Generative AI Development

The reliable wins today: assistants that answer from your own knowledge, content production at scale with human review, automation of repetitive text-heavy work and better customer self-service. We scope from your operation, and we will say plainly when a use case is not ready to pay back.

By payback: hours saved or revenue created against the cost to build and run it. The first project should be small enough to ship in weeks and valuable enough to prove the case, which is exactly how we scope it.

Retrieval-augmented generation grounds an AI model in your own documents and data before it answers, so responses come from your knowledge with sources rather than the model’s guesswork. It is the difference between an assistant you can deploy to customers and one you cannot trust.

The ones that fit the job: frontier models where quality decides the outcome, smaller or open models where cost and speed matter more. We build so the model is swappable, because the leaders change quarterly and your product should not be married to one.

Deliberately: your data stays in your infrastructure or contractually protected environments, retention and training rights are locked down in the model agreements, and access is scoped per system. Compliance requirements are design inputs from day one, not a retrofit.

A focused first build, one assistant, one workflow, one integration, is typically weeks of work, not months. Cost scales with integrations and data complexity, and every proposal ties the price to the hours or revenue the system is expected to return.

GEO is making your brand the answer inside AI assistants: when customers ask ChatGPT or Google’s AI Overviews for recommendations, cited brands take the demand. It runs on structured content, verifiable authority and machine-readable proof, and we build it alongside the AI work itself.