AI transformation, one useful job at a time
Put AI to work alongside the people who know the business.
Your employees already know the work. I work with them to find where AI can help, build what the job requires, and test it in the real conditions of the business. Sometimes that means an agent. Sometimes it means software, analysis, or a better use of the systems you already have.
What I do
Build around the job, the people, and the evidence.
AI by itself is rarely the finished answer. I help decide which part of the work it should take on, build the surrounding software and rules, and test the combination with the employees who will know whether it is right.
01
Learn the work with the team
Read the files, trace the workflow, inspect the data, and listen for the exceptions that only appear when someone actually does the job.
02
Give AI a useful part of it
Choose a bounded responsibility, connect the evidence and tools, and return uncertain cases to the person equipped to decide them.
03
Make the whole system dependable
If people rely on the result, permissions, tests, monitoring, exceptions, data integrity, documentation, and maintenance are part of the work.
The work
AI becomes useful in a particular job.
These are not demonstrations built around ideal data. Each began with employees who knew the work, an important problem, and a chance to discover what AI could actually do inside the business.
They replaced a field app employees disliked with software built around the way the team works.
Account managers document a property once, even with weak cell service. The same standardized record is translated, routed into field tickets, reviewed by supervisors, and turned into client reporting.
One company-specific system replaced the old app and the manual handoffs around it.
Read the landscape operations case study →Hours of manual reconciliation became a repeatable monthly process.
AI helped us turn repeated sorting, checking, and spreadsheet preparation into a process finance can run each month. The partnership now spans leadership reporting, business investigations, and new accounts payable work.
Working with the CEO, CFO, and employees across departments to find the next useful application of AI.
Read the finance-operations case study →They replaced manual rebate entry with one review-and-generate workflow.
A Housecall Pro job becomes a reviewed union submission and one double-sided PDF, ready for the office to print, sign, and mail.
The software handles the repeated data entry, portal work, and document assembly. The employee reviews the facts and controls the final physical submission.
Read the HVAC rebate case study →Where it starts
Bring the hunch. Bring the people who know the work.
Clients rarely arrive asking for an AI adoption program. Usually an executive sees a piece of important work that is slow, manual, difficult to understand, or newly possible. The employees doing that work already know things the software and source data cannot explain on their own.
I work with those people to understand the job and make something tangible quickly. Most engagements involve some custom software: an analysis, reporting pipeline, processing tool, internal interface, carefully bounded agent, or a combination of them.
A design rule
Employees should not have to work for the AI.
A useful system should fit the operation and rely, wherever possible, on information the business already produces. If it only works after dozens or hundreds of people adopt a new behavior perfectly, the first job is to redesign the system. Good existing products stay in place. Botworks builds the company-specific software, connections, and intelligence around them.
The employee is not the fallback when AI fails. The employee is part of what makes the system useful.
Who this works for
Authority helps. Access and a useful problem matter more.
I usually work with an owner, CEO, CFO, COO, CMO, senior operator, or private-equity operating partner who can open the right doors and let the work move. The title is less important than the ability to involve the people and systems that hold the real answer.
Good fit
A good prospect has an itch: important work is slower, more manual, less visible, or less capable than it should be. They suspect AI could change it and are willing to let their team help test that belief against the real business.
Probably not a fit
This is a poor fit when a good existing product already solves the material problem, the work cannot reach the relevant people or evidence, or nobody can test the result in practice.
No artificial lock-in
The work belongs to the company. Someone still has to own it.
Engagements commonly run as a monthly retainer because production systems need an accountable operator. While we work together, Botworks fills that role. The client owns what we build, but that does not mean every employee becomes a developer or that agents can safely run production without direction. A future handoff requires a named person or partner prepared to take responsibility.
- 01Client-specific source code and repository history
- 02Operational data, definitions, rules, and tests
- 03Infrastructure, deployment, and monitoring paths
- 04Documentation and a deliberate path to the next responsible operator
Who is Botworks?
Hi, I’m Matt.
I spent 15 years in software product management, most recently as a VP of Product Management. I was used to having engineers, designers, analysts, and product people around the table. Most operating companies do not. It would be a strange use of their budget to recreate that whole department.
AI lets me carry much more of the investigation, analysis, product, and engineering work myself. But the client’s employees supply the operating knowledge and judgment that make any result worth using. I care about getting to the truth, shipping something people can depend on, and keeping the work understandable enough to transfer when another responsible operator is ready.
What could AI help your people do differently?
Send me the rough version of the problem. An initial idea is enough; you do not need a specification, a budget category, or the right technical language.