We help you find where AI genuinely pays off, prove it cheaply, and then build the version that survives contact with production — instead of another pilot that never ships.
You do not have to take all of these, and you should not start with the expensive one. Most engagements begin with the first two.
We look at your processes and data and come back with a short list of places AI would pay for itself, each with a rough cost and a rough return — and, just as usefully, the ones where it would not.
A working prototype against your real data, in weeks not quarters, with a clear answer at the end: this is worth building, or it is not. A negative answer for a small cost is a good outcome.
Bringing language models into your product or your internal tools — retrieval over your own documents, structured extraction, assistants and classification — with evaluation so you know when it regresses.
Data pipelines, training and inference infrastructure, monitoring and cost control, so the model keeps working after the people who built it move on.
Document understanding, data entry, triage and routing — the unglamorous work where AI reliably pays for itself faster than anything customer-facing.
Practical sessions for your engineers and your business teams on what these tools do well, what they do badly, and how to tell the difference.
HRxAI is ours: resume screening and ranking, interview scheduling and hiring analytics, built on Google's Gemini models. We run the same problems you have — cost, evaluation, hallucination, drift.
Our engineers build with Codex and Claude every day, under review. Our AI-powered SDLC page explains exactly how, including the parts we do not let it do.
Plenty of problems are better solved with a database query, a rule or a better form. Saying so costs us a project and saves you a budget.
Business-tier accounts on plans that do not train on your content, secrets never in prompts, and the option to run without AI assistance entirely. See Security & Compliance.
Where does the time and money actually go, and which of those places has usable data?
The two or three candidates worth trying, each with an estimated cost and return.
Build the most promising one against real data and measure it honestly.
Integration, evaluation, monitoring, cost controls and the boring reliability work.
Your team takes it on with documentation and training, or we keep running it. Your call.
With the assessment. It is deliberately short and cheap, and its job is partly to rule things out. Most companies have one or two good candidates and several bad ones that sound exciting.
Often the honest answer is not yet, and knowing that early is worth a lot. Where data is the blocker we will say so and scope the data work separately rather than build on sand.
No. We use business and enterprise accounts on plans where the provider does not train on the content we send, and secrets and personal data never go into a prompt.
Yes. We also supply data engineers directly — see our current openings for the kind of work our people do on enterprise data platforms.
It depends on the problem, but the point of a PoC is that it is small enough to be worth risking. We scope and quote it after the free consultation, before anything starts.
A first conversation is free and there is no commitment. Tell us what you are trying to do and we will tell you honestly whether we are the right people for it.
Contact usHow we protect your code, data and systems — NDAs, IP ownership and access control.
Security & ComplianceThe first conversation is free, and the assessment that follows is designed to rule things out as much as to rule them in.
