Ten years owning the CRM, lead-to-cash, billing, and data systems most AI hires have only integrated against. I put agents on top of those systems with eval harnesses and human-in-the-loop gates, encode the business logic into auditable workflows, and you stay in control.
AI teams hire engineers who have never owned a HubSpot pipeline or a billing reconciliation. MarTech and RevOps teams hire generalists who have never shipped an evaluated agent system.
I have done both for ten years, and the AI-and-internal-tools half is now the larger surface.
Nothing publishes until it clears the gate.
An automated post-production pipeline with AI-assisted gating before anything publishes.
One connector interface, every system in sync.
A multi-connector ETL platform syncing QuickBooks, Copper, Basecamp, and PandaDoc to PostgreSQL and Airtable behind a shared connector interface.
Every record accounted for on the way into one identity layer.
A PostgreSQL-staged pipeline that ingests Airtable records and resolves them into a deduplicated master-contact identity layer with reconciliation reporting.
Intermedia usage, billed straight into ConnectWise.
Full-stack billing automation syncing Intermedia usage data into ConnectWise agreements.
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CSV in, enriched contacts out, at a fraction of the cost.
A contact-enrichment pipeline: a Brave + Gemini CLI and an OpenAI + Firecrawl web UI, with per-contact cost tracking.
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Where scattered business data becomes client intelligence.
A Postgres-backed client-intelligence system that syncs Airtable records, normalizes identity fields, and surfaces a unified client view with source lineage.
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Keeps your AI coding sessions alive and restarts the ones that hang.
A terminal session supervisor for AI coding assistants.
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Run your dev ops from your phone.
A macOS daemon and CLI for mobile-first development ops.
Read the case studyAgent retrieval accuracy, the guardrails that keep failure rates low, the observability that turns agent behavior into something you can act on, and the connection between agents and the real systems they touch: billing platforms, CRMs, internal tools.
Small surface, strong contracts, eval gates, and a clean handoff to the non-technical operators who actually run the system.
When the work is "this process is manual, fragile, or invisible," when the data spans systems that do not agree, or when an AI workflow needs a quality bar it cannot fake.
Pure ML research, ground-up infrastructure or Kubernetes platform work, or anything that requires me to also own visual design. I will tell you when a role is a stretch rather than waste your screen.
Always glad to compare notes with people building agent-assisted systems, internal tools, and the RevOps and MarTech backbone they run on. Reach out any time.