Braven provides an AI‑driven operating layer that automates the intake and processing of reinsurance submissions. By ingesting documents, applying defined rules and compliance criteria, and delivering completed work such as bordereaux, the platform lets agents focus on judgment and strategic decisions while the system handles repetitive tasks. The solution continuously learns from interactions, improving accuracy and speed as teams scale.
Funding
$1.1M 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.
Founders
Product
Problem
Reinsurance teams spend a large portion of their time manually extracting data from PDFs, applying static rules, and generating reports such as bordereaux, which slows underwriting, limits capacity, and creates error‑prone processes.
Solution
Braven offers an AI‑driven operating layer that ingests incoming submissions, parses and structures the data, and executes user‑defined workflows automatically. Users configure rules that reflect appetite, compliance, and internal processes; the platform’s agents apply these rules consistently, learning and improving over time. The system delivers completed work—triaged submissions, generated slips, and finalized bordereaux—rather than just raw answers, allowing teams to focus on judgment, relationship management, and strategic decisions. All actions are bounded by configurable limits and escalated when human intervention is required, ensuring control and auditability. Braven integrates with existing policy administration, email, and document systems, providing a unified view of the entire policy lifecycle from submission to renewal.
Target Audience
Primary customers are MGAs, reinsurance brokers, and reinsurers that need to process high volumes of specialty submissions, generate accurate reports, and maintain real‑time visibility into their risk pipeline.
Features
- AI agents that extract and structure data from PDFs and other documents automatically
- Configurable rule engine for appetite matching, compliance checks, and workflow automation
- End‑to‑end processing that delivers finished outputs such as slips, bordereaux, and renewal notifications
- Dynamic, role‑based workspaces that surface only the relevant fields, documents, and actions for each task
- Routine automation that runs repeatable tasks (e.g., daily triage, monthly bordereaux) with consistent standards
- Integration layer that connects to policy admin systems, email, document stores, and data providers while maintaining full audit trails
- Continuous learning loop where agent performance improves from user interactions and feedback