Loss history and policyholder data are exactly what AI is good at analyzing, and exactly what you've been told to keep away from it. Nera closes that gap without asking you to trust a policy.
Numbers from independent research on AI-assisted analytics adoption, not a Nera-specific claim, but a real starting point for the math on your own team.
The data that makes underwriting decisions better is the data you're least able to run through AI.
Five years of claims history, read line by line, for every account. There's been no safe way to point AI at it, so nobody has.
State privacy and insurance regulations don't leave room for "we were careful." The data needs to stay structurally protected, not just handled carefully.
Anomalies across a book of business are hard to catch by hand, and the tools that could catch them automatically aren't ones you can safely use today.
Fair question. Here's the answer, no dodge.
Admin controls, a zero-retention agreement, a contract that says your data won't be used to train the model. Real protections. But the model still receives policy and claims data in a readable form, every safeguard is a promise about what happens after that.
The model never receives policy and claims data in a readable form, not under a policy, by architecture. There's no promise to trust, because there's nothing exposed to begin with.
You're not alone, plenty of teams skip the enterprise conversation and use the tools they already have. Nera runs in the same kind of chat interface, nothing new to learn. Just add the one thing that was missing.
Per-seat sticker prices don't matter if you can't buy at that seat count. Here's the real annual cost to cover an 8-person team doing this actual job, sustained analytical work, not casual chat, control plane included.
Figures based on an 8-person team doing sustained analytical work. Usage estimates drawn from published heavy-workload benchmarks for comparable AI usage patterns, actual costs vary by vendor and usage pattern, confirm current rates directly with each vendor.
Even at the top of any Enterprise contract, ChatGPT and Claude still receive your data in a readable form, that's true whether you're paying $8,000 a year or $150,000. More spend buys better controls around the data. It doesn't change whether the model sees it. Full data isolation, residency, and sovereignty, the actual requirement across regulated industries and regulated markets, isn't a pricing tier anyone else sells at any price. Nera includes it by default, and doesn't charge a premium to do it.
See the full pricing page for what's included at each tier.
Nera is engaging with underwriting and risk teams evaluating exactly this use case today.