Lantern

Lantern markets an AI platform for revenue teams built around agents that take a written brief and carry it through research, enrichment, targeting, creative production and activation, positioning itself as an alternative to workflow-builder tooling. The company frames the problem it addresses as an "execution gap": the manual work between a campaign idea and its launch — list pulls, data cleaning, segmentation, enrichment, personalization, sequencing, QA, approval chains and reporting — which it attributes to customer data being spread across many tools without a shared model of the market.

The product is organized as a library of specialist agents grouped by revenue function. Demand-generation agents include an ABM Strategist that monitors target accounts for buying signals and, when an intent threshold is crossed, assembles personalized ads, per-persona email sequences for each member of the buying committee and an account-executive briefing document; other agents cover 1:1 nurture campaigns and competitive monitoring of pricing, launches, hiring and reviews. Deal-stage agents produce pre-call meeting briefs, real-time multi-factor lead and account scoring, and inbound SDR handling that enriches, scores and routes form fills. Customer-growth agents cover churn prediction from support volume and renewal timing, expansion signals from usage patterns, and composite customer health scoring. Operational agents handle CRM hygiene (duplicates, stale data, missing fields), deal forecasting with automated next steps, and job-change tracking for champions.

The platform is described as three layers: a "Data Waterfall" that routes enrichment requests sequentially across more than 150 providers with per-segment optimization; a "Revenue Ontology" that normalizes CRM, intent, enrichment, product usage and support data into one continuously updated model with entity resolution across systems, explicitly stored relationships and an evolving schema; and an open-source "Agent Engine" in which workflows are defined in code with human-in-the-loop checkpoints and a full audit trail on every action. Published code examples reference packages such as @lantern/agents, @lantern/ontology, @lantern/signals and @lantern/campaigns.

Business model

Lantern sells an enterprise software platform combining hosted agents with an open-source execution engine; the website routes prospective buyers to a booked demo and provides a customer login, and the enrichment layer is presented as multi-provider to avoid vendor lock-in.