Law
Years of documents, put in order.
Emails, contracts and other records became a draft chronology with facts linked to the original document and page.
A first draft in about 35 minutes instead of about six hours, ready for attorney review.
The records that never quite agree. The task that eats up an afternoon. The business question your reports don’t answer. You don’t need an AI project in mind.

A few examples of where we’ve put AI to work.
Law
Emails, contracts and other records became a draft chronology with facts linked to the original document and page.
A first draft in about 35 minutes instead of about six hours, ready for attorney review.
Med spa
We connected an AI receptionist to the spa’s scheduling and service information. It answered questions and booked eligible appointments, passing clinical questions to staff.
About 60 completed visits a month came from calls previously missed.
Construction
Project emails and field reports were matched against change orders and job-cost records.
The contractor reviewed the findings, got the extra work approved and billed, and collected about $148,000 that had gone unbilled.
Property management
Meter readings and sensors flagged unusual water use. AI matched alerts with maintenance history and prepared work orders.
Seven leaks were confirmed during a six-month pilot.
Nonprofits
AI read funding requirements, drafted sections from verified organizational information and assembled document checklists. Staff reviewed every submission.
A six-person team completed 18 applications in a year instead of seven.
Churches
Alongside standardized systems and monitoring, an AI assistant used approved documentation to guide volunteers through common technical problems.
Routine fixes fell from about 25 minutes to eight during a six-month pilot.
Three offices. Two years of decline. The owner was considering closing two locations. We looked at what was actually going wrong.
We pulled data from their own systems and used AI to connect information that would have taken months to piece together by hand, if it could have been done at all. The answers were hidden in those records. Bringing them together revealed where the business was failing and what needed to change.
The question
The business was no longer generating enough to cover what it paid its owners. The reports made closure look like the obvious way to cut costs.
The real questionWhat would closing save, and what would it lose? We examined the underlying financial and operating records, then tested the analysis against how the business actually worked.
What we found
An accounting convention distorted the location comparison. Closing offices would save less than the reports suggested. Meanwhile, opportunities were being lost before the team even spoke with a prospective client.
What needed fixingLost inquiries. Confusing contact details. Unused capacity. The business had useful work to recover and room to deliver it with people it was already paying.
What changed
We helped repair the contact and booking problems, clarify the offer, measure returning-client outreach, and test better appointment access and referral relationships. Then we kept measuring and refining the work.
Twelve months laterThe offices stayed open. Revenue had grown, and the business had money left after paying its owners, from its existing locations.
We take no vendor commissions. We start with the tools you already have, check what your licenses include, and recommend what fits. We show you what is possible, talk through what matters to your business, and begin with the improvement that would make the biggest difference.
For our ongoing clients, AI advice and implementation are generally part of the relationship. We usually build the improvement, help your people use it, and keep looking for what could work better.
How we charge
Studying AI and experimenting independently since the 1980s.
We put different AI models to work checking one another’s reasoning, evidence and conclusions. They look for errors, unsupported claims and invented facts. We work through the disagreements against the underlying records until we have high confidence in the result. Agreement between models is a checkpoint; the evidence still has to hold up.
We have the models collaborate to refine the answer, connect useful findings and develop a more complete result. We direct the process, bring your knowledge of the business into the review, and turn that work into something your business can use.
We build in checks and limits that protect your systems, data, and customers or patients. They control what the AI can see, what it can change, and when it must stop and ask.
I was introduced to BDunn & Company when looking for a new IT option; they were referred to as "computer wizards". They've lived up to that description.
Every review on Google: five stars.
Billy reads these himself. You’re welcome to ask while you’re still exploring, including before involving the owner.
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