Production AI systems, for real businesses.
I'm Ben Burdick. I design and ship AI wired into the tools your business already runs on — private assistants your team uses in Claude or ChatGPT, data-extraction systems, and custom integrations. Built for production, not demos.
Four ways AI earns its place in a business.
Every engagement starts from your systems and your constraints — not a generic chatbot bolted on the side.
MCP servers
Custom Model Context Protocol servers that make your CRM, database, and internal tools callable by AI agents — with structured error handling, access control, and observability from day one.
Agentic & extraction pipelines
Multi-agent workflows and document-to-data pipelines that turn unstructured inputs into structured, schema-validated output — and hold their accuracy at scale.
Private AI deployments
A private assistant your team can use on confidential work — deployed with a privacy-first, zero-retention posture for firms that can't send client data just anywhere.
Where AI actually fits
A straight assessment of where AI earns its keep in your workflow — and where it doesn't. I'll talk you out of the wrong project as readily as into the right one.
Shipped and in use.
Client work is described by sector to respect confidentiality. Demos are labeled as demos.
Production MCP server
Two production systems: a nine-tool Model Context Protocol server over MLS and county records — live with a brokerage, in daily use, passed client acceptance — and a managed MLS-data platform for brokerages: replicated MLS feed, private assistant access in Claude and ChatGPT, and installable workflow skills. For brokers →
Private Claude workspace (working demo)
A working demo of a private, confidential Claude workspace for a law firm: case PDFs handled as native document blocks (scans included), task presets for record summaries and chronologies, admin oversight of every conversation, per-user usage caps. Built to show a firm what a private deployment looks like before committing; zero-retention is configured on the firm's own account at deployment.
Multi-tenant platforms
GroomCounter — multi-tenant operations software for dog-grooming shops, running in production: an MCP server (30 tools) that a shop's staff connect to their own Claude or ChatGPT, and an AI phone receptionist that books appointments into it. One-click demo →
Placeforge — a Google Places data SaaS with usage-based billing, built on Cloud Run and Cloud SQL, in pre-launch.
Structured extraction pipeline
A transcript-to-structured-data pipeline using tool-use with JSON schemas, validation-retry loops, and few-shot prompting — inherited at roughly 11,000 lines, calibrated against a held-out set to 95%+ adjudicated accuracy, and handed off to run on the client's own hosting.
Enterprise software behind the AI.
Before the AI work: years in enterprise software — Salesforce development, systems integration, and business-systems engineering across large legacy application estates. That's why the systems I build are wired into your real tools and made to run in production — not to demo well and fall over in week two.
Built for production
Error handling, access control, and observability are part of the build, not an afterthought.
Wired into real systems
Your data and your tools, made callable by AI — not a chatbot sitting in a vacuum.
Honest about AI
I'll tell you where AI is the right tool and where a plain script or a phone call wins.
Have a system that could use AI?
Tell me what you're working on. I read every message and reply personally — no funnel, no bot.