An AI software development company for firms from $5M in revenue. We design the system, engineer it into the tools you already run, and operate it after launch. The code is yours.
Three things should arrive at the end of a build. A system running in production, the source code and infrastructure it runs on, and a written account of how it reaches every decision it reaches. Most of what is sold as AI product development stops at the first, and the client works out a year later that the thing holding up their operation is a pile of prompts nobody outside the vendor has read.
This page is about the build itself. Deciding what is worth building, and proving it pays before you commit a budget to it, is a separate stage and we run it first on the consultancy side. Once that gate is cleared the question stops being whether to build and becomes how, and how is an engineering question: what the system is allowed to decide on its own, where your data sits, what it does at three in the morning when a supplier's API stops answering.
Bespoke does not mean everything from scratch. Most of a good build is unglamorous and already solved, queues, retries, authentication, logging, a database that will not fall over. The custom part is narrow, usually the domain logic and the way the model is fenced in around it. We spend the budget there and use proven components everywhere else, because a system made entirely of novel parts is a system nobody can maintain.
The design decision that determines whether an AI system survives contact with a real business is which parts of it are the model and which parts are ordinary code. Language models are very good at reading a messy input and working out what it means. They are unreliable at arithmetic, at applying the same rule consistently across a thousand cases, and at saying they do not know. So we use them for the first job and rarely for the others.
The Quoter, live in plumbing, is built that way. It reads an inspection report or a Spanish voice note and works out which jobs are being described. The prices come from the client's own book by lookup, not from the model. The totals are calculated in code with tests around them. Anything that cannot be mapped confidently to a line in the book is flagged to a person with the source attached, instead of being priced by inference.
That split is also what makes a system auditable. When a number comes out wrong you can see whether the reading stage misheard the input or the pricing stage applied the wrong rule, and repair the one that broke. A single large prompt gives you nothing to inspect and nothing to test, which is why systems built that way get rewritten rather than fixed.
Custom AI solutions fail on integration far more often than on intelligence. The model behaves in the demo, then has to read from a job management system holding fifteen years of inconsistent records, write back into an accounting package with a rate limited API, and not break a workflow twenty staff already know by heart. Our AI integration services start from the stack you actually have rather than the one a diagram says you should have.
Data handling is settled before any code is written. What leaves your systems, what is stored and for how long, which providers see what, and what is stripped out before it goes anywhere. On most builds the sensitive fields never reach a model at all, because the model needs the description of the work, not the customer's name or their card details. Where your obligations require it, processing runs in a region you choose.
Instrumentation is part of the build, not a report bolted on afterwards. The Tracking Layer is that discipline taken far enough to become its own project, conversion tracking rebuilt end to end across a financial services funnel so every unit of spend could be traced to the customer it actually produced. Every system we ship carries a smaller version of the same idea, a record of what it did, what it flagged and what a human overrode, so any later argument about whether it is working gets settled with data.
Assume it will be wrong sometimes. That is not an argument against building, it is the specification. Every system we ship has a defined blast radius: what it may do alone, what needs a sign off, and what it must refuse outright. Customer facing output goes past a person by default until the error rate across real volume justifies loosening that, and loosening it is a decision you make on evidence rather than a setting we quietly change.
When it is wrong, the failure should be cheap and visible. A flagged item sitting in a queue with the reason written next to it costs somebody a few minutes. A confident wrong price sent to a customer costs the job and some of the relationship. We design for the first and monitor for the second, with alerting on the patterns that come before it, such as a rising share of low confidence reads after a supplier changes their document format.
On lock-in, the code is yours. It lives in your repository, on your infrastructure, under your accounts and your keys, documented well enough that another engineer can pick it up. Most clients keep us on to operate it, because a system in production needs somebody watching model updates, API deprecations and quiet drift, and that is genuine ongoing work. It should stay your choice each renewal because the work earns it, not because leaving would break something.
We settle the design before the code: what the model decides, what runs deterministically, where your data goes and who is allowed to see it.
We build the narrow working version, run it against your own records rather than a demo set, and score it on the number agreed before the run started.
We engineer the full system into your existing tools, with tests around the logic that matters and a person in front of anything a customer sees.
We run it, review what it flags, patch what drifts as models and APIs change, and hand the whole thing over whenever you want it in house.
Nothing here is sold as a silo. Most engagements start with one of these and pull in the next once the first is paying for itself.
Half an hour with a founder, not a sales rep. We tell you straight whether a machine fixes this, what it would cost, and what it would return.
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