Getting started

Aqmen organizes commercial due diligence work around projects. A project is the home for one engagement (one target, one market, one set of questions) and it groups everything that belongs together: analyses, hypotheses, sources, and scenarios.

Sign in and create a project

Log in at app.aqmen.ai with your work account. Your workspace is shared with your organization: teammates you invite see the same projects and work in the same models.

Create a project for the engagement you're starting. Give it a name the deal team will recognize: the target or the market under study.

Add analyses

Inside a project you build analyses. There are three kinds, mirroring the three workstreams of a CDD:

• Market Sizing: model TAM, SAM, and SOM as a driver tree with dimensions, variables, and time-series values.
• Competitive Landscape: benchmark players across a standardized dataset grid.
• Company Analysis: build a P&L model of the target with computed and input nodes, and a valuation on top.

A project can hold several analyses of each kind, for example, one market sizing per geography, or a base-perimeter and an expanded-perimeter view of the same market.

Why they belong in one project

These three are the components of a commercial due diligence, and they answer each other's questions:

• The market sizing sets the growth the target's plan is judged against.
• The competitive landscape triangulates that market size from player revenues, and frames where the target sits.
• The company analysis tests whether the target's plan is credible in that market, against that field.

Split across three spreadsheets, a market model claiming 8% growth can sit next to a target plan assuming 15% for weeks before anyone notices. In Aqmen the three analyses share one workspace: one click apart, same mechanics, same body of sources. Each still carries its own values, assertions, hypotheses, and scenarios, but holding one against another is a glance, so contradictions get loud early.

The working rhythm

Most work in Aqmen follows the same loop, whichever module you're in:

1. Structure first: shape the tree, grid, or P&L before filling in numbers.
2. Populate with cited data: historical values are asserted with sources, so every number stays defensible.
3. Forecast with hypotheses: explicit growth assumptions drive the forecast, dialled per scenario.
4. Review and iterate: challenge any number and trace it back to where it came from.

Working with an AI agent

Everything in this guide can also be done by an AI assistant connected to your workspace over MCP. The agent builds the same structures, cites its data, and leaves everything reviewable. See MCP integration for setup.