Step 01
Map the CRM terrain
AI Terrain Mapping reads your current CRM state alongside every source around it: product analytics, billing, support, spreadsheets. What the team believes about accounts and deals, compared against what the systems prove, charted into a map your team can inspect and correct.
Output: the terrain map, committed to your PATCH repo
Step 02
Model the revenue data
We design the Attio data model around how you actually sell, then join product usage, billing, and support behind it in your warehouse. Every attribute in Attio traces to a source. One account truth, with no duplicate records arguing with each other.
Output: an Attio model backed by one joined memory
Step 03
Generate account foresight
Memory shows what is true. Foresight shows what to move: the usage spike without a deal, the expansion no one owns, the renewal carrying support risk, all surfaced into the Attio views your team already works.
Output: the queue of next actions, inside Attio
Step 04
Ship the workflow
The next action becomes a workflow patch: enrichment, routing, lifecycle, alerts, all as code in your repo, all landing in Attio as clean records, tasks, and views. Verified against live data before your team relies on it.
Output: a verified workflow, live behind your Attio
Step 05
Read the signal report
Every patch reports back: what the system surfaced in Attio, what the team acted on, and the revenue it moved, trending week over week. The ROI report reads like an instrument, not a slide deck.
Output: the weekly ROI report, revenue moved
Step 06
Update the memory
The result feeds the model. Attributes sharpen, joins extend, routing rules adjust. Attio stays clean because the system underneath it is clean, and it compounds in infrastructure you own.
Output: a sharper model, and the next patch ships