Manifesto
Revenue teams run on systems that were never designed to remember.
The CRM is a point-in-time database. Every stage move, close-date push and amount change replaces the last one. The pipeline report shows where every deal stands today, but the story of how it got there is gone.
When a sales leader asks why a deal slipped three quarters in a row, or a board member asks what happened to the pipeline we had in January, the honest answer at most companies is that nobody knows. The data that would answer the question was overwritten months ago.
The story is scattered, too.
Revenue data doesn't live in one place. Calls live in one tool, emails in another, invoices in a third. Marketing touches sit in the marketing platform, support tickets in the help desk, payments in billing. The same customer appears under slightly different names in five systems.
No one sees the whole deal, and no single system can say what a customer experienced across their journey, from first marketing touch through sale, onboarding, support and renewal.
Every workaround falls short.
- CRM audit trails and field history capture fragments: a handful of tracked fields, in one system, in a format built for compliance rather than analysis.
- BI dashboards and warehouse projects promise a complete picture but take quarters to build, need dedicated data engineering, and still show snapshots rather than the sequence of changes that explains them.
- Revenue intelligence platforms like Clari and Gong analyze forecasts and conversations, but each covers its own slice of the stack and keeps its insights inside its own interface.
AI made the gap urgent.
Revenue teams now expect to ask Claude or ChatGPT about their pipeline the way they'd ask an analyst. But AI can read your CRM, not what it said last month. An assistant with no memory of how deals moved, what was promised, or which accounts looked like churned customers before they left can only comment on the present.
The teams getting real value from AI in revenue operations are the ones feeding it history. And history is exactly what today's stack throws away.
Meanwhile, RevOps teams are stretched thin. Headcount is scarce, the stack keeps growing, and the questions from leadership keep getting harder.
So we built a flight recorder for revenue.
Not another dashboard showing where things stand, but a memory layer that captures how they got there: complete, cross-system, queryable by people and AI, watched continuously by agents, and interpreted by a named engineer. Here's what we believe.
- 01
Record, don't overwrite.
Every stage move, close-date push, amount change and new contact, with a timestamp. And the history your CRM already has, pulled in at setup, so the record starts deep rather than empty.
- 02
One account means one account.
We connect CRM, email, calendar, calls, marketing, support and billing, and match people and companies across all of them. The whole deal, from first touch to collected cash, reads as one story instead of five partial ones.
- 03
Give AI a memory.
We turn revenue history into clean context that Claude, ChatGPT and MCP-compatible agents can query directly. Anyone on the team can ask what changed, when and why, and get an answer grounded in the full record.
- 04
Watch around the clock.
Agents run on the recorded history: the Forecast Auditor scores committed deals before every forecast call, Deal Rescue flags deals that look like past losses, and Churn Early Warning spots accounts that look like churned customers did 90 days before they left.
- 05
Software records. People decide.
Every customer gets a named Revenue Engineer, a specific person rather than a support queue, who connects the systems, runs the agents, brings the findings and joins your reviews.
- 06
Prove it on a deadline.
Live in your first week, with setup done by our team. Every engagement starts with a 30-day revenue evaluation against three success criteria agreed up front. Proof on a deadline, not a platform project.
Stop losing the story of your revenue. See how an evaluation works.