Signals
One definition of what counts as a signal and what it implies commercially: the event, the evidence that validates it, who it is relevant to and what should follow. Held as your GTM system's memory so a signal arrives as interpreted context rather than as another alert.
The moments signals decide, and who decides what they mean
An event is not a signal until someone decides what it means. That decision either draws on company logic everyone can see, or gets made alone, fast, and without a record of why.
01 | The Current Way
02 | AI Added On
03 | AI-Native
Something happens at an account
Noticed, if noticed
A funding round or a new hire is spotted by whoever happened to be looking that morning.
Found faster, meaning unchanged
More events surface, faster. Each one arrives on its own, with no company history or contact context attached, so the rep still has to work out what it means.
Detected and interpreted
The event is validated against your evidence standard, then read in the context of that account and your GTM: what it means here, and who it is relevant to.
An alert reaches a rep
One more notification
It arrives in the same stream as everything else and competes with genuine work for attention.
More alerts, less attention
A system that finds more things sends more things. Volume is the failure, not the fix.
Arrives with an implication
The alert names the account, cites the evidence and states the recommended action. The rep has something to act on in the next five minutes.
Two teams see the same event
Two readings, no reconciliation
Marketing treats it as intent, sales treats it as noise, and neither knows the other decided differently.
Two tools, two verdicts
Each system scores the event on its own logic, and the disagreement is now automated.
Same evidence, different reading
The evidence and confidence behind the signal are shared, so sales and marketing read the same facts, even when what each does with it is different.
Priorities get set from signals
Recency wins
The newest alert gets the attention, regardless of whether that signal type has ever led anywhere.
Ranked by a borrowed score
A confidence number from a model trained elsewhere, not on which signals convert for you.
Weighted by your outcomes
Priority reflects which signal types actually produce meetings and pipeline in your business.
A signal stops working
It stays in the stack
A trigger that once predicted something keeps firing for years because removing it is nobody's job.
Noise, at scale
Automated detection makes a dead signal louder rather than quieter.
Analysis proposes the cut
Downstream outcomes show which signals earn their place. RevOps reviews any proposal to remove or reweight one before it takes effect.
Capabilities that reason with this memory
Prospecting
Propensity
Prospecting
Daily Brief | Prospecting
One system that understands, decides, acts and learns.
Every GTM signal flows through an AI-native operating layer into a system that runs on the surfaces your team already uses.
Explore the GTM System →Deal & Account State
Stakeholders
Risk & Health
Pipeline & Forecast
Campaign Planning
Renewals & Expansion
Meeting Prep
CRM Updates
Alerts & Escalation
Upgrade Messaging
Upgrade Playbooks
Upgrade Forecasting
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The GTM teams that learn fastest will win. Build yours a system that learns.
An advantage competitors cannot buy back: years of success and failure, codified.
One approved definition of which events matter, what evidence validates them, who they are relevant to and what should follow. Held as a versioned record, so a signal reaches a person already interpreted rather than as raw detection.
By stating the event, the evidence standard that makes it credible, the ICP and personas it matters for, and the commercial implication. An event without those four is a notification. With them it is something a rep can act on immediately.
By tracing signals through to what happened next. When each signal carries its evidence and interpretation, outcomes can be attributed back, and Signal Analysis can propose removing, reweighting or reinterpreting types that stopped earning attention.
An intent tool tells you an event occurred. Signal memory holds what your company has decided that event means, for which accounts, with what confidence, and what should happen as a result. The detection is the easy half.
RevOps does. The system tracks outcomes across accounts and surfaces evidence when a signal's meaning or weighting looks wrong. RevOps reviews that evidence and decides whether the new interpretation replaces the old one, so a change reflects company judgement rather than a single model's read.