Primer offers a secure AI platform that ingests and structures massive unstructured text collections, enabling intent‑aware search, entity extraction, and source‑grounded summaries with line‑level citations. Its Retrieval‑Augmented Generation with Verification (RAG‑V) ensures defensible insights for intelligence, defense, and civilian analysts, and can be deployed in classified, air‑gapped environments or accessed via APIs for integration into existing workflows.
Funding
$69M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
1OFounders
Product
Problem
Intelligence, defense, and civilian analysts must sift through massive, fragmented unstructured data—from classified reports to open‑source news—to extract actionable insight, but existing tools are slow, lack traceability, and cannot operate in secure, air‑gapped environments.
Solution
Primer provides a secure AI platform that ingests, indexes, and structures large volumes of unstructured text, enabling intent‑aware search, entity and relationship extraction, and source‑grounded summaries. Its Retrieval‑Augmented Generation with Verification (RAG‑V) breaks down answers into individual claims and attaches line‑level citations, ensuring every insight is defensible. The system can be deployed on classified networks (IL5/IL6), air‑gapped or DDIL environments, and integrates via APIs, batch workflows, or a full UI. Analysts can explore results through interactive timelines, maps, and network graphs, while the AI agent suggests follow‑up questions and curates findings within a unified workspace. This accelerates knowledge discovery, supports rapid decision‑making, and maintains the auditability required for mission‑critical operations.
Target Audience
Primary customers are intelligence analysts, defense mission teams, and federal civilian agencies that require secure, traceable analysis of large unstructured data sets.
Features
- Intent‑aware search that automatically selects Boolean, semantic, or hybrid modes and interprets implied filters such as dates or source types
- RAG‑V engine that verifies each claim against original documents, providing line‑level citations and flagging gaps or contradictions
- Automated extraction of entities, events, locations, narratives, and relationships with visualizations (timelines, maps, network graphs)
- Secure deployment options for classified, air‑gapped, and DDIL environments with role‑based access controls
- Model‑agnostic architecture supporting multiple AI models and allowing mission‑specific model selection
- API and batch interfaces for embedding search, extraction, and summarization capabilities into existing command‑and‑control or analytical tools
- Real‑time monitoring (Primer Command) that tracks evolving narratives across 60,000+ news sources and social media platforms, delivering context‑rich alerts and SITREPs