AI automation services for regulated industries
I build four kinds of internal AI tools for businesses in regulated niches. Lead triage. Document processing. Internal team tooling. Compliance-aware workflows.
Every build starts the same way. I sit down with you on a 30 minute call, ask what is eating the most hours in your week, and quote a build that ships in two to six weeks. Pricing is gated to a call so I can scope it to your situation, not a generic package. Most clients start with a single build, see the time and money it returns, then add a second.
Four categories. Most engagements start with one and grow.
Lead triage and intake automation
The inbound problem. You are running ads. Forms are filling out. Your team is calling everyone or nobody and the gap between the two is where money leaks. I build a triage layer that reads every inbound, applies the criteria you actually use to decide who is worth a call, and routes the qualified ones to your CRM with the context your team needs to open the conversation.
Common starting point. Two to four weeks. Pays for itself fast.
Document processing
Intake forms. Records. Contracts. PDFs that get retyped into a system. I build the pipe that turns a scanned document into structured data your team can act on, with a human review step where the stakes require one. Compliance-aware from the first version, not bolted on after.
Internal team tooling
The operator layer. Reporting your media buyer needs by Tuesday morning. Briefs your writers actually use. Dashboards that show the few numbers your team should care about and hide the noise. Built around your workflow, not an off-the-shelf SaaS that almost fits.
Compliance-aware workflows
The differentiator. AI that knows when to stop and ask a human. Audit trails that pass a regulator's read. Approval gates where the rules say there have to be approval gates. This is not a layer on top of a build. This is how the build is designed from the first commit.
Three verticals.
Health
Clinics, advocacy organizations, patient-facing brands.
Legal
Personal injury, mass tort, defense firms.
Medical-legal
The overlap. Mass tort, pharma litigation, medical malpractice.
Vertical-specific pages live at /services/healthcare, /services/legal, and /services/medical-legal (rolling out June through July). For now, anchor links above scroll to short descriptions below.
Three steps. Same every time.
Discovery call
30 minutes. You tell me what is eating your week. I tell you whether AI can fix it and whether I am the right person to build it.
Scope and quote
I write a one-page scope of what I am building, what it will do, and what it will cost. You sign off before I open an editor.
Build and ship
Two to six weeks for a first build. I show you progress weekly. You get the working tool, the docs, and the access to the underlying systems. No black boxes.
I am up front about scope so we both save time.
- No customer-facing chatbots. If your build replaces a human conversation with a bot, I am not your person.
- No hype features. "AI-powered" anything that does not save hours or close cases is a no.
- No outsourced builds. I write the code. I review every line a model writes for me. You are not paying for an offshore team behind a brand.
- No work outside health, legal, or medical-legal. Other niches get a polite no and a referral.
Common questions.
What does an AI automation consultant do?
An AI automation consultant builds internal tools that use AI models to remove repetitive work from a team's day. The good ones scope the build to a specific business problem, ship working software, and stay involved long enough to fix the things that break in production. The bad ones sell strategy decks and never ship code. I am the first kind.
How is AI for regulated industries different?
Regulated niches add three constraints most AI builds ignore. Patient or client data needs to stay inside systems that respect HIPAA, attorney-client privilege, or both. Audit trails need to survive a regulator's review. Some decisions cannot legally be made by a model alone, so the workflow has to keep humans in the loop where the rules require it. A regulated build is the same toolset as any other AI build, designed around those three constraints from day one.
What does matt clarke / ai build that a generic AI agency does not?
Three things. First, every build is operator-tested in my own business before I sell it to you. Second, I scope to your actual workflow, not a generic package, which is why pricing is gated to a call. Third, compliance-aware design is built in from the first version, not bolted on after. A generic agency wins on logo and pricing pages. I win on the actual work.
How is a custom AI build different from off-the-shelf SaaS?
Off-the-shelf SaaS is faster and cheaper for problems that are 80 percent generic. The moment your workflow is the differentiator, off-the-shelf fights you. A custom build wraps the same model APIs the SaaS uses and shapes them to your exact process. You own it. You change it when your process changes. You do not wait on a vendor's roadmap. The right answer for any specific tool is a comparison conversation, which is exactly what the discovery call is for.
What does a typical engagement look like?
A first build runs two to six weeks from kickoff to shipped tool. I deliver weekly progress, a working version at the end, documentation, and access to the underlying systems. After ship, most clients move to a monthly retainer for maintenance and the next build. Pricing is gated to the discovery call so I can quote the actual scope, not a generic package.
Want a build scoped for your practice?
30 minutes. No pitch deck. Tell me what you are trying to fix and I will tell you if I can help.