AI Integration for Businesses That Need It to Work
Agents, assistants and classifiers wired into your systems, measured on your own cases rather than on a demo.
Most AI projects fail in the same place. The demo answers five questions beautifully, the pilot meets a real inbox, and nobody can say whether the thing is right often enough to be trusted. We build the second part first: the evaluation set, the refusal path, the escalation to a human. The impressive part is easy.
What we actually build
- Assistants grounded in your own documents, which cite their source and say they do not know rather than inventing your refund policy.
- Agents that do work rather than talk about it: reading an inbox, drafting a reply, updating the CRM, escalating what they cannot finish.
- Classifiers and extractors that turn unstructured arrivals — emails, PDFs, voice notes, tickets — into fields your systems can act on.
- Human review where it matters, so the model proposes and a person disposes on anything that touches money or a customer promise.
How the engagement runs
- We take fifty real cases from your business, with the answers you would want. That set becomes the definition of done, and it is yours whether or not you continue with us.
- We build the smallest system that clears it, using the model that fits the task rather than the one in the headlines.
- It runs alongside your team, not instead of it, until the numbers hold on cases nobody has seen.
- We hand over the code, the prompts, the evaluation set and the dashboard that says whether it is still working next quarter.
What it costs
- 2-4 weeks for a scoped assistant on a clean corpus
- 6-10 weeks for an agent wired into live systems
- per case running cost, which we model before you commit
The running cost is the part vendors skip. Usage is billed per call, so success makes it grow, and we show you that curve at ten times today's volume before anyone signs anything.
An assistant that answers eighty percent of questions with a citation beats one that answers everything and has to be checked anyway.
Frequently asked questions
Do we need our own AI model?
Almost never. Training or fine-tuning a model teaches it style and format, not facts about your business, and the facts are usually the problem. Retrieval over your own documents, with a good evaluation set, gets you further for a fraction of the cost and updates the moment you update a document.
Where does our data go?
Wherever you decide, and we write it down before we build. Providers differ on retention and on training rights, some deployments keep everything inside your own cloud account, and the correct answer depends on what the data is. What is not acceptable is finding out afterwards.
How do we know it is working?
Because we agreed on the number before building it. Accuracy on your evaluation set, the share of cases escalated to a human, and the cost per case, all on a dashboard you keep. A system nobody measures degrades quietly as your business changes around it.