Anything you do in Operating, an AI agent can do too
Operating exposes its whole operating model through an MCP server — so the AI assistant your firm already uses can read it, reason over it, and act on it. Every action in the UI becomes something you can automate, on a data structure clean enough for agents to trust.
A data structure agents can rely on
Operating already holds people, projects, time, and money as one connected model — clean and current enough for agents to act on.
Connect the AI assistant you already use
The MCP server links Operating to Claude, ChatGPT, or any MCP-capable assistant — no new tool for your team to learn.
Automate the manual work
Point an agent at the busywork — staffing plans from a proposal, timesheet gaps filled, skills kept current automatically.
A data structure agents can rely on
Agents fail on messy data, not hard questions. Operating already holds the firm as one connected model: people, skills, projects, allocations, time, budgets, revenue and more. An agent reasons over a clean, current structure instead of guessing across exports and screenshots.
One connected model: people, projects, time, and money
Structure generic tools lack: rates, billing types, revenue recognition etc.
Clean and current enough for agents to read and act on
Connect the AI assistant you already use
The MCP server connects Operating to Claude, ChatGPT, or any MCP-capable assistant. Anything you can do in the interface: staff a project, check a margin, log time, update a skill, an agent can do through the same model. No new tool to learn; automation lives where your team already asks questions.
Works with Claude, ChatGPT, and any MCP-capable assistant
Anything in the UI is available as an agentic workflow
Read and act on live data — not a snapshot or a chatbot bolt-on
Automate the manual work — a few examples
Point an agent at the busywork and let the clean model do the rest:
Paste a statement of work or proposal to Claude → a staffing plan built in Operating
A time-tracking agent that watches calendars and fills the gaps in people's timesheets
Skills kept current automatically, mapped from each person's project history
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Why an MCP server, not a chatbot?
Agents need more than access. They need context they can trust.
Most assistants reason across exports and screenshots, and guess at the gaps. Operating already holds the firm as one connected model — people, skills, projects, allocations, time, budgets and revenue — so an agent reads a current structure instead of guessing.
More of the working week runs through an AI assistant. Operating gives that assistant one clean operating model to work from, so the business still adds up.
So we exposed the whole operating model through an MCP server rather than bolting a chatbot onto the interface. Anything you can do in the UI — staff a project, check a margin, log time, update a skill — an agent can do through the same model.
It also means automation lives where your team already asks questions. Connect Claude, ChatGPT, or any MCP-capable assistant, then point it at the busywork: paste a statement of work and get a staffing plan, let an agent watch calendars and fill timesheet gaps, or keep skills mapped from their project history.
Get the Operating Routine for resource planning
A weekly routine for staffing and capacity, written by people who ran it in their own firms. It gives your resource planning meeting a fixed agenda and a short list of numbers to check. Get the guide.
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