Stellaris leads $7m series A for Pibit.AI to streamline underwriting
Pibit.AI, a San Francisco-based insurtech, has raised US$7 million in a series A round led by Stellaris Venture Partners, with participation from Y Combinator and Arali Ventures. The company builds Cure, an AI platform focused on speeding up underwriting for carriers and MGAs by cutting manual steps and improving decision flow.
The funding will go into AI infrastructure, integrations, and advanced risk models. Translation: more data pipes, smarter triage, and faster submission handling.
Who's using it
Pibit.AI serves clients across the US and employs over 125 people. Current customers include HDVI, Shepherd Insurance, RMS Insurance Brokerage, Kinetic, and Method Insurance Company.
The pitch is straightforward: reduce manual work, shorten cycle times, and help underwriters focus on risks that matter.
What Cure likely means for underwriting teams
- Submission intake: Automated document ingestion and normalization to clear queues faster.
- Risk signals: Consistent enrichment from third-party data to improve appetite checks and routing.
- Decision support: Scoring and summaries that make sense at the desk level, with an audit trail.
- Integration-first: Connectors into policy admin, rating, CRM, and broker portals to avoid swivel-chair work.
Why this funding round matters
Underwriting leaders are under pressure to process more submissions without growing headcount. Tools that cut repetitive tasks can move the needle on hit rate, quote turnaround, and expense ratio-without forcing a full core replacement.
If Cure continues to build reliable integrations and transparent risk models, adoption will come from measurable cycle-time gains and cleaner broker experiences.
How to evaluate AI underwriting platforms (quick checklist)
- Data ingestion: Accuracy on common artifacts (loss runs, ACORDs, SOVs) and handling of messy PDFs.
- Explainability: Clear reasoning behind scores, with versioning and audit logs.
- Integration depth: Native connectors for your PAS, rating engine, CRM, and data vendors.
- Controls and compliance: PII handling, model governance, and change management that satisfies audit.
- User experience: Minimal clicks for underwriters, with simple exception handling.
- Outcomes: Hard metrics-quote turnaround, bind rate, clearance speed-not just demo polish.
Practical next steps for carriers and MGAs
- Run a 90-day pilot on one LOB or class with a clear baseline for TAT, clearance rate, and bind rate.
- Define underwriter-in-the-loop checkpoints so decisions stay controlled and explainable.
- Track broker satisfaction and submission responsiveness alongside operational metrics.
- Plan for data governance early: retention, access, and model update cadence.
The bottom line
Funding gives Pibit.AI more room to deepen integrations and sharpen risk models-where most AI underwriting tools win or lose. For insurance teams, the upside is simple: fewer manual touches, faster decisions, and cleaner handoffs across the underwriting workflow.
Source: Pibit.AI
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