Every source file stays connected to the claim. PAi Claims records what the file is, where it came from, how it was read, how confidently it was read, and whether independent readings agree.
The goal is simple: information should not enter the claim