
Revenue Ontology
Your revenue team runs on twelve systems that have never been introduced to each other. The CRM knows deals. The support platform knows tickets. The call recorder knows conversations. The enrichment provider knows firmographics. The intent vendor knows web signals. None of them know that the Sarah Chen in Salesforce, the S. Chen in Zendesk, and the sarah@acme.io on the Gong transcript are the same person — or that her company just raised a Series C, her champion counterpart just left, and her support ticket from last month is about the exact competitor your AE is about to pitch against. Every rev ops team has tried to fix this with integrations, syncs, and duct-tape automations. It doesn't work. Syncing data between systems that don't share a schema just duplicates the mess. You end up with the same wrong data in more places, and a team that spends half its time reconciling instead of operating. The Revenue Ontology replaces the reconciliation layer entirely. Twelve systems feed into one knowledge graph. One model of every account, every contact, every deal, every signal, and the relationships between them. Every agent reads from it. Every action writes back.
Your revenue team runs on twelve systems that have never been introduced to each other. The CRM knows deals. The support platform knows tickets. The call recorder knows conversations. The enrichment provider knows firmographics. The intent vendor knows web signals. None of them know that the Sarah Chen in Salesforce, the S. Chen in Zendesk, and the sarah@acme.io on the Gong transcript are the same person — or that her company just raised a Series C, her champion counterpart just left, and her support ticket from last month is about the exact competitor your AE is about to pitch against. Every rev ops team has tried to fix this with integrations, syncs, and duct-tape automations. It doesn't work. Syncing data between systems that don't share a schema just duplicates the mess. You end up with the same wrong data in more places, and a team that spends half its time reconciling instead of operating. The Revenue Ontology replaces the reconciliation layer entirely. Twelve systems feed into one knowledge graph. One model of every account, every contact, every deal, every signal, and the relationships between them. Every agent reads from it. Every action writes back.
Every system sees the same customer. Every agent reads from one model.
Every system sees the same customer. Every agent reads from one model.
Twelve systems, one truth
Your CRM says Acme Corp has 1,200 employees. Your enrichment provider says 1,450. Your intent vendor has them under a subsidiary name your CRM doesn't recognize. The Ontology resolves these — matching entities across name, domain, phone, title, company, and behavior, then merging them into one record with provenance on every field. Clear matches merge automatically. Ambiguous cases surface for review. Every merge is reversible. The result isn't a "master record" that overwrites everything — it's a graph where every value has a source, a timestamp, and a confidence score, and the best answer wins.

Twelve systems, one truth
Your CRM says Acme Corp has 1,200 employees. Your enrichment provider says 1,450. Your intent vendor has them under a subsidiary name your CRM doesn't recognize. The Ontology resolves these — matching entities across name, domain, phone, title, company, and behavior, then merging them into one record with provenance on every field. Clear matches merge automatically. Ambiguous cases surface for review. Every merge is reversible. The result isn't a "master record" that overwrites everything — it's a graph where every value has a source, a timestamp, and a confidence score, and the best answer wins.

Every question is one query, not three exports
An AE wants to know: which accounts have a champion who opened a support ticket about a competitor in the last 90 days, are in an active deal above $100K, and showed an intent surge this quarter? Today that's three system exports, a VLOOKUP, and an hour. The Ontology stores relationships natively — person-to-company, person-to-deal, deal-to-signal, signal-to-account — so that query runs in seconds against the graph. Agents don't reconstruct context by joining CSVs. They read it directly.

Every question is one query, not three exports
An AE wants to know: which accounts have a champion who opened a support ticket about a competitor in the last 90 days, are in an active deal above $100K, and showed an intent surge this quarter? Today that's three system exports, a VLOOKUP, and an hour. The Ontology stores relationships natively — person-to-company, person-to-deal, deal-to-signal, signal-to-account — so that query runs in seconds against the graph. Agents don't reconstruct context by joining CSVs. They read it directly.

The model bends to your business, not the other way around
Out-of-the-box data models force your business into someone else's schema. Your CRM says deals go Qualification → Negotiation, but your reps run a Technical Validation step in between that doesn't exist in the system. Your team calls a certain type of intent signal a "budget unlock" — no vendor's taxonomy includes that. The Ontology detects these gaps from usage patterns and proposes schema updates. When your team overrides a signal classification or skips a deal stage, the system learns what those patterns mean for your business specifically and suggests structural changes. Approve them and every agent immediately operates on the updated model.

The model bends to your business, not the other way around
Out-of-the-box data models force your business into someone else's schema. Your CRM says deals go Qualification → Negotiation, but your reps run a Technical Validation step in between that doesn't exist in the system. Your team calls a certain type of intent signal a "budget unlock" — no vendor's taxonomy includes that. The Ontology detects these gaps from usage patterns and proposes schema updates. When your team overrides a signal classification or skips a deal stage, the system learns what those patterns mean for your business specifically and suggests structural changes. Approve them and every agent immediately operates on the updated model.

