The intelligence and memory layer for modern revenue operations
Give your revenue systems a shared memory.
RevenueGEM connects revenue evidence, prior decisions, human judgment and outcomes so people and AI agents can operate from the same trusted context.
We're starting with attribution, because revenue intelligence can't be trusted if the evidence underneath it is messy, conflicting or incomplete.
Clean, reconcile and explain messy attribution data. Fix it once, and RevenueGEM remembers.
- records analyzed
- 500records analyzed
- needed attention
- 114needed attention
- Attribution Health
- 67 → 85Attribution Health
Example from RevenueGEM's 500-record validation dataset.
Explore RevenueGEM AttributionThe problem
Agents and automation are multiplying. Revenue context is still fragmented.
Every system in the revenue stack sees part of the customer journey. AI agents and automations can act, but they often lack consistent revenue state, historical context, prior human decisions, reliable provenance, and any record of what worked before.
The result
- Repeated mistakes
- Bad context
- Conflicting data
- Unnecessary human intervention
- Automation that works in demos and breaks in real operations
- CRMknows one version of the source
- Ad platformsknow another
- Website behavioradds another
- Sales activityadds another
- Human decisionsdisappear into Slack, spreadsheets and tribal knowledge
- AI agentsstart each workflow without the full history
The RevenueGEM model
Evidence → Memory → Intelligence → Action
Each layer depends on the one before it. Intelligence is only as good as the evidence and memory beneath it, and actions are only safe when the intelligence can be trusted.
- 01Available now for attribution
Revenue Evidence
Clean, reconcile and connect the signals generated across the revenue stack.
Today: Normalizes sources and channels, keeps conflicting evidence, records provenance.
- 02Available now for attribution
Revenue Memory
Preserve decisions, mappings, context, workflow history and outcomes, so teams and agents stop solving the same problem twice.
Today: Approved mappings become workspace memory and apply to every future dataset.
- 03We're building
Revenue Intelligence
Determine what the evidence means, identify problems, surface patterns and recommend what should happen next.
Today: Today: confidence scores, explanations and review routing in Attribution.
- 04Where RevenueGEM is going
Revenue Actions
Help people and agents safely intervene, execute and learn from the outcome.
- Evidence
- Decision
- Human judgment
- Action
- Outcome
- Memory
RevenueGEM uses that accumulated context to improve the next revenue decision.
Why memory matters
Your revenue systems should remember what your team already learned.
The value isn't storing more data. It's remembering what happened, what was decided, and what worked, so the next person or agent doesn't start from zero.
Know what happened. Remember what was decided. Learn what works.
- What a source value actually means
- Which evidence was trusted, and why
- Why a person overrode an automated decision
- Which workflow produced the right outcome
- What happened when a particular action was taken
Starting with attribution
Available nowTrust starts with the evidence.
Before RevenueGEM can help teams and agents make better revenue decisions, the underlying data has to be trustworthy. RevenueGEM Attribution is the first product in the platform, and the first layers of Revenue Evidence and Revenue Memory.
- Clean
- Normalize broken UTMs, drifting campaign names and overwritten CRM fields.
- Reconcile
- Weigh first touch, latest touch and self-reported evidence. Keep conflicts instead of hiding them.
- Explain
- Every record shows its confidence and the evidence behind it.
- Review
- Real judgment calls go to people. Uncertain cases are never guessed.
- Remember
- Approve a mapping once and it applies to every future dataset.
- Source
- Channel
- Paid Social
- Confidence
- 96%
- Independent sources agree on the channel
- Latest touch describes a later visit and is kept separately
- Self-reported answer names the same platform (paid vs organic differs)
RevenueGEM Memory
Decide once. RevenueGEM remembers.
How it works today
Available now- 1A reviewer approves
linkedn → LinkedIn - 2RevenueGEM saves it as workspace memory, with who decided and when
- 3A new dataset arrives and linkedn appears again
- 4RevenueGEM applies the decision instead of asking again
What memory can hold next
Where RevenueGEM is going- Attribution decisionsAvailable now
- Routing decisionsPlanned direction
- Workflow exceptionsPlanned direction
- Agent actionsPlanned direction
- Human overrides across systemsPlanned direction
- The revenue outcomes that followedPlanned direction
Where RevenueGEM is going
Revenue operations are becoming agentic. Their memory needs to become shared.
As agents take on more work across revenue systems, they need current state, prior context, human judgment and outcome history. RevenueGEM is building the shared revenue memory and intelligence layer that connects those decisions over time.
- Attribution intelligence
- Signal intelligence
- Workflow observability
- Decision intelligence
- Human-in-the-loop actions
- Revenue GEM discovery
Future direction, not shipped features. What is available today is RevenueGEM Attribution.
Design partners
Help build the future of revenue operations.
We're working with a small group of B2B teams to build RevenueGEM from real revenue workflows, beginning with attribution. Design partners get early access at no cost and help shape what we build next.
Ideal partners
- B2B SaaS
- HubSpot
- Multiple acquisition channels
- Inconsistent or unreliable attribution data
- Willing to test using a real CRM export
No Anthropic or AI API account required. RevenueGEM provides the technology.