AI & Implementation

AI implementation is building practical AI into your product and workflows, LLM features, RAG over your own data, and automation, where it measurably helps, with the evals, guardrails and cost controls that keep output reliable in production. It's the engineering that turns a demo into something users can trust.

Teams with a real use case (support, search, document processing, drafting) who want AI integrated into their product rather than a one-off demo, and who want an honest read on whether AI even beats the simpler option before they invest.

A working proof of concept on your real data is usually 2 to 4 weeks; production hardening, evals, guardrails and integration, follows from there. We start from the business problem, not the model, so the first step is deciding whether to build at all.

LLM or RAG features wired into your stack, with evaluation suites, guardrails against prompt injection, cost controls and monitoring, plus an honest recommendation when AI isn't the right tool for the job.

We start with APIs because they ship fastest with zero infrastructure, and only fine-tune or self-host a model when proprietary data, privacy requirements or call volume genuinely justify it. We cross that threshold with numbers, not hype.