AI Features in Your Product
Assistants, search, and copilots inside the product you already have, measured before release and with costs under control.
Demos are easy. Production is not.
An AI feature that looks good in a demo can still give wrong answers to a share of users, cost more per request than the user pays, or change behavior when the model provider ships an update. We treat evaluation, cost, and fallbacks as part of the feature from day one.
What you get
- Assistant over your content. Answers from your help center, documents, or catalog with links to the source (RAG).
- Search that understands intent. Semantic search across products, documents, or tickets.
- Copilots in workflows. Draft, summarize, classify, and extract inside the screens people already use.
- Document understanding. Fields pulled from invoices, contracts, or forms, with a human check where it matters.
- On-device models. For privacy or offline use: small models running on the phone or laptop.
Every AI feature ships with
- An evaluation set and a measured quality score before release
- Per-user cost limits, caching, and cheaper models for simple steps
- Fallbacks when a model is slow, down, or unsure
- Analytics: usage, quality signals, cost per request
From test set to release
The evaluation set comes first, so every later change can be measured against it.
- Week 1
Evaluation set
Real requests collected, expected answers, or grading rubrics agreed.
- Weeks 2–4
Build
Retrieval, prompts, integration into your app and backend.
- Weeks 5–6
Measure and tune
Quality, latency, and cost per request against release targets.
- Weeks 7–8
Release
Staged rollout, monitoring dashboards, handover.
What we need from you
- Repository access or a clear API boundary we can build against.
- A product owner who decides what “good enough” means.
- Real examples of user requests, tickets, or documents the feature will handle.
Questions
Can you add AI to an app someone else built?
Yes. We start with a short code review, then propose where AI fits and what it will cost to run. We work in your repository and follow your conventions.
How do you keep AI costs predictable?
Per-user limits, caching, smaller models for simple steps, and a cost-per-request estimate before release. You see the numbers before users do.
Which models do you use?
Whatever fits the task, budget, and privacy requirements: cloud models through their APIs or open-weight models on your infrastructure. The evaluation set decides, not habit.
Will our users' data train a model?
No. We configure providers with no-training options where they offer them, and we don't use your data for training.
Show us the product. We'll show you where AI fits.
A 30-minute call is enough to tell whether AI fits, what it would cost, and where to start. We reply within one business day.