Signals mean what they mean for your business
A Series C means different things to different companies selling different products. For a security vendor, it's a compliance trigger — the company just hit the size where SOC 2 becomes mandatory. For a data platform, it's a budget unlock — new money, new infrastructure spend. For a recruiting tool, it's a hiring surge. Generic intent platforms assign one score to one event. The Ontology classifies signals based on your industry, your ICP, your competitive landscape, and your historical conversion patterns. The same event gets a different weight, a different classification, and a different downstream action depending on whose Ontology it's in.

Signals mean what they mean for your business
A Series C means different things to different companies selling different products. For a security vendor, it's a compliance trigger — the company just hit the size where SOC 2 becomes mandatory. For a data platform, it's a budget unlock — new money, new infrastructure spend. For a recruiting tool, it's a hiring surge. Generic intent platforms assign one score to one event. The Ontology classifies signals based on your industry, your ICP, your competitive landscape, and your historical conversion patterns. The same event gets a different weight, a different classification, and a different downstream action depending on whose Ontology it's in.

Works with
Works with
ABM Strategist
Queries the graph for accounts where intent score, ICP fit, and buying committee engagement all converge — a query that requires three separate exports without the Ontology.
Meeting Brief
Pulls buying committee, competitive signals, deal history, and recent engagement from one graph instead of six tabs. The brief is as good as the model it reads from.
Research Analyst
Traverses the full graph for any company or person in seconds — org structure, competitive landscape, funding history, call themes, support patterns — all connected.
Data Waterfall
150+ enrichment providers feed into the Ontology. Every field arrives with source, timestamp, and confidence score. The Ontology's entity resolution deduplicates and merges what the Waterfall enriches.
Track Champions
When a champion changes jobs, the Ontology surfaces every relationship tied to that contact — prior deals, NPS scores, support interactions — so the outbound references the full history, not just the name.
CRM Hygiene Expert
Deduplicates by matching on relationships and behavior patterns, not just name and email. The Ontology's entity resolution catches merges that string matching misses.
Score Leads & Accounts
Computes multi-factor scores against the live graph — fit, engagement, intent, competitive context — in real time, not from a nightly batch export.
Agent Engine
Every agent reads from the Ontology and writes back to it. The shared model means agents compound — what one agent discovers, every agent knows. The Engine calls the Ontology on every read and every write.
ABM Strategist
Queries the graph for accounts where intent score, ICP fit, and buying committee engagement all converge — a query that requires three separate exports without the Ontology.
Track Champions
When a champion changes jobs, the Ontology surfaces every relationship tied to that contact — prior deals, NPS scores, support interactions — so the outbound references the full history, not just the name.
Meeting Brief
Pulls buying committee, competitive signals, deal history, and recent engagement from one graph instead of six tabs. The brief is as good as the model it reads from.
CRM Hygiene Expert
Deduplicates by matching on relationships and behavior patterns, not just name and email. The Ontology's entity resolution catches merges that string matching misses.
Research Analyst
Traverses the full graph for any company or person in seconds — org structure, competitive landscape, funding history, call themes, support patterns — all connected.
Score Leads & Accounts
Computes multi-factor scores against the live graph — fit, engagement, intent, competitive context — in real time, not from a nightly batch export.
Data Waterfall
150+ enrichment providers feed into the Ontology. Every field arrives with source, timestamp, and confidence score. The Ontology's entity resolution deduplicates and merges what the Waterfall enriches.
Agent Engine
Every agent reads from the Ontology and writes back to it. The shared model means agents compound — what one agent discovers, every agent knows. The Engine calls the Ontology on every read and every write.
Three layers, one platform by Lantern
Three layers, one platform by Lantern
Every agent runs on three layers: a unified data model, 150+ enrichment providers, and an open-source engine where every decision is auditable.
Every agent runs on three layers: a unified data model, 150+ enrichment providers, and an open-source engine where every decision is auditable.
Data Waterfall
150+ enrichment providers. Sequential routing optimized per segment. The best answer wins. No vendor lock-in.

Agent Engine
Open-source execution engine. Workflows defined in code. Human-in-the-loop checkpoints. Full audit trail on every action.

Revenue Ontology
Every data source normalized into one model. Entity resolution across systems. Relationships stored, not inferred. Schema that evolves with your business.

FAQ
FAQ
Is this a CDP?
How long until we're live?
What about data security?
What if we add a new tool or data source?
How is this different from a data warehouse?

Your data already knows everything about your customers. It just can't talk to itself yet.
Your data already knows everything about your customers. It just can't talk to itself yet.
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USE CASES
Revenue Team
Marketing Team
Customer Success
PRICING
Pricing
RESOURCES
Blog
About Lantern
Status
Support
© LANTERN 2025
Terms
Privacy
USE CASES
Revenue Team
Marketing Team
Customer Success
PRICING
Pricing
RESOURCES
Blog
About Lantern
Status
Support
© LANTERN 2025
Terms
Privacy
