Independent assurance for insurers and lenders
Tensyl checks every quote and decision against what you filed and approved, and gives your reviewers a signed record an examiner can re-run without us.
What Tensyl isAn independent check for the people who sign off, delivered like an auditor’s report.
Findings report
Small business · checked against the filed WA rate manual
A surcharge the filing removed is still applied
RuleFiled $2,513 · charged $2,856
Owner not found in vendor data, default filled in
Hand-offFiled $2,513 · charged $3,041
Classifier missed the fryer, wrong business class
ModelFiled $2,513 · charged $1,866
1,630 checks built from the filing
No correct price flagged
Sign-off
Already in production: a public P&C rating platform pays us on outcomes.
The problem
Every price comes out of a chain: the quote site, the data hand-off, your rate tables, a risk model, an underwriter. Each team checks the piece it owns. Nobody checks the price that comes out the end, and the premium leaks where the pieces hand off to each other.
Regulators act on the rules, not on model accuracy. Discover’s rule was wrong for about 17 years, and Bank of America paid $225M for denying claims on a fraud filter alone. Yet the Federal Reserve’s model-risk guidance (SR 26-2) leaves fixed rules and generative AI out, and no one owns the final outcome.
How it works
Tensyl reads your filed rates and approved rules, writes down the right answer for every case, and re-runs your quotes and decisions against it. Each finding names the rule or hand-off that caused it and the dollars involved, totaled across your book. Your owners set the thresholds before anyone sees a result.
Every rate-table combination priced against the filed manual, so a missed rate change shows up on day one, not month 21.
What the shopper entered, what the quote site sent, and what your system filled in by default, with the dollars lost per field.
A sign-off pack for every generative AI step and vendor update, re-checked before the change reaches a decision.
Extends the record you already keep to cover what other companies sent you, so every hand-off, and every error, has an owner.
Proof
In production
A public P&C rating platform pays us on outcomes.
In our prototype, on mock systems
Errors were planted in mock systems modeled on real enforcement cases, not found in any company’s live system. See the full prototype results →
Why now
Before a model touches an underwriting, claims or lending decision, the people who can say no have to agree it is right, fair and controlled. Today that proof is written by hand, per use case, per model, per update.
No hand-checked cases, no fixed model version, and vendors update on their own schedule.
The NAIC says 42% of homeowners and auto AI models come from third parties, and no vendor can certify itself.
AI can read a filed rate manual or a regulation and turn every rule into a check. Until now that was done by hand, filing by filing.
Colorado's AI law, California's CARS Act and the NAIC's AI review supplement. Reinsurers and litigators ask for the same evidence.
Trust
We check your vendors' outputs as they are. What went in and what came out is enough.
Your team sets the thresholds before any result is seen, and can plant known errors to check our work.
An audit uses one de-identified export. A pilot runs read-only inside your cloud. We never train on your data.
Leave with every check and every record. Your validator can re-run them without us.
Your evidence is your IP
You keep every check, finding and signed record. We keep the engine and a library of failure types that grows with every customer.
How this differs from the tools you have: engineering tools tell you the software changed or a model improved. Tensyl tells you a price or decision is wrong, which rule did it, and what it cost. See the comparison →
Who builds it
Co-Founder & CEO
Product head, Uber Infrastructure and Director at Twitter Growth; product leader for Bing Ads API & platform. VP Product leadership at Instawork (Series D, Benchmark-backed). Rebuilt an operating model around AI and decoupled revenue from headcount.
LinkedIn ↗
Co-Founder & CTO
CTO at Instawork (Series D, Benchmark-backed), from Series A through Series D. Two-time founder with multiple exits; an early pioneer in social and mobile gaming, later CTO of a multibillion-dollar global gaming business. Turned a ten-million-professional labor marketplace into a training ground for real-world robotics.
LinkedIn ↗Free audit
Start where there’s no security review and no system access: one de-identified file of past decisions. A spreadsheet is fine. We check it against your own rulebook and send back what we found, with dollars attached.
Email only here. We’ll reply with a secure way to send the file, so please don’t include data in this form.
What we need
One de-identified export of past decisions, such as past quotes for one state and line with the inputs, rate version and premium charged. No access to your systems.
What you get
A findings report: every decision that doesn't match your filing or policy, the rule that caused it, and the dollars involved.
If it's useful
A 60 to 90 day pilot on one use case. Your team plants known errors, and your validator re-runs the evidence without us.