Comparison

Generic LLM vs IDP vs reinsurance operations software

Three different jobs. Treating them as one category is how you buy a chatbot and still re-key bordereaux.

Three different jobs get sold as one category: a generic large language model, template intelligent document processing, and source-grounded reinsurance operations software. Treating them as interchangeable is how you buy a chatbot and still re-key bordereaux.

This page expands the comparison in prose. There is no table, because a table invites fake win rates. There are no accuracy percentages. If you need the definitions first, use what is IDP in insurance?, insurance AI versus ChatGPT, the parent reinsurance AI page, and insurance AI. The field contract for the third job is source-grounded extraction.

Generic LLM

A generic model completes text. You paste a slip, a wording, or a bordereau screenshot into a chat window, or you wrap the same model in an internal assistant. It will summarise fluently. It will answer questions 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.

Missing fields are the failure mode. The honest behaviour is to say the hours clause is not in the file. The typical behaviour is to invent it. That is why a treaty summary chat is the wrong product for administration: exclusions drop out, reinstatement formulae become the word reinstatement, and binders argue about what was said.

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.

A generic LLM can sit inside operations software as a proposer of spans. On its own it is a writing tool. Do not buy it as bordereaux infrastructure.

Template IDP

Intelligent document processing classifies files and pulls fields from known templates. It is the right tool for high-volume, stable layouts: invoices, ACORD-like forms, a coverholder bordereau that truly never changes columns. What is IDP in insurance? is the definition this site uses.

Reinsurance packs are not stable. Slips vary by broker and year. SOVs are workbooks with extra rows and merged headers. Treaty wordings are manuscript plus endorsements. Bordereaux insert a column without warning. Template IDP then does one of two things: it fails the document, or it silently maps the wrong column because "Pol Ref" moved. Empty is better than wrong. Silent wrong is how you book a total you cannot unwind.

A missing field in classic IDP is often an empty cell, which is closer to a gap than a chat invention. The weakness is the map. When the template does not fit, you do not get a chase list that names hours clause. You get a low extract rate and a queue of unclassified PDFs. Conflicts between two documents in one zip are out of scope unless you built a second product on top.

The data path is usually enterprise: files stay in a processing pipeline you already trust for invoices. That is a real advantage for accounts payable. It is not an argument that the same template engine understands a London slip. The gap this product targets is messy packs, not a replacement for every IDP licence in the enterprise. Keep IDP where layouts are stable. Do not force a facultative zip through an invoice model and call it placement.

Source-grounded reinsurance operations

The third job is operations software for the B2B chain between brokers, cedents, and reinsurers. Inbox or folder in. Pack out. Each field carries a document, page or cell, and span. Gaps are empty values plus chase items. Conflicts stay as two spans. Unverifiable scans are refusals, not brave guesses. Files stay in the tenant. The marketing site uses a sample pack only.

Layouts are messy on purpose: mixed PDFs, workbooks, forwarded threads, endorsement scans. The product is not "we accept any file and always emit a complete sheet." The product is "we emit only what we can point at, and we list the rest." That is slower to demo than a chat that fills every box. It is the only behaviour that survives a loss.

The data path is tenant-scoped ingest, typically a read-only mailbox grant, not a public paste box. Write-access to send mail as the user is a different boundary. Operators review evidence. They do not re-key from a summary. Humans still triage, set technical price, and quote. The software does not bind a layer because a model was confident.

This is the job described on reinsurance AI and the extraction contract on source-grounded extraction. It is not generic insurance chat, which is why insurance AI versus ChatGPT exists as a separate answer, and why insurance AI is a wider cluster than this comparison.

The same file, three behaviours

Use one fictional pack in all three products. ACME Construction Ltd, acme.example. Slip TIV USD 42,000,000. SOV TIV USD 47,100,000. Hours clause absent. Limit USD 10,000,000 on slip page 2.

A generic LLM will usually return a TIV and a hours clause in a paragraph. It may mention that sources differ, then still pick a number. It will not store two TIV spans as first-class objects. It will not leave hours clause empty unless you bully the prompt, and the next session will forget the bullying.

Template IDP will often classify one PDF, miss the workbook, or map a "limit" field from a header that is not the occurrence limit. If the slip template was trained on a different broker layout, the extract is empty or shifted. You do not get a chase list that says hours clause. You get an unclassified file or a thin record.

Source-grounded ops should emit a traced limit with a page-2 span, a TIV conflict with both sources, and a hours-clause gap. That is the behaviour to buy. If a vendor cannot show that on a sample, the comparison is over, regardless of which category they claim.

Bordereaux show the same split. A generic model summarises the sheet and invents a total. Template IDP maps last quarter's columns and silently follows a shifted "Pol Ref." Operations software maps cells, validates against cited treaty terms, and lists rows that have no span into the wording. Re-keying continues until you have the third job, not until the chatbot sounds more insurance-native.

How to choose without a scorecard

If your documents are stable forms and you already run IDP, keep running it on those forms. If your problem is a wording you want explained in English, a generic model will explain it, and you must not book from the explanation. If your problem is a mailbox of slips, SOVs, and bordereaux that disagree with each other, you need packs, spans, and a chase list.

Mystery-shop all three with the same fictional pattern this site uses. ACME Construction Ltd: two TIVs, no hours clause. The generic model will likely give you a TIV and a hours clause. Template IDP will likely miss the zip or map one PDF. Operations software should show a TIV conflict and a hours-clause gap. That outcome, not a marketing percentage, is the test.

Do not ask any of the three for a savings figure as a substitute for that test. Do not ask this page for a win-rate table. The three-way split is the honest comparison: unsourced fluency, template maps, or source-grounded packs. Only the last one is reinsurance operations software.