A generative AI development company that builds features, not demos

The gap between an impressive LLM prototype and a shippable feature is where most generative AI budgets disappear. The prototype works because one person tried five inputs they already knew the answer to. The feature has to work for a thousand strangers, stay within a cost envelope, keep its facts straight, and not embarrass you in a screenshot.

We build the second thing. That means retrieval that cites its sources, prompts held in version control with tests around them, structured outputs your code can rely on, and a fallback for the day the provider has an outage. It is less exciting than the demo, and it is the entire difference between a feature and a liability.

When an LLM feature gives wrong answers, the instinct is to reach for a larger model. It is usually the wrong lever. Most factual failures are retrieval failures: the model never saw the relevant passage, or it saw six near-duplicates and picked the stale one. A better index fixes more than a better model, and it costs a fraction as much to run.

So we spend the effort where it pays: chunking that respects document structure, hybrid search rather than pure vectors, reranking, and freshness handling so superseded content stops surfacing. Then we measure. An accuracy benchmark on your own data is the only honest way to know whether any of it worked.

A generative feature that returns prose is a demo. A generative feature that returns validated, typed data is something the rest of your application can build on. The difference in engineering effort is small; the difference in what you can do with the result is enormous.

We use schema-constrained outputs with validation and a repair path, so a malformed response gets retried rather than crashing a downstream job. It is the least glamorous part of generative AI work and the part that determines whether the feature can be trusted inside a workflow.

We are not tied to a provider. The right model for a step depends on what that step needs: a frontier model for genuinely hard reasoning, a small fast one for classification and routing, and often a cached response for the question forty users asked this morning. Mixing them is where the cost curve bends.

Because we route through a gateway rather than hard-coding a vendor, switching a step to a cheaper model is a configuration change with an eval run behind it, not a rewrite. That matters more than it sounds: this market re-prices every few months, and you should be able to take advantage of that without a project.