What is the difference between insurance AI and ChatGPT?

ChatGPT is a general language model. Insurance AI, as used here, is workflow software: field extraction, validation against a pack, and an audit trail. You can wrap a general model in a product, but the product still has to refuse unsourced numbers.

A general model will answer fluently when the slip is ambiguous. That is the failure mode in placement and treaty ops. Helpful, in a treaty, is dangerous. A missing deductible should be empty. A conflict should stay a conflict. Fluency without a span is how a wrong attachment gets quoted.

What is insurance AI? is the parent definition: extract, validate, pack, then a human decides. This page is the contrast with a chat window.

What a general model does

You paste a slip, a wording, or a bordereau screenshot into a chat, or you wrap the same model in an internal assistant. It will summarise. It will answer in complete sentences. It will often fill a missing hours clause with a market-standard number because that is what completion does when the page is silent.

Layouts are not a constraint, which sounds like a strength. Anything can be pasted. Nothing is sourced. The limit that comes back is a string in a paragraph, not a field with a document, page, and span. When the slip and the SOV disagree on TIV, the model picks one figure or blends them. When the PDF is a scan, it still sounds sure.

The data path is usually a vendor endpoint, or a workspace that still is not your treaty administration system. Even when the vendor is serious about tenancy, the object you get is a conversation, not a pack. There is no chase list unless you prompt one into existence every time. There is no conflict object. There is no audit of who accepted a span.

What insurance operations software does

Source-grounded extraction is the field contract. Each emitted value points at a document, a page or cell, and a character span. No span, no value. Gaps stay empty and become chase items. Conflicts stay as two spans. Files stay in the tenant. The marketing site uses a sample pack only.

A generic model can sit inside that loop as a proposer of spans. On its own it is a writing tool. Do not buy it as bordereaux infrastructure, and do not paste a live wording into a public endpoint and call it operations.

Reinsure-8B is still a model

Reinsure-8B is a domain model you can run in a tenant. A public LLM is a third-party endpoint. Neither is a system of record. Pair the model with provenance or you are back to unsourced summaries with better jargon. Domain language is not a pack.

Worked example: ACME Construction Ltd

Paste the fictional ACME Construction Ltd pack, acme.example, into a general model and ask for the TIV and the hours clause. Slip.pdf page 2 says TIV USD 42,000,000. SOV.xlsx totals USD 47,100,000. Hours clause is not in the file. Occurrence limit USD 10,000,000 is on slip page 2 and is not in dispute.

You will often get a fluent sentence. You may get a blended TIV. You may get 72 hours because that is common on construction occupancies. That is not underwriting support. That is hiding a conflict and inventing a clause.

Insurance AI, in this product, stores both TIV spans, leaves hours clause empty, and puts the gap and the conflict on a chase list. A person still decides whether the warehouse is on cover. The chat window is not that product, even when the answers sound insurance-native.