How it works
Four steps, no migration project
Connecting a CRM is usually a same-day job. The weeks after that go on agreeing definitions and deciding which signals your team trusts, which is the part that makes the number stick.
- 01
Connect data
Point the platform at the systems that already hold your revenue data.
Authenticate your CRM, warehouse, billing, product analytics and support tools. Historical records backfill on the first sync and incremental syncs keep the picture current from then on. You review the field mappings before anything is used, and nothing is written back to your CRM unless you enable it.
- CRM, warehouse, billing, product and support sources
- Full historical backfill on first connection
- Incremental syncs, read-only by default
- Field mappings reviewed before anything is modeled
- 02
Model signals
The platform learns how deals behave in your business, not in general.
Models are fitted on your own closed-won and closed-lost history, segment by segment, so the patterns that predict a close in enterprise are never assumed to hold in self-serve. Engagement, stage velocity, buying-group coverage, product usage and billing behavior are weighted by what has actually predicted outcomes for you.
- Trained on your own closed history
- Segment-specific weighting, not a shared template
- Signals ranked by predictive value rather than opinion
- Data quality problems flagged instead of absorbed
- 03
Generate forecasts
A ranged forecast, refreshed continuously, with its reasoning attached.
Forecasts are produced bottom-up from deal to segment to company, each with a confidence range. Every figure can be opened to reveal the deals and signals underneath it, and the submitted forecast sits alongside the modeled one so the difference between what the team commits and what the data suggests is always on screen.
- Ranged output with a stated confidence level
- Roll-up from deal to segment to company
- Submitted and modeled forecasts side by side
- Drill-down from any number to the deals behind it
- 04
Monitor and optimize
Watch the number move, get told why, and hold the model to account.
Changes are tracked continuously and delivered to the people who need them in the tools they already work in. The platform scores its own accuracy after every closed period, so you can see where it reads your business well and where it does not, and the weighting adapts as segments and motions change.
- Movement alerts with the cause already attached
- Notifications in Slack, email or your CRM
- Accuracy scored against every closed period
- Weighting adapts as the business shifts
What to expect
The honest version of the timeline
Days one to three
Authenticate your CRM, let the historical backfill run, and review the field mappings. A first modeled forecast appears at the end of this, along with a backtest against your closed history.
Weeks one to three
Agree segment definitions, connect billing, product and support sources, and work through which signals your team believes. Expect to argue about definitions here. That argument is the work.
From the first close
The platform scores itself against the period that just closed and keeps doing it. Accuracy by segment and by horizon becomes a number you can point at, rather than a claim in a proposal.
Bring your systems list to the first call
Tell us what you run and we will tell you plainly what we can read on day one, what needs a warehouse connection, and what is out of reach.