so you can see whether it landed.
We aren't
Stennir is not a team that bolts a generic chat box onto your app and calls it an AI feature.
#What does this look like for a regulated fintech?
For illustration, take a series b fintech with a customer-facing AI assistant built in Lovable. It answers questions in a demo. It cannot go to production: no streaming, no way to measure quality, no audit trail for a regulated product. This is a hypothetical team, not a client.
- streaming: responses render token by token, so the user sees an answer forming instead of a loading spinner.
- eval suite: a fixed set of real questions runs on every prompt change, so quality does not silently regress when someone edits the system prompt.
- audit logging: every answer is recorded with its inputs, so a regulated fintech can prove what a customer was told and when.
- in-product UX: the assistant answers inside the transaction view the user already had open, not in a separate chat tab.
A team in this position would roll the feature out to a small share of users first, then widen it. The numbers to watch are adoption of the feature and support tickets on the topic it covers, because users who get the answer in the product do not open a ticket to ask. Set both baselines before launch so the change can be measured.
The prototype shows people want it. The production work is what makes them keep using it. The ticket number is the one a CFO will ask about.
#How do you scope a build like this?
We run it through AI implementation: the code is yours from day one. You keep the prototype you already made in v0, Bolt, or Lovable as the starting point. We take it the rest of the way to a feature in your product. Scope and terms are agreed on the 30-minute call.
If you want the pattern for your team specifically, the AI for product page lays out where AI earns its place inside a product, and AI consulting is where these builds are scoped. Want to see how we think before you commit? Read the journal, or book a 30-min discovery call.
