One layer your entire stack agrees on
RevSure ingests every record from the 20+ tools you already run, resolves it to one governed, identity-resolved truth, and serves it to your attribution, your forecasting, and every AI agent through MCP. This is the context the whole platform runs on, and your whole team with it.
- Marc Becker · VP Engineering · Acmecontact
- M. Becker · marc@acme.io · Acme Inclead
- Marcus Becker · Acme Corporationaccount
- Marc B. · (no email) · Acmecall
Every source, in its raw form
Structured and unstructured, real-time and historical, online and offline. Read from the stack as it is, without forcing your teams to change how they work.
Native two-way connectors
Salesforce, HubSpot, Marketo, Snowflake, 6sense, Gong, and 40+ more · no CSV exports, no nightly dumps.
Real-time and historical
Stream live events as they happen and backfill years of history · current to the minute, deep to the quarter.
Structured and unstructured
CRM fields, web events, call transcripts, emails, and documents · parsed into one model the same way.
Your stack, unchanged
Connect what you already run. No rip-and-replace, no warehouse prerequisite, no schema migration.
One entity from many records
A duplicate in the CRM, an alias in the MAP, an anonymous session on the site. RevSure collapses the scattered records into a single resolved entity every agent can trust.
Identity resolution
Deterministic keys plus probabilistic matching collapse duplicate people and accounts into a single resolved entity.
Taxonomy standardization
Every tool's stages, fields, and labels normalized to one shared model · 'MQL' means the same thing everywhere.
Account hierarchy
Subsidiaries, divisions, and buying groups rolled into the corporate tree the way your revenue team actually sells.
Deduplication
Duplicates collapsed, so every agent and every report counts the same pipeline once, not four times.
Safe by construction
A source of truth every agent reads from has to be governed at every read. RevSure builds the controls into the layer itself.
MCP server
The whole layer exposed through one secure gateway · any agent, ours or yours, reads the same truth.
Token auth and RBAC
Every agent call carries a scoped, revocable token. Role-based access decides what it can read or write.
PII redaction
Field-level masking strips sensitive values before they ever reach a model.
Decision traces
Every score, attribution, and recommendation logged with the reasoning behind it · auditable and immutable.
One truth, every consumer
The same governed layer feeds the learning engine, the agents, and every application, so insight, action, and outcome never disagree.
The learning engine
Attribution, propensity, next-best action, and forecasting · all computed on one model, on one layer.
The agents
A team of GTM agents reads the layer, decides the next move, and runs it across your live tools.
The applications
Marketing ROI, funnels, pipeline acceleration, predictability, retention · five outcomes, one source of truth.
Bring your own model
Point Claude, GPT, Gemini, or your own model at the layer through MCP · governed the same way as ours.
Eleven capabilities. One layer
Data Integrations
The 25+ sources that feed the context layer.
ExploreData Harmonization
Map every schema to one model the platform reasons over.
ExploreIdentity Resolution
Reconcile every system into one account agents act on.
ExploreFull Funnel Data Graph
Touch to revenue, connected in one graph.
ExploreData Hygiene & Maintenance
Continuous dedup, validation, and decay control.
ExploreData Enrichment
Firmographics, technographics, and contacts on every record.
ExploreCookie-Less Tracking
First-party, account-matched web tracking. No cookies.
ExploreWritebacks & Real-time Activation
Push resolved data back into your live tools.
ExploreReal-Time Orchestration
Signals trigger action in seconds, not the nightly batch.
ExploreAPI Access
Every record, score, and event by REST, webhook, and warehouse.
ExploreMCP Server
Expose the layer to your own agents.
ExploreContext is two things. Most layers only build one
There's a reason a person succeeds at one company and fails at another. They think their capabilities travel with them, and they do. The context usually doesn't. The same is true for agents: one that performs in a demo fails in your environment because the context is different and was never handed over.
The first thing a context layer holds is the graph: events, entities, time traces, linked and resolved. That part converges. Anyone with enough connectors eventually builds a substrate that looks like context.
The second is what the graph was shaped by. RevSure's layer was stress-tested by years of real questions from CMOs, CROs, and RevOps teams: hundreds of variations of attribution, prioritization, and forecast questions, each one feeding back into how the context is organized and served. A layer built from the technology has no opinion about what matters. Ours was built from the problems.
That is why an agent pointed at RevSure acts like it has worked here for years.
The intelligence, the agents, the gateway
AI Learning Engine
The intelligence on top of the context · attribution, propensity, next-best action, forecasting.
ExploreAgents
The team that reads the layer and runs the next move across your live tools.
ExploreMCP Server
Token auth, RBAC, PII redaction, immutable audit · any agent, securely.
ExploreOne layer. Every agent agrees
Implementation included. Migration off your current attribution stack is on us.