Skip to main content

Integration & reliability

Connect it properly. Then prove it works.

Most AI projects stall in two places: the model cannot reach the data, and nobody can show the output is good enough. I build the connection and the evidence.

Book a working session →

What this includes

The unglamorous half of an AI system.

Access, integration and measurement — the parts that decide whether it survives production.

01

MCP servers & AI integration

Give Claude, ChatGPT or Cursor controlled access to your own systems over the Model Context Protocol. I maintain three open-source MCP servers running on Cloudflare Workers.

DigestSEO MCP Suite
02

LLM evaluation & observability

An evaluation harness that catches regressions before your users do, plus the logging and traces that explain why a system behaved the way it did.

Flight Deck
03

Document & ERP integration

REST integrations across document management and ERP systems — including DocuWare and .NET estates — so automation reaches the data it needs.

What you walk away with

Systems that stay explainable.

Access without exposure

Scoped permissions and audit trails, so an assistant reaches the data it should and nothing more.

Regressions caught early

A change to a prompt or a model gets measured before it reaches your clients.

Failures you can locate

When something goes wrong you can see where it went wrong, not just that it did.

Bring the integration that stalled.

We will look at what is blocking it — access, data shape, or the lack of any way to measure success.

Book a working session →