Most AI projects end their life as a demo that impressed someone once. Keyrex finds the repetitive work quietly eating your team’s week, builds the thing that takes it over, and then makes sure it keeps running.
A real engineer reads every message. You will usually hear back inside a day.
An open challenge
Six practices, one team, one contract. Most projects start with a single use case and grow into the platform underneath it.
Plenty of AI ideas are not worth building. We look at the data you actually hold, work out which ones pay for themselves, and hand back a ranked list with prices on it. Including the ones we think you should drop.
Assistants and agents that do real things to real systems. Tool calling, guardrails, a human in the loop where it counts, and a sensible plan for the day the model says something strange. Because it will.
Search across your contracts, tickets, claims and the wiki nobody has opened since 2019. Chunking, hybrid search and reranking tuned on your own documents, with citations so people can check the machine.
Sometimes a language model is the wrong tool and a smaller one wins. Forecasting, classification, extraction, recommendations, vision. Trained on your data and measured against whatever you run today.
Evals, prompt and model versioning, tracing, drift alerts and a spend cap per customer. Nobody has ever demoed this layer. It is most of the reason a pilot turns into a product.
The software wrapped around the model. Multi-tenancy, billing, roles, audit trails, admin tools. So the clever part ships inside something a customer can actually buy.
Each one ends with something you can look at and judge for yourself. You always know what got built, what it cost, and what happens next.
One or two weeks on the problem, the data and the fastest honest route to something real. You leave with a written scope, a price and our read on whether it will work. You keep all three even if you walk away.
A narrow prototype against your actual data, scored on a test set we build with you. If the numbers do not stack up we say so early, while stopping is still cheap.
Two week cycles, a live staging link from day one, a demo at the end of every cycle and a written update every Friday. Nothing gets revealed at the end because nothing was hidden.
Evals, tracing, drift alerts and cost caps go in before launch. Then a proper handover, or we stay on as your AI team. Your call, and never written into the contract.
Keyrex is a studio and a product company at the same time. The people building your system also operate AI in production, pay the inference bill every month and get woken up when it breaks at 3am. That tends to produce very conservative architecture.
Most clients start with one and move between them as the product grows up.
A defined outcome, fixed scope, fixed timeline.
Best when you already know what you want built.
A senior squad working inside your team, billed monthly.
Best when the roadmap keeps moving.
Architecture, due diligence and vendor calls on retainer.
Best when you need judgment more than hands.
These are in every contract we sign. They are also most of the reason clients come back for the second project.
No quiet handover to a junior bench once you sign. You meet your engineers before you commit and they stay on the work.
Every prompt, model and retrieval change is scored against a test set you can open and read yourself. If we cannot show it got better, it does not ship.
Source, fine tunes, prompts, eval sets, infrastructure and documentation. All of it in your accounts from day one. Leaving us should be a conversation, not a migration project.
If a use case does not clear the bar, or forty lines of ordinary code would beat a model, we say so during discovery. Before it becomes a line in your budget.
Discovery usually begins within a few days. Full delivery teams start one to two weeks after a signed scope, once we have matched the right engineers to the work rather than whoever happens to be free that month.
Project builds are quoted as a fixed price or against milestones once discovery is done. Dedicated teams are a monthly rate per engineer. Either way you get a written estimate with the assumptions spelled out, so you can argue with them before you sign anything.
No, and it almost never is. Working out the state of your data is part of discovery. The roadmap we hand back prices any cleanup, labelling or pipeline work the use case actually needs, listed separately so you can see what you are paying for.
Whichever ones clear your bar on quality, latency, cost and data residency. Frontier APIs, open weights you host yourself, or plain old machine learning when a language model is overkill. We keep the model layer swappable, so switching later is a config change rather than a rewrite.
You do, completely, from the first commit. That includes fine tuned weights, prompts and evaluation sets. We work in your repositories and cloud accounts wherever possible, and everything transfers unconditionally where it is not.
Yes, and it is a large part of what we do. Usually it is a pilot that wowed a boardroom last year and has not moved since. We start with a short paid audit and tell you honestly what is worth keeping, what should be replaced, and what each option costs.
A shared Slack or Teams channel, agreed overlap hours, a written update every week and a live demo every cycle. You should never have to email us asking how it is going.
A few lines about the problem is plenty to start. You will get a considered reply from an engineer, not the first of five automated nudges.
Or write to us directly
farrukh@keyrextechnologies.com