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Generic AI is built to sound right. Qumis is built to be right — and to prove it. The Qumis coverage intelligence platform delivers attorney-trained AI for insurance policy analysis, with citation-linked outputs and proprietary market data from thousands of commercial programs.
Trusted by 5 of the top 15 U.S. insurance brokerages
Insurance policy analysis isn't basic document search. It requires understanding how exclusions interact with definitions, how endorsements modify coverage, and how sublimits affect recovery. ChatGPT and other generic tools weren't designed for any of that — and on insurance work, the gaps create real exposure.
Generic AI produces fluent, authoritative-sounding answers even when the policy language doesn't support them. In insurance, a confident wrong answer is an E&O event.
ChatGPT outputs are ephemeral and untraceable — there's no defensible record of how a coverage conclusion was reached. That's a problem the moment a claim, regulator, or carrier asks.
Generic AI treats a policy as undifferentiated text. It can't distinguish exclusions from conditions or endorsements from definitions — the very structure that determines whether coverage applies.
Six structural differences between a tool built on the open web and a coverage intelligence platform built by attorneys for commercial P&C.
I tested it with real-world coverage disputes, and it consistently delivered thoughtful, well-reasoned responses.
Building a chatbot on the API is a weekend project. Building a platform that delivers legal-grade accuracy with citation-linked outputs, multi-agent validation, insurance-specific taxonomies, and proprietary market benchmarking is a multi-year, multi-million-dollar effort — and there are pieces no in-house team can replicate at any price.
The interpretive frameworks inside Qumis come from practicing coverage counsel, encoded in software. That domain depth doesn't ship with a foundation model — and most IT teams don't have it on staff.
Citation-linked outputs aren't a prompt; they're an architectural decision that has to be designed in. Bolting traceability onto a generic LLM after the fact rarely produces something defensible.
Qumis's multi-agent AI architecture mirrors actual legal review processes — including the safeguards that say "I don't know" when evidence is insufficient. That's how we keep hallucination out of the answer.
A proprietary database of thousands of commercial insurance programs isn't something you can scrape or license. It's accumulated, structured, and benchmarked over years — and every analysis Qumis runs is informed by it.
Qumis is the platform of choice for the industry's most demanding users — and the outcomes show up on the bottom line, not just in time saved.
On production coverage workflows.
Of teams that run a Qumis pilot become paying customers.
Additional claims captured by one senior claims advocate (top-5 regional broker).
Enterprise-grade data security, passed by 5 of the top 15 U.S. brokerages.
See Qumis on your own policies. A focused 30-minute walkthrough on the use cases that matter most to your team — policy review, claims, renewals, or coverage comparison.
30 minutes. No prep required.