The fabric beneath every insurance workflow.

Unsupervised ML runs across your entire submission corpus — across every class, geography, NAICS segment, and fleet profile — without being told what to look for. It surfaces statistically distinct clusters with favorable loss characteristics, volume thresholds sufficient for credibility, and pricing inefficiency that indicates an untapped facility. A senior underwriter scanning the same book takes months and finds three opportunities. The model takes hours and finds thirty. Then ranks them.

Once a program is live, the same intelligence that discovered it watches every claim that flows through it. Frequency shifts, severity inflection points, geographic clustering of losses, and reserve development patterns are monitored continuously — not quarterly. If a driver safety score distribution is drifting, you see it in week four. Not at the treaty renewal when your carrier asks why the loss ratio moved twelve points. The programs that survive are the ones defended in real time.

Acquired books, assumed programs, and inherited facilities all arrive without the institutional memory of how they were built. The audit layer ingests the historical policy and claims data, reconstructs the risk segmentation logic, identifies which cohorts are performing and which are silently degrading, and produces a structured assessment in days — not the six-month review cycle a traditional actuarial engagement would require. Know what you own before you're accountable for it.

The model analyzes your bound portfolio, declined submissions, and external market data simultaneously — cross-referencing class segments against your current treaty structure, reinsurance appetite, and capacity utilization. It identifies segments where you are systematically underwriting favorable risks but not running a facility: the evidence that a program should exist, derived from your own experience data, presented with loss projections and volume estimates. Faster and more rigorous than any market study you could commission.

For every active MGA program, the fabric monitors claims in real time — frequency trends, severity distributions, geographic clustering, reserve development, and IBNR trajectory. When a program is developing adversely, you see it in the data weeks before it appears in a bordereau. You can act: adjust guidelines, tighten eligibility, increase pricing, or invoke your cancellation right — before the loss ratio has moved enough to threaten the relationship. Program defense is not a renewal conversation. It's a continuous posture.

When an MGA presents a program — or when you assume a facility through an acquisition or market exit — you receive their data and their story. The audit layer provides yours: an independent ML reconstruction of the program's risk segmentation, performance by cohort, loss development by vintage, and projected trajectory. No reliance on the originator's own analysis. Your treaty reinsurers will ask the same questions. Answer them before the meeting.

The same ML engine that your MGA and carrier clients use to discover programs is available at the treaty level. Analyze the aggregate submission and claims data flowing through the fabric across your entire client portfolio to identify emerging program opportunities before they're pitched to you. When a carrier approaches with a new facility, you already have an independent view of the segment — its loss characteristics, pricing dynamics, and market volume. You negotiate from a position of knowledge, not a position of trust.

Real-time claims monitoring across every treaty in your portfolio — frequency trends, severity shifts, geographic clustering, and reserve adequacy — updated as data arrives from clients, not when your actuaries run the quarterly model. When a program is developing adversely, the fabric surfaces it through a statistical signal before it appears in a reported loss ratio. You can invoke contractual rights, adjust pricing at renewal, or engage with your client on remediation while there is still time for any of those actions to matter.

A carrier presents a new program. An MGA requests a facility. You receive their actuarial submission and their projected loss ratios. The audit layer runs independently: ingesting their historical data, reconstructing the underlying risk cohorts, stress-testing the loss projections against comparable programs in the fabric's experience base, and producing a structured assessment with explicit assumptions and sensitivity ranges. Not a review of their work. An independent answer to the same question.