AI-native products.
From discovery to production.

The model works, the demo lands, and adoption never arrives—because retrieval was tuned on a curated corpus rather than the real one, the interface hides how confident the system actually is, and there is no way to distinguish a good answer from a merely plausible one at scale. These are product and engineering problems, not model problems.

Intellivetrix builds AI-native products the way we would build any production software, with the additions that non-determinism demands: evaluation datasets written during discovery rather than after launch, interfaces that expose provenance and uncertainty, and telemetry that measures whether an answer was useful rather than whether the request returned a 200.

We work across the whole surface—retrieval and knowledge design, prompt and context engineering, application and API development, and the operational tooling your team needs to run the product once we hand it over.

Quality targets and test sets are defined during discovery, so “is it good enough” becomes a measurement rather than an argument.

Chunking, indexing and graph structure are designed around your actual corpus, its update cadence and its access rules.

Provenance, uncertainty and easy correction do more for adoption than an incremental gain in model accuracy.

Documentation, tests, dashboards and runbooks so your team can operate and extend the product without us.

Non-determinism. The same input can produce different output, quality cannot be asserted with a unit test, and users need to see provenance and uncertainty to trust the result. That changes how you test, how you design the interface and what you monitor in production.

RAG retrieves passages by semantic similarity, which works well when answers live in a single document. GraphRAG additionally models entities and the relationships between them, which matters when a question requires connecting facts across sources. Intellivetrix chooses between them, or combines them, based on the structure of your corpus and the questions being asked.

By building an evaluation baseline on your real data before committing to a build. That establishes achievable quality, latency and cost early, when the answer is still cheap to act on. A use case that cannot clear its quality bar in discovery will not clear it after six months of engineering.

Hand over, by default. Engagements include documentation, tests, evaluation harnesses, dashboards and runbooks so your team can operate and extend the system independently. Ongoing support is available where it is wanted, but the build is not designed to require it.

Yes. AI products generally succeed or fail on the quality and accessibility of existing knowledge, so we work with the data platforms, warehouses, document stores and permission models already in place rather than proposing to replace them.