Build and launch AI features for your product.
We integrate AI features, assistants, document search (RAG) and personalization into your product. We define the intended result, safety limits and budget before development.
What we do
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.
Why us
FAQ
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.
Read also
AI agents for business: which tasks to delegate
AI agents for business can help with workflows that involve interpreting text, choosing a next step and using several systems. Start with one process and define its result, permitted actions and acceptance checks. The scenarios and selection table below help you choose a pilot.
AI implementation: plan your first business pilot
A first pilot should answer a specific question: can the system complete the chosen task at acceptable cost and quality? Document the current workflow and acceptance conditions before development. Those inputs support a decision to launch, revise or stop.
How to build an AI agent: what you need beyond the prompt
The prompt and the model are the smaller part of the job. An agent becomes usable when it has tools, explicit authority boundaries, state, a log and cost control. A step-by-step breakdown of what it is made of, how to get it to production and where it breaks.
What RAG actually is and when a business needs it
RAG means the model answers from your documents rather than from the internet, with a visible source under every claim. What it is made of, where quality is lost in chunking and updates, how to measure that it works and when you do not need it.
Checklist: are your documents ready for RAG
PDF, 2 pages. No sign-up, no email — just the file.