Matthew Carlson AI Solutions Engineer · Forward Deployed Engineer · Madison, WI · working nationally

I build the AI systems that run your marketing operations.

AI agents, voice and chat assistants, CRM automation and reporting pipelines, designed by someone who ran a $40M marketing budget and shipped with tests, verification gates and a known cost per run. I work forward deployed: inside your systems, with your team, until it runs.

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SkillEngine Assistant
Example of the systems I build
Online
Which client accounts have leads waiting more than 24 hours?
4 accounts flagged. Two have unassigned leads from last night's form fills; tasks created for their account managers.
Build the speed-to-lead workflow from the spec.
10 workflows drafted in the test location. Gap report: 2 SMS actions need a compliance-safe number before live testing.
Try the assistant
  • 33AI agents in one production client-intelligence platform
  • 104packaged AI skills in a published library
  • 12–15 hrsof manual ad reporting replaced every week
  • 30 daysconcept to launch, 20+ page enterprise website, solo
  • $40M+annual marketing budget managed over 11 years

What I build

Six ways AI goes from pilot to production

Each one ships with a written specification, a test plan and a cost model. You own everything I build.

All solutions
  1. 01AI agents & workflowsMulti-agent systems, MCP servers and skill libraries that do real work on a schedule or a trigger.
  2. 02Voice & chat AIPhone and chat agents that answer, qualify, book and hand off, tested before a customer hears them.
  3. 03CRM automationSpeed-to-lead, AI-researched onboarding and CRM builds generated from a spec.
  4. 04Reporting & ad-ops automationGoogle Ads scripts, nightly account-health checks and client report packets that build themselves.
  5. 05AI readiness & roadmapsWhere AI fits, what to buy versus build, and the order to do it in. Fractional AI lead available.
  6. 06AI-ready websitesSites built to rank on Google and be cited by ChatGPT, Perplexity and Gemini.

Featured work

AI systems already doing the work

A few of the systems I've built, each marked live, in pilot, tested or delivered.

Websitecrawled pagesReviews & listingsGoogle, Yelp, BBBPublic recordslicenses, WHOISClient Mind26-section profile33 AI agentscontent, ads, SEOMCP serverClaude reads it
Public sources feed one 26-section client profile, which 33 agents and an MCP server read.
LiveAI agents

Multi-agent client intelligence platform

One sourced memory of every client, shared by 33 AI agents and reachable from Claude through an MCP server.

39 client profiles · 8-tool MCP server · ~$32–42/month to run
TestedAI agents · CRM

Agents that build inside the CRM

Claude and OpenAI Codex construct workflows, pipelines and knowledge bases directly in the platform from written specs.

3.5-hour unattended run · 351 custom values · verified at every step
TestedVoice AI

Inbound voice AI agent family

Phone agents that answer, qualify, book showroom visits and transfer with a spoken summary.

11 of 11 first-block checks · 50+ case test suite · 6 service branches
LiveReporting

Account health dashboard & alert bridge

Every client CRM account checked nightly; problems become tasks in the tool account managers already use.

18 health flags · read-only · Monday digest email

 See all projects

How I build

Engineering habits that make AI safe to depend on

  1. 01

    Spec first

    A written specification defines every object before anything is built. The spec and the gap report are deliverables.

  2. 02

    Test before demo

    No client hears a voice agent until a 50+ case test suite and a stop-ship regression pass.

  3. 03

    Read-only by default

    Reporting systems observe and change nothing. Agents that write run in test environments first.

  4. 04

    Stop and notify

    When an input looks wrong, the system halts and tells a person how to restart. It doesn't guess.

  5. 05

    Meter every run

    Token, model and crawl costs are tracked per run, so AI becomes a known unit cost you can price.

  6. 06

    Buy the commodity

    If a tool already does it well, buy it. Build only where the interpretation is your advantage.

Process

From conversation to running system

  1. Discovery

    A working session on the process, the data and what success is worth in hours or revenue.

  2. Specification

    A written spec, test plan and cost model you approve before the build starts.

  3. Build with verification

    Built in a test environment, checked against the spec, with a gap report at each phase.

  4. Launch & hand off

    Pilot, monitor, document and train your team, or keep me on to run and improve it.

Insights

One question, answered properly

Short articles from the work, plus a 20-question checklist that scores whether a process is ready for AI.

Voice AI

How do you keep an AI voice agent from quoting prices?

A written list of what it must never say, a safe action for each, and a test suite before anyone hears it.

Read →
AI agents

What does an AI client research pull cost, and why meter it?

About $4–8 per client, measured. Metering is what makes AI priceable.

Read →
Checklist

Is this process ready for AI?

Twenty yes-or-no questions across process, data, systems, people and risk, scored out of 100.

Take the checklist →

Have a process AI should be running?

Tell me what it is. In 30 minutes we'll know whether it's worth building and roughly what it takes.

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