Solutions
Six ways I put AI into production
Every engagement ships with a written specification, a test plan and a cost model. You own everything I build.
AI agents & workflow engineering
Multi-agent systems, skill libraries and orchestration pipelines that do real work on a schedule or a trigger, with shared memory, QA gates and a known cost per run.
A client-intelligence platform with 33 agents, an 8-tool MCP server and 39 client profiles, operating for about $32–42 a month. Read the case study
What's included
- Multi-agent platforms with a shared, sourced memory of each customer or client
- MCP servers that connect Claude to your own data and tools
- Skill libraries with versioning, evaluation and a searchable catalog for your team
- Orchestration pipelines with validation gates, auto-remediation and cost telemetry
- Coding agents (Claude Code, OpenAI Codex) building inside SaaS platforms from a written spec
- Webhook-triggered agents that research, write and post results back into your systems
Voice & conversational AI
Phone and chat agents that answer, qualify, book and hand off, built around your routing and tested before a customer ever hears them.
An inbound voice agent family for a multi-location retailer: 11 of 11 first-block checks passed, with live calendar booking verified on a call. Read the case study
What's included
- Inbound voice agents with booking, message taking and warm transfer with a spoken summary
- Website, SMS and social chat assistants writing to the same CRM record
- Knowledge-base architecture shared cleanly across brands and bots
- Consent capture, safety rules and clear limits on what the agent will say
- 50+ case test suites and stop-ship regression checks before launch
- Pricing that separates launch, build and maintenance, with usage billed at cost
CRM & marketing automation
GoHighLevel and HubSpot-class systems that respond to leads in under a minute, onboard clients with AI research, and build themselves from a specification.
A 10-workflow speed-to-lead engine, built by Claude from a written spec; the first clean test sent a text and an email in under a minute. Read the case study
What's included
- Speed-to-lead engines with business-hours, compliance and response-time checks
- AI-researched client onboarding: the client corrects a profile instead of filling out a questionnaire
- Sub-account provisioning, snapshots and custom values at scale
- Lifecycle and purchase-driven campaigns from your order or POS data
- Webhook, API and MCP integrations between your CRM and AI tools
- Automated QA that enrolls test contacts and checks every workflow path
Reporting, analytics & ad-ops automation
The reporting and optimization work your team does by hand every Monday, automated, from someone who ran weekly business reporting for a $40M+ marketing program.
A 15-script Google Ads automation suite running across an agency's whole portfolio, replacing about 12–15 hours of manual reporting a week. Read the case study
What's included
- Google Ads scripts at manager-account level: audits, budget pacing, search-term and wasted-spend analysis
- Nightly account-health monitoring with alerts written back into your CRM as tasks
- Client report packets generated from your Looker Studio dashboards
- Weekly meeting decks built from live data
- Attribution architecture: click-ID capture with the CRM as the join point
- Pipeline and portfolio analysis that turns raw CRM data into decisions
AI readiness, roadmaps & fractional AI lead
Where AI fits in your business, what to buy versus build, and the order to do it in. Then, if you want, I lead the work.
An AI automation roadmap and systems-integration readout for a healthcare IT consultancy's leadership and finance teams. See the project
What's included
- AI readiness audit scoring 5 operational domains out of 100
- Opportunity map ranked by hours saved, revenue impact and risk
- Build-vs-buy evaluations with a clear recommendation
- Vendor, tool and builder selection, including paid screening tests
- Team enablement: skill libraries, playbooks and training
- Fractional AI lead or CTO scope on a monthly basis
AI-ready websites & search visibility
Websites built to rank on Google and to be cited by ChatGPT, Perplexity and Gemini, with accessibility engineered in from the first component.
A 20+ page site for a healthcare IT consultancy, concept to live in 30 days, solo; it passed 1,000 organic clicks per 28 days within four weeks of launch. Read the case study
What's included
- Hand-coded, fast builds with no page-builder bloat
- WCAG AA accessibility audited before launch
- SEO, answer-engine and generative-engine optimization from day one
- LLM visibility audits across ChatGPT, Gemini and Claude, scored 0–100
- llms.txt, structured data and citable, question-led content
- Launch runbooks with redirect maps and instant rollback
Solution patterns
Common starting points
Systems I've already designed and tested. Each one can be scoped in a single call.
AI lead research report
A new lead triggers an agent that researches the business and briefs your team before the first call.
Speed-to-lead responder
Every form, call and chat answered in under a minute, then qualified and booked.
Voice receptionist
Answers, books, takes messages and transfers with a summary when a person is needed.
Research-then-verify onboarding
AI pre-fills the client profile; the client corrects it in minutes instead of filling out forms.
Nightly account health
Silent forms, stalled deals and waiting leads flagged every morning as tasks.
Monthly report packet
Dashboards in; an internal report, a client report and a meeting agenda out.
Website asset loader
Logo, colors, fonts and photos pulled from a site and filed into your CRM in minutes.
Missed-call text-back
An unanswered call gets a text and an AI qualifier instead of a lost lead.
Engagement models
Four ways to work together
Launch, build and maintenance are priced separately, and AI usage is billed at cost, so you always know what you're paying for. Not sure where you stand? Take the 20-question readiness checklist.
2 weeks
AI Readiness Sprint
Scored audit, opportunity map, build-vs-buy calls and a 90-day roadmap.
2–6 weeks
Build Sprint
One system specified, built, tested and handed off with documentation.
Monthly
Fractional AI Lead
Ongoing builds, operations and team enablement with a set number of days each month.
Monthly
Managed AI Operations
Monitoring, maintenance and iteration of the systems I built for you.
Tools I work in
Not sure which one fits?
Describe the process you want off your team's plate. I'll tell you which approach fits, or whether AI is the wrong tool for it.