Services
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AI Automation
AI AUTOMATION
AI that does a specific
job in production.
Not a demo.
Every agency claims AI now. Here's our claim, made specific: two Claude Code
developers on staff and AI systems running in production today — including internal tooling that generates automation documentation through the Airtable MCP server. We'll tell you exactly what we built and what it does.
WHAT WE ACTUALLY BUILD
Three kinds of AI systems, all with a defined job
AI workflow steps
A model dropped into an existing
automation to do the judgment work —
classify the intake, extract the fields, draft
the response — with a human checkpoint
where it matters.
AI agents
Systems that use tools: reading your
Airtable base, calling your APIs, taking
bounded actions. Built with Claude and
MCP, scoped so you always know what
the agent can and cannot touch.
AI pipelines
Multi-step flows in Langflow, n8n, or custom code — retrieval, generation, validation — that
you can see, audit, and maintain visually.
RECEIPTS
Built and running
Automation documentation via Airtable MCP
Internal tooling: Claude reads our clients' Airtable automations
through the MCP server and generates the documentation their
teams never had.
AI Scraping and Data Validation
Client Tooling: Through a series of AI agents we scrape unstructured data, turn it into structured data, sanitize, and validate using API's. This data gets uploaded to Airtable and Supabase to power a custom web application.
What we won't do: promise autonomous everything, wire an LLM to systems it
shouldn't touch, or ship something your team can't supervise. AI earns a place
in your operation the same way any automation does — by being reliable,
observable, and worth its cost.
RELATED
Have a job AI should be doing?
Describe it. We'll tell you whether current models can do it reliably —
and if they can't yet, we'll say that too.