Questions about working with me

An AI Solutions Engineer designs, builds and runs AI systems that do real work inside a business: agents, voice and chat assistants, automations and reporting pipelines. The job covers the whole path from a business problem to a tested system in production, not only the model or the prompt.

In my practice that means writing the specification, building in a test environment, verifying the result, and launching with monitoring and a known cost per run.

A Forward Deployed Engineer works inside the client's business rather than from the outside. Instead of handing over recommendations or a product and leaving, I embed with your team, work in your actual systems and data, build the solution in your environment, and iterate with the people who will run it. Deliverables are working systems, not slide decks.

It's the way I work with my agency clients today: building inside their CRM, reviewing results with the account managers each day, and handing off documentation and tests so the team owns what was built.

Mostly marketing agencies, in-house marketing and revenue-operations teams, and professional-services firms. Agencies are a natural fit because I've built AI systems that run across an entire client portfolio.

An AI Readiness Sprint takes two weeks. Most single-system builds take two to six weeks from approved spec to handoff. Some move faster: a nightly account-health dashboard went live in about a week, and a 20+ page website launched in 30 days.

Sprints and builds are fixed-scope and fixed-price after discovery. Ongoing work is a monthly retainer. For systems with running costs, such as voice agents, launch, build and maintenance are priced separately and AI usage is billed at cost. I share a quote after our first call.

Yes. I'm based in Madison, Wisconsin and work with clients across the U.S. by video. Madison-area clients can meet in person for kickoffs.

Book a 30-minute call, or score your process on the readiness checklist first. Bring the process you'd like AI to handle. We'll talk through the data, the risks and what success is worth, and you'll leave knowing whether it's worth building.

Mostly small and mid-sized businesses: agencies with a few dozen client accounts, in-house marketing teams of five to fifty, and professional-services firms. The systems I build are sized to that scale and priced for it. If you're larger, the same approach works inside one team or business unit.

Less than you'd expect. A typical build needs a 60-minute discovery session, a short review of the written spec, access to the systems involved, and 20 to 30 minutes a week for check-ins. The people who will run the system join for the pilot and a handoff session at the end.

Yes, and I prefer it. I work in your repositories, your CRM and your tools, document everything in writing, and leave your team able to maintain and extend what I built. If you already have an agency, I can take the AI and automation layer while they keep the rest.

You do. Code, prompts, workflows, data and documentation are yours, in your accounts, on your infrastructure. There's no platform fee to keep using what I built. If you want me to keep running and improving it, that's the Managed AI Operations retainer, but it's optional.

Several layers:

• Agents that change things are built and tested in separate test environments first, with checks that confirm where they're working.
• Reporting systems are read-only by default.
• Client-facing agents pass a written test suite (50+ cases for a voice agent) and a safety regression before anyone outside the team hears them.
• When an input looks wrong, the system stops and notifies a person instead of guessing.

It depends on volume, and I measure it rather than estimate it. Every pipeline I build tracks its own model and data costs per run. For example, a full AI research pull on a new client costs about $4–8. A 33-agent platform serving an entire agency runs for about $32–42 a month.

• API keys stay on the server, never in the browser.
• Agents get the minimum access they need, often read-only.
• Sensitive items such as tax IDs and passwords never go through forms or AI prompts.
• AI-researched facts are labeled verified, derived or needs input, and unverified details are never presented as fact.

Yes. I design and test inbound phone agents that answer questions, book appointments, take messages and transfer to a person with a spoken summary. They capture consent before follow-up texts and have clear rules about what they won't say, such as prices or technical advice.

Yes, with the right method. I write a specification for every object, and Claude or OpenAI Codex builds it directly in the platform. After each phase an API readback and gap report show what was built and what wasn't. One unattended run lasted 3.5 hours, and the two-day build produced 351 custom values, a 12-stage pipeline and a 52-entry knowledge base.

Buy the commodity and own the interpretation. If a tool already measures something well, use it. Build where your data, process or judgment is the advantage. An AI Readiness Sprint ends with a clear build-or-buy call for each opportunity.

Each system has a written success measure before it's built: hours saved per week, response time to a lead, leads caught that were missed before, reports produced without a person. During the pilot those numbers are tracked and reviewed with you, and the system logs its own activity and cost so the numbers hold up after handoff.

Not in my experience. The systems I build take the repetitive, time-bound work (first replies to leads, nightly checks, report assembly, research before a call) so your people spend their time on judgment and relationships. Every system I ship keeps a person in control of anything that goes to a customer.

• AI: Claude (API, Claude Code, MCP), OpenAI (API, Codex), Gemini
• CRM & marketing automation: GoHighLevel, HubSpot
• Data: Supabase/Postgres, Google Sheets, Looker Studio, GA4
• Ads: Google Ads API and Scripts, Meta
• Web: WordPress, static sites, Netlify

If a tool has an API or webhooks, it can usually be connected.

Yes. I built a 15-script Google Ads suite that runs at the manager-account level across a whole agency portfolio: audits, budget pacing, search-term and wasted-spend analysis, and structure checks. It replaced about 12–15 hours of manual reporting a week.

It's the AI assistant on this site. You can ask it about my work or describe a process and get a first-pass automation plan. I build similar assistants for clients, connected to their own knowledge and tools.

The Model Context Protocol (MCP) is an open standard that lets AI assistants such as Claude securely use your data and tools. I've built an MCP server in production use that gives Claude access to an agency's client profiles, with token authentication and audit logging.