How is asking ClaimVision different from asking a chatbot?

A chatbot reads your documents when you ask and improvises an answer. ClaimVision has already turned every page of the claim into structured, checked data, so you can ask your claims data a question and the answer comes from the data, with the page attached.

Code for the facts, language for the conversation. Purpose-built claims logic extracts and checks the figures, the dates, the coverage and the endorsements; a language model lets your team question that data in plain English.

ClaimVision answering a plain-English question about the example claim from its structured claims data: with the endorsement text missing, it marks the answer for review and names its sources. — Asked whether the solar panels are covered at replacement cost before the endorsement is in the file, ClaimVision says what it can and cannot confirm, names its sources and asks for the missing pages instead of guessing.

What sits under every answer

Is asking ClaimVision the same as asking a chatbot? No.

Anyone can put a chat window on top of a pile of PDFs. The work is in what the chat window is talking to.

Every page, as structured data

The estimate lines, limits, deductibles, dates and endorsements become claim data you can sort, total and compare, not paragraphs you have to search.

Checked before anyone asks

The figures reconcile and the coverage traps are found before the file is opened, each with its clause and its dollar impact.

Asked in plain English

Your team questions the claim the way they would question a colleague, and every answer points at the page it rests on.

Example exchange

Ask ClaimVision Example claim · Vasquez · Wind/Hail · OK

Are the solar panels covered at replacement cost?

Partly. The array is covered as part of the dwelling, but the policy's solar panel limitation endorsement limits what is paid for solar panels, their components and the cost to remove and reset them. Four of the sixteen panels were damaged, so the limitation applies to that part of the estimate.

…coverage for solar panels, their components and removal and reset costs is limited as shown in the declarations, and loss of income from energy production is not covered…

Solar panel limitation endorsement · example policy

Impact on this claim $12,400 claimed for solar → $6,200 covered

Does anything else on this claim need a person's call?

Yes: the skylight. The broken skylight on the south face let water into the main bedroom. If a moisture assessment confirms mold, the policy's mold sublimit caps remediation, so request the assessment before deciding that line. Worth flagging to the family now; it changes what they should do next.

Adjuster report · interior damage

Illustration: the same questions once the policy endorsements are in the file (Vasquez example claim, fictitious demo data; names and figures are invented). Each answer comes from the claim's structured data, with the clause, the page and the dollar impact attached, and a follow-up a colleague would think to mention.

An example exchange

One question, and everything that comes back with the answer.

The clause.: Not a paraphrase: the operative sentence, highlighted in the document it came from, with the page reference.
The number.: The answer lands in dollars on this claim, and the figure is the same one the settlement will use, because it comes from the claim's data, not from a fresh guess.
The honesty.: When the data does not settle the question, ClaimVision says so and hands it to a person. Every answer, and every override of one, is kept in the AI claims audit trail.

The difference

Document search, a chatbot, structured claims data.

What comes back when you ask
You ask	Document search	A chatbot over the PDFs	ClaimVision
"Is the roof covered?"	Every page that mentions "roof". You do the reading.	A fluent summary, which may not have noticed the endorsement that overrides it.	Yes or no with basis: the clause it rests on and the settlement consequence in dollars.
"Do the numbers reconcile?"	Cannot answer; arithmetic is not retrieval.	Might compute; figures can drift between question and answer.	Line by line, from the claim's data, or flagged where it does not reconcile.
"Can I defend this in a file review?"	You keep your own notes.	The conversation scrolls away.	Every answer cited and kept in the audit trail, beside the decision it informed.

The questions claims people really ask: "Why doesn't the estimate total match the statement of loss?" "Which exclusions could apply to the interior water damage?" "Walk me through how you got to the net payable." Each one gets the answer, the policy language behind it, and the number.

For the next generation

How does a new adjuster learn from it?

They ask the questions they would hesitate to ask a senior colleague twice.
Every answer arrives with the clause and the reasoning.
So it teaches the pattern, not just this claim.

Questions about asking your claims data

Can you ask questions of claims data in plain English?

Yes. ClaimVision turns every page of a property claim into structured data before anyone opens the file, and your team can question that data in plain English. Each answer comes from the data, with the clause, the page and the dollar impact attached.

How is ClaimVision different from a chatbot over claim documents?

A chatbot reads the documents when you ask and improvises a fluent answer, which can miss the endorsement that changes it. ClaimVision has already turned the claim into structured, checked data: the figures, the dates, the coverage and the endorsements. The language model handles the conversation; the answer comes from the data.

Where do ClaimVision's answers come from?

From the claim itself. Every answer points at the page, the clause or the photo it rests on, and every answer and every human override is kept in the audit trail.

What happens when the data does not settle the question?

ClaimVision says so instead of guessing. Every recommendation shows how confident it is, and financial figures carry a gold, silver or bronze quality label, or are withheld until they can be confirmed. The adjuster decides what happens next.

Can asking ClaimVision help train new adjusters?

Yes. Every answer arrives with the clause and the reasoning, so a new adjuster learns the pattern, not just this claim's outcome, and can ask the questions they would hesitate to ask a senior colleague twice.

Ask it something you already know the answer to.

Bring a closed claim to the demo and question it the way you would question a new hire.

Book a demo See a claim walked through