AI in production, not in a demo.
We embed AI into a real product: LLM features, assistants, Retrieval (RAG), personalized flows — with safety and cost control, for a specific measurable outcome. Not hype for the sake of hype.
AI assistants & interfaces
LLM features and assistants embedded into the product so people actually use them, not just open them once.
RAG & data
Retrieval over your data, indexing, answer-quality evaluation and hallucination control.
Safety & cost control
Guardrails, access control, logging and token-cost monitoring — so AI is predictable in production.
Integration into the product
AI is not a separate prototype but part of the product: wired to backend, analytics and real user flows.
Can you embed AI into an existing product?
Yes — that's the common case: we add an LLM feature or assistant to a live product and wire it to your data and flows.
Whose models do you use?
Chosen for the task and data constraints: external LLM providers or self-hosted models, weighing privacy and cost.
How do you control answer quality?
Through evaluation on your data, guardrails, logging and monitoring — so quality and cost are visible in production, not guessed.
What if AI errs in a critical domain?
In sensitive domains the product supports the decision and does not replace the specialist; responsibility and regulation remain with the client.