AI automation
The workflows that eat your week. Invoices read and filed, leads scored and routed, orders chased, reports written before Monday. Exceptions go to a person with the reasoning attached.
We map the work, build the thing, and hand back a system that runs on its own. Automation, chat and voice assistants, or plain custom software when that is what the job actually needs.
Some of it is AI. Some of it is plain software that should have been written years ago. We start with whichever one is costing you the most hours.
The workflows that eat your week. Invoices read and filed, leads scored and routed, orders chased, reports written before Monday. Exceptions go to a person with the reasoning attached.
Trained on your handbook, past tickets, and the way your team actually answers. It cites where every answer came from, so you can check it. Put it on your site, in Teams, or behind your support desk.
A voice that answers on the first ring, day or night. It qualifies the caller, books the appointment, takes the order, and hands off to a person the moment the call needs one.
The app, the API, the integration, the migration off the system nobody wants to touch. A decade of production work in .NET, Python, Azure, and the front end, with or without any AI attached.
Where it usually starts
Invoices, bills of lading, claims, and contracts read, matched, and filed.
Jobs matched to the person, the skill, and the window that fits, reschedules included.
Where is it, did it ship, has it been signed, asked and answered without opening five tabs.
The portal, dashboard, or admin screen your team is currently running on a spreadsheet.
Hours a month handed back on a typical first build.
From kickoff to the first thing running in production.
Of routine tasks clear the accuracy bar we agree on up front.
Software fails when it is bolted on top. We learn the need as well as the business context before proposing any solutions.
A working conversation with the people who do the task, not a questionnaire. We map every handoff, exception, and unwritten rule before proposing anything.
A narrow build against your real data, running beside your team so you can compare its output to theirs before anyone commits.
Deployed in your cloud, using your identity provider and your data retention rules. We hand over the repo and the runbook.
Models drift and processes change. We monitor accuracy, retune, and extend it to the next piece of work when you are ready.
Chaparral Dr AI was started by people who spent a decade building distributed systems in freight, warehouse, and healthcare, where a failed job is not a support ticket, it is a truck sitting idle. We build automation and software the same way we built those platforms: observable, reversible, and boring in production.
Your data stays in your tenant. We deploy into your cloud account, not ours, and you keep the keys.
Every automated decision is logged with its inputs and its reasoning, so an auditor can follow it later.
You own the code. If you end the engagement, the system keeps running and your team can maintain it.
Named for the street where it all started