AI in your product
LLM features built into the software your customers already use, designed so that the cost, the latency and the failure behaviour are all decided rather than discovered.
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Features worth adding
The ones that remove a step users currently resent: drafting a description from a few bullet points, summarising a long record before someone opens it, classifying an incoming message so it routes itself, answering a question about the data already on screen, or extracting structure from something a user pasted in. What is rarely worth adding is a general chat box in the corner. Users do not want a conversation with your product; they want the task done. The best AI features are usually invisible as AI, appearing simply as the product being noticeably less tedious.
Engineering the unglamorous parts
AI features fail in production for boring reasons. The provider has an outage and the whole page breaks because nobody wrote a fallback. Latency turns a snappy screen into a four-second wait. A user pastes ten thousand words and the cost per call multiplies. A prompt injection in user-supplied content redirects the model. So we build with timeouts and graceful degradation, streaming where a wait is unavoidable so something appears immediately, caps on input size, caching for repeated requests, and treatment of user content as untrusted. None of this is glamorous and all of it is the difference between a feature that survives and a demo.
Cost per user, not cost per call
The question that decides whether an AI feature is viable is not what a call costs but what an active user costs per month. A feature used once a week by a few hundred people is trivially affordable. The same feature used constantly by thousands may not be, and that is worth knowing before launch rather than at the first invoice. We model it, and where the numbers are tight we look at the usual levers: a cheaper model for the simple majority of requests, caching, and moving work to a cheaper path when the input is easy. Often that halves the bill with no visible difference to users.
What you get
Features, not a chat box
The best AI in a product is invisible as AI. Users want the task done, not a conversation.
Degrades gracefully
Timeouts and fallbacks, so a provider outage is a missing feature rather than a broken page.
Streaming where waits are real
Something appears immediately instead of a spinner, because perceived latency is what users judge.
Cost modelled per active user
Per-call price is not the number that decides viability. Monthly cost per active user is.
User content treated as untrusted
Prompt injection is a real attack on any feature that puts user text into a model.
Measured before launch
An evaluation set on your real cases, so quality is known rather than hoped for after release.
Technologies
Frequently asked questions
Which model should our product use?
Usually more than one. A cheaper, faster model handles the simple majority of requests and a stronger model handles the rest, routed on complexity. That commonly halves cost with no user-visible difference, and it is why we build the model choice as configuration rather than hard-coding it.
Can you add AI to an application we already have?
Yes, and it is often straightforward: AI features tend to sit at the edges rather than requiring architectural change. We start with one feature that removes a step users dislike, measure it, and expand from evidence rather than enthusiasm.
What about our customers' data?
That is a design decision to make explicitly. Options include providers with contractual no-training commitments, regional deployment, redaction before sending, and self-hosted models for genuinely sensitive content. It also needs saying in your privacy policy, which is frequently forgotten.
Add the feature that makes your product less tedious
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Serbia