Overview

Lantern is an AI marketing agent platform that unifies market knowledge and operational intelligence into a continuously learning world model. The platform combines a Revenue Ontology (schema defining entities, relationships, and signal types), a Data Waterfall (150+ enrichment providers in one call with 95% coverage and full provenance), and operational ingestion from CRM, MAP, engagement, product, support, billing, and conversation intelligence systems. Entity resolution runs continuously with double-threshold matching and LLM judgment to unify records across all sources into canonical identities with stored relationships. On this foundation, Lantern deploys AI agents that compose campaigns from evidence — not templates — using the world model to determine what to say, through which channel, in what sequence, and why. The homepage showcases six agent templates: ABM Command Center (turn target account lists into full campaigns), Waterfall List Builder (build verified lists from natural language), Closed-Lost Resurrection (automatically work closed-lost pipeline), Competitor Displacement Plays (find unhappy competitor customers), Pre-Call Research Briefs (equip reps with full context), and Speed-to-Lead Agent (respond in 90 seconds vs 26 hours). Agents learn your market, build campaigns, and keep them running so teams brief instead of execute.

Key Features

Data Waterfall

One enrichment call across 150+ providers with per-segment optimization, automatic conflict resolution, cost controls, and full provenance on every field. Achieves 95% coverage with zero vendor management. Only enriches delta — fields that are missing or stale.

Revenue Ontology

Defines the structure of the world model — entities, relationships, signal types, campaign objects. Detects schema gaps from usage patterns (overrides, skipped stages, reclassified signals) and proposes updates that propagate to all agents immediately upon approval.

World Model

Unifies market knowledge (companies, people, deals, relationships, signals from CRM, MAP, engagement, product, support, billing, conversation intelligence, and 150+ third-party providers) with operational knowledge (campaign data, sequence performance, deal progression, call outcomes, win/loss context) in a single graph with entities, relationships, and outcomes as first-class objects.

Entity Resolution

Continuous, cross-source unification with double-threshold matching and LLM judge. Real-time deduplication, merging, and relationship linking across all connected systems.

ABM Command Center Agent

Turns a target account list into a full campaign automatically, composing personalized ads, unique email sequences per buying committee persona, and AE briefing docs based on the world model.

Waterfall List Builder Agent

Builds a verified lead list from a natural language sentence instead of a week in spreadsheets, leveraging the Data Waterfall and Ontology for enrichment and scoring.

Closed-Lost Resurrection Agent

Automatically works closed-lost pipeline as the cheapest pipeline source, re-engaging prospects based on updated signals and operational patterns.

Competitor Displacement Plays Agent

Finds unhappy competitor customers as warm leads by monitoring review sites, job posts, and signals indicating dissatisfaction.

Pre-Call Research Briefs Agent

Ensures every rep walks into every call already knowing everything — compiling full context from the world model including relationship history, signals, and operational patterns.

Speed-to-Lead Agent

Responds to inbound leads in 90 seconds vs the typical 26-hour SDR response time, using instant resolution, enrichment, and scoring against the full world model before routing fires.

Schema Evolution

Ontology learns from team behavior (overrides, skipped stages, reclassified signals) and proposes structural updates that propagate to all agents immediately upon approval.

Business-Contextual Signal Classification

Classifies signals based on your industry, ICP, competitive landscape, and historical conversion patterns — not generic taxonomies. The same event (e.g., Series C funding) gets different weights and downstream actions depending on your business context.

Operational Intelligence Ingestion

Ingests campaign data, sequence performance, deal stage progression, call outcomes, and win/loss context from native integrations (CRM, MAP, engagement, product, support, billing, conversation intelligence) and attaches them to the relevant entities in the graph.

Agent Composition from Evidence

Agents query the world model to compose campaigns informed by market data, relationship context, and operational evidence. Every action draws on accumulated intelligence about what works for which segments, personas, and stages.

Pros & Strengths

✓

Unified World Model: Combines market knowledge and operational intelligence in a single continuously learning graph with entities, relationships, and outcomes as first-class objects — eliminating the reconciliation layer across 12+ systems.

✓

Superior Enrichment Coverage: Data Waterfall queries 150+ providers in a single call with 95% coverage, automatic conflict resolution, full provenance, and zero vendor management — replacing manual vendor arbitrage.

✓

Evidence-Based Agent Composition: Agents compose campaigns from operational evidence (what emails get replies, which sequences convert, which messaging resonates) rather than generic templates, improving per-customer over time.

✓

Business-Contextual Intelligence: Signal classification and schema evolution adapt to your specific industry, ICP, competitive landscape, and conversion history — not one-size-fits-all taxonomies.

✓

Continuous Entity Resolution: Real-time, cross-source entity resolution with double-threshold matching and LLM judgment ensures canonical identities and stored relationships across all data sources.

✓

Ready-to-Deploy Agent Templates: Six pre-built agent templates (ABM Command Center, Waterfall List Builder, Closed-Lost Resurrection, Competitor Displacement, Pre-Call Briefs, Speed-to-Lead) cover core revenue workflows and set themselves up from a brief.

Cons & Tradeoffs

⚠

Custom Pricing Model: Pricing requires direct contact with sales, which may create uncertainty for budget planning and smaller teams.

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Learning Curve: New users may need time to understand agent configuration, world model concepts, Ontology structure, and signal interpretation to maximize value.

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Integration Dependency: Effectiveness relies on the quality and completeness of connected tool integrations for accurate entity resolution, signal detection, and operational intelligence ingestion.

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Enterprise-Focused Architecture: Platform architecture (single-tenant, PrivateLink, forward-deployed engineer) suggests focus on mid-market to enterprise, potentially less suitable for small teams.

Known Limitations

• Platform effectiveness depends on integration quality and data completeness across connected tools

• Custom pricing model may not suit teams requiring predictable subscription costs

• Enterprise-grade deployment model (single-tenant, PrivateLink, dedicated engineer) may be excessive for smaller organizations

• Schema evolution requires human approval, which could slow adaptation in fast-changing environments

• Agent template library currently limited to six core revenue workflows

Pricing & Plans

Model: custom

Lantern uses a custom enterprise pricing model. Pricing details are not publicly disclosed and require direct contact with the sales team for a tailored quote based on organization size, requirements, and deployment scope (including single-tenant architecture, PrivateLink, and forward-deployed engineer).

✓ Free Plan ✓ Free Trial

Pricing verified: 2026-08-20

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