Custom AI development for businesses past $10M.

AI systems built around your data, your workflows, and your margins, instead of a generic tool your competitors can buy tomorrow. Scoped against a business case and priced before a line of code is written.

01why now

Off-the-shelf AI tools have made the easy wins cheap and universal. A chatbot on the website, a meeting summarizer, a writing assistant: every company in your category can buy the same ones this afternoon, which means none of them is an advantage. The value that is left sits where a generic tool cannot reach: inside your own records, your own process quirks, and the decisions only your business makes.

At the same time, the cost of building has dropped. A small senior team using AI tooling through the whole engineering process now covers ground that used to take a room of engineers. On Pinned Golf that approach reduced the engineering need from five engineers to one. Custom used to mean slow and expensive. For a business past $10M a year it now often means the cheaper option over a three year horizon, because you stop paying per seat for software that only half fits.

02what we build

• AI features inside products you already sell. The model becomes part of what the customer pays for. Supreme Dental's apps carry an AI smile preview on generative imaging and an LLM assistant, the front door of the patient experience rather than a bolt-on. Read the case study.
• Retrieval over your own knowledge. Contracts, SOPs, tickets, product specs, call notes: made searchable and answerable, with citations back to the source, so answers come from your business and not from a model's imagination.
• Decision support. Pricing, triage, routing, underwriting style judgments where a model drafts the call and a person makes it, with the reasoning logged.
• Agents and automations. When the work is a sequence of steps across systems, we build AI agents or workflow automation, whichever is the simpler thing that works.
• Vision and imaging. Computer vision and generative imaging where the input is a photo, a scan, or a document image rather than text.

build or buy: the test we apply first

Custom is not always the answer, and the first thing we will tell you is when it is not. We recommend buying when the workflow is generic, the vendor already integrates with your systems, and the tool does not touch what differentiates you. We recommend building when at least two of these are true:

• The value depends on data only you have, and you do not want that data training or living inside someone else's product.
• The workflow is specific enough that a generic tool forces your team to work around it.
• Per-seat pricing across your headcount costs more over three years than owning the system.
• The output reaches customers, so quality, tone, and liability have to be yours to control.

03who this is for

Operators and owners of companies doing roughly $10M or more a year who already know where the friction is and want it engineered away, not workshopped. Typical sponsors are a CEO, COO, or head of operations who owns a P&L line and can say what a week of saved work is worth. If you are earlier than that, our prototype to production work or a fractional AI officer engagement is usually a better first step. If you are a mid-sized company weighing firms, see AI consulting for mid-sized companies.

It is not a fit if the goal is a demo for a board slide, or if nobody on your side can give the team access to real data and real users.

04how it runs

1. Diagnose. We start with the P&L, not the tech stack: where a model measurably earns or saves money, what that is worth, and what data it needs. You get a written opportunity map, ranked by return.
2. Fix the budget. One scoped plan with a fixed number you can take to the board. Change requests are priced before they are started, never after.
3. Build in the open. Working software in your hands every two weeks. Our engineers and your operators review it together, so scope conversations end in decisions instead of tickets.
4. Evaluate before launch. A test set drawn from your real cases measures accuracy before anything reaches a customer, and guardrails hold it there afterward.
5. Operate and compound. We stay past launch, measuring what shipped, hardening what works, and retiring what does not earn its keep.

05common questions about custom AI development

What is custom AI development?

Building AI systems around a specific company's data, workflows, and customers instead of adopting a generic tool. That can mean retrieval over internal knowledge, AI features inside a product, decision support, agents, or computer vision, all wired into the systems the business already runs.

When should a company build custom AI instead of buying a tool?

Build when the value depends on data only you have, when the workflow is specific enough that a generic tool forces workarounds, when per-seat pricing across your headcount costs more over three years than owning the system, or when the output reaches customers and quality has to be yours to control. If none of those are true, buy.

How much does custom AI development cost?

A fixed number agreed before work starts, set by the opportunity audit rather than a rate card. Change requests are priced before they are started, never after. Nestwell's $30K budget is a public reference point for what a focused zero to one build can look like.

How long does a custom AI project take?

Scope is fixed up front, so the timeline is set when the budget is. Working software lands in your hands every two weeks from the first sprint, so you see progress on real data early instead of waiting for a big reveal.

Who owns the code and the data?

That is settled in writing before work starts, alongside the budget. The scoped plan spells out who holds the code, where your data lives, and which vendors touch it, so there is no ambiguity after launch.

Which models and technologies do you use?

LLM agents, retrieval-augmented generation, generative imaging, and computer vision, using whichever model provider fits the job, security requirements, and cost. The choice follows the business case, not the trend.

Why focus on companies doing $10M or more a year?

Because at that size a few hours saved per person per week, or a few points of conversion, is worth enough to pay back a custom build quickly, and there is enough real data and process to build on. Smaller companies are often better served by off-the-shelf tools first.