The whole funnel, connected
Every touch, person, account, opportunity, and dollar linked in one graph, so attribution, forecasting, and agents traverse the same reality instead of joining tables and hoping.
A full-funnel data graph links every touch, person, account, opportunity, and dollar into one connected model, from the first anonymous visit to closed revenue. Instead of joining tables across disconnected tools and hoping the keys line up, RevSure builds the graph on its context layer, so attribution, forecasting, and agents all traverse the same reality.
Tables lose the relationships
| Node | Linked to | Edges |
|---|---|---|
| Account · Cursor | Opportunity | 3 |
| Buying group | People | 7 |
| Opp · $140K | Touches | 22 |
| Touch · LinkedIn | Person | 1 |
A lead, an account, three opportunities, and forty touches live in separate tables joined by brittle keys. The relationships, who influenced what, which touch preceded the deal, are exactly what attribution and forecasting need, and exactly what a row-and-column join throws away.
- Touches, people, accounts, and opps as connected nodes
- Relationships preserved, not flattened into a join
- Multi-threaded buying groups mapped to the deal
- Influence paths that a flat table cannot express
Every journey, end to end
| Step | Touch | Person | Stage |
|---|---|---|---|
| 1 | Ad click · LinkedIn | Anonymous | Visit |
| 2 | Content · SEO | VP Eng | MQL |
| 3 | Demo request | VP Eng | SQL |
| 4 | Exec call · Gong | CTO | Opp |
| 5 | Closed won | CTO | Won |
The graph stitches the full path from first anonymous touch to closed revenue, across people and channels, so you can see the sequence that actually created the deal, not the last click before it.
- First-touch to closed-won, stitched across people
- Cross-channel and cross-device paths joined
- Buying-group journeys, not single-contact trails
- The real sequence behind the deal, in order
One graph. Every model reads it
Attribution credits the right touches, the forecast traces pipeline to its source, and agents traverse the same connected reality. The graph is the substrate, the rest of the platform just queries it.
- Multi-touch attribution that follows the real path
- Forecasts that trace pipeline back to its source
- Agents that reason over relationships, not rows
- One queryable substrate for the whole platform
A substrate, not a dashboard
Related capabilities
The Context Layer
The identity-resolved spine the whole platform runs on.
ExploreIdentity Resolution
Reconcile every system into one account agents act on.
ExploreReal-Time Orchestration
Signals trigger action in seconds, not the nightly batch.
ExploreAPI Access
Every record, score, and event by REST, webhook, and warehouse.
ExploreQuestions, answered
What is a full-funnel data graph?
It is a single connected model of your go-to-market, where touches, people, accounts, opportunities, and revenue are linked as one graph rather than stored in separate tables. That structure lets attribution and forecasting follow a buyer end to end instead of stitching exports together.
How is a data graph different from a data warehouse?
A warehouse stores rows in tables you join at query time. A graph stores the relationships themselves, so the path from a touch to the account to the opportunity to revenue is already connected. RevSure builds and maintains that graph on the context layer.
What does the graph power?
Attribution, pipeline forecasting, and the AI agents all read from the same graph, so they act on one shared version of the funnel. When a record resolves or a signal lands, every downstream decision sees it.
The whole funnel, in one graph
See your funnel connected end to end.