Custom AI development, for problems that do not fit a template
Off the shelf AI is right when your problem is ordinary. It is wrong when the value is in something specific to you: your documents and who may see them, your workflow and where a decision has to land, your systems of record and what may write to them, your accuracy bar and the cost of getting it wrong. Custom AI development is the engineering of that specific thing. We do it the way we would want it done to us: a written plan before a commitment, senior people on the work, the system in your accounts, and an evaluation set so you can change it later without us.
Who this is for
- - A business with a process that a general tool nearly handles, where the last mile, the part that touches your own systems, is where all the value sits.
- - A software company that needs an AI capability as part of its own product, built to its architecture and quality bar, not bolted on.
- - An enterprise with data, compliance or integration constraints that rule out hosted AI products.
- - A company that has outgrown a no code or platform solution and needs to own the next version.
What you get
- - A two week discovery that produces the architecture, the plan, the cost and the timeline in writing, which you own whether or not you continue.
- - The system designed and built by the same senior engineers, with the founder on architecture decisions.
- - Integration with your systems of record on least privilege access, inheriting user permissions where they exist.
- - An evaluation set, cost and latency budgets, and observability, so the system is measured rather than trusted.
- - Code, prompts, data, configuration and documentation in your accounts, with a handover to whoever maintains it next.
What it costs
There is no honest fixed price before discovery, and anyone who gives you one is guessing. What we can say in writing after two weeks: the build cost, the running cost per month at your expected volume, and what moves each. The build is driven by how many systems the work must touch and how strict the accuracy, latency and compliance bars are. The running cost is model calls plus infrastructure, and it can differ by ten times depending on the choices made in week one. How we price AI work.
How long it takes
Discovery, two weeks: we map the problem, the systems involved and the success criteria, and put the numbers in writing. Pilot, four to six weeks: one capability, live, on a slice of real traffic, measured against those criteria. Then production hardening and scale out. Each stage is a separate agreement with no minimum commitment, so you can stop after discovery with a plan you own. How we work.
What you own afterwards
Everything. The code, the prompts, the evaluation set, the infrastructure configuration and the documentation, in your accounts, under your name. We do not hold anything hostage and we do not license our own platform to you. The evaluation set matters most: it is the thing that lets your team, or any other team, change the system later without breaking it. The deliverables clause.
Custom, in production
A US automotive SaaS product needed an assistant that understood intent, identified leads, searched 120 GB of documents with per user authorization, and answered fast. No product did that. We built it: intent routing before retrieval, authorization inside retrieval, caching and targeted context, on the client’s cloud. First token in under three seconds in typical interactions, about 98 percent accuracy in the evaluated use cases, operating cost down roughly 30 percent.
Read the caseQuestions buyers ask
When do you need a custom AI development company rather than a product?
When the value is in something specific to you that a product cannot reach: your documents and their permissions, your workflow and where decisions land, your systems of record and what may write to them, or an accuracy and compliance bar a general tool cannot meet. If a product nearly handles it, the last mile is usually where the custom work is.
How does a custom AI project start?
With two weeks of paid discovery that produces the architecture, the plan, the cost and the timeline in writing. You own that document whether or not you continue with us. It is the only honest way to give a number for custom work, and it is how you avoid paying for a build that was scoped wrong.
Who owns a custom AI system after it is built?
You do, entirely: code, prompts, evaluation set, infrastructure configuration and documentation, in your accounts under your name. The evaluation set matters most, because it is what lets your team or any other team change the system later without breaking it.
How much does custom AI software development cost?
Scope decides it: how many systems the work must touch, how many of the actions are irreversible, and how strict the accuracy, latency and compliance bars are. The running cost is model calls plus infrastructure and can differ by ten times depending on early choices. Both go in writing after discovery, before you commit to a build.
Before you decide
If you want the technical background first, read what we build across AI. Then, from our engineers:
Tell us the problem in your own words
Two weeks of discovery puts the cost, the timeline and the plan in writing before you commit to anything. Or start with the free audit.