Custom AI Agent Development
We build AI agents for businesses whose real work does not fit an off-the-shelf product: insurance carriers and agencies, credit unions and banks, manufacturers, and collegiate athletic programs. Built into the workflow your team already uses, tested against your own data, and yours to keep.
Most things called an AI agent should not be one
An AI agent decides what to do next. It reads a situation, picks among the tools it has, takes an action, checks the result, and keeps going until the job is done. That is genuinely useful when the path varies from case to case: every claim is a little different, every invoice arrives in a different format, every recruit needs a different follow-up.
When the path is the same every time, an agent is the expensive answer. A plain automation runs faster, costs a fraction as much, and does not surprise you. We start every build by asking which one you actually need, and a good share of the time the answer is the simpler thing.
That is not modesty. Agents that were built where a workflow would have done are the most common way an AI project quietly becomes a maintenance problem eighteen months later.
Agents we have built, by industry
Every one of these started as somebody describing a job that ate their team's week.
Insurance carriers and agencies
Reading policy declaration pages into structured coverage analysis, spotting gaps across home, auto, umbrella, and life, and preparing the agent's meeting materials. One live build compressed roughly an hour and a half of per-prospect preparation into minutes, across a multi-state coverage engine.
AI for insurance →Credit unions and banks
Member-facing and internal operational workflows in a regulated environment: request triage, document handling, and the analyst work that eats the day. Built so your compliance people can see what the system did and why.
AI for credit unions and banks →Manufacturers
Invoice generation, accounts payable document reading, accounting workflow, and capturing operational knowledge before the people holding it retire. Four concurrent builds at one Wisconsin commercial printer.
AI for manufacturers →Collegiate athletics
Our deepest vertical. Recruiting operations, athlete analytics, recovery planning, and the staff work around compliance-adjacent workflows. Serving collegiate and professional programs, including an NCAA national championship team.
AI for collegiate athletics →Finance and accounting teams
Accounts payable, invoice matching, reconciliation, and the reporting somebody rebuilds by hand every month. Usually the fastest place to prove an AI project pays, because the hours are already counted.
AI for accounts payable →Operations and back office
Intake, routing, packet preparation, quality control on outbound work, and the meeting commitments that get made and then lost. The unglamorous middle of a business, which is where most of the recoverable time actually sits.
AI for operations →Named clients are described by category rather than by name unless they have said otherwise.
Four steps, weeks not months
The point of the order below is that something real gets in front of your people early, while changing it is still cheap.
Scope the workflow
We watch the job get done and write down what actually happens, including the exceptions people handle without thinking about them. The exceptions are usually where the difficulty lives.
Prove the hard part
Whatever is riskiest gets built first against your real data, not a demo set. If the thing cannot read your documents accurately, everyone should find that out in week one.
Build it in
Into the systems your team already opens. An agent that lives in a separate tool nobody remembers to check is a project that quietly stops being used.
Hand over the keys
Code, documentation, and a way to tell whether it is still working. It runs in your accounts. Your team can change it without calling us.
The answers, up front
These come up in nearly every technical review, so here they are before the meeting.
| The question | The answer |
|---|---|
| Where does it run? | In your cloud accounts wherever practical. Your IT team owns the infrastructure, the keys, and the off switch. |
| Does our data train the model? | No. We build on commercial providers under agreements where customer content is not used to train the underlying models. |
| Are we locked into one AI vendor? | No. The model sits behind an interface and can be swapped. That is a deliberate design choice, not a happy accident. |
| Is this a new trust boundary? | Usually not. Most clients already run on the cloud and model providers involved, so the agent inherits an approval that already exists rather than asking for a new one. |
| Who supports it at 3am? | Your team owns it, and we scope the handover so that is realistic: documentation, monitoring, and a named person on our side during the transition. |
| Is it just a wrapper? | The model is one component. The work is in reading your documents correctly, connecting to your systems, handling the exceptions, and proving accuracy against your data. |
We are happy to join a call with your IT, security, or infrastructure group directly, and we would rather do that early than late.
Before you pick anyone
What does a custom AI agent cost?
We price by scope, not by hour, and quote the fee before the work starts. One well-defined agent on one workflow is a much smaller engagement than a set of agents across a department. The fastest way to a real number is a short call where you describe the workflow, the volume, and where the data lives.
Do we own it?
Yes. You own the code we write for you. No per-seat license on custom work, and no dependency on us to keep it running.
Should we build this or buy a product?
Buy when your process looks like everyone else's and a mature product already covers it. Build when the thing you do differently is the thing that needs automating, when your data lives in systems no vendor integrates with, or when the per-seat cost over three years exceeds building it outright. We will tell you when buying is the better answer, and we have.
How to decide, in detail →How long until we see something working?
Weeks, not months, for a first working version on a scoped workflow. We put something real in front of the people who do the job early, because the fastest way to find out an agent is wrong about the work is to let them use it.
We do not know what to build yet. Where do we start?
With an assessment rather than a build. Three weeks, fixed fee, and you come out with a scored list of what is worth doing and a recommendation on what to do first.
AI opportunity assessment →Are you a Wisconsin firm or do you work nationally?
Both. We are based in Madison, a good share of our clients are Wisconsin organizations, and we build for clients across the country.
- Featured by Apple in Best New Apps & Updates (LilSense)
- Serving collegiate and professional programs, including an NCAA national championship team
- Founded Wisconsin's longest-running AI practitioner community (meeting weekly since 2024)
- Co-hosts the AI Leadership Breakfast Forum with Steve Cretney (EVP, Colony Brands)
Tell us about the workflow
Thirty minutes. Describe the job that eats your team's week, and we will tell you whether it wants an agent, a simpler automation, or a product you should just buy.
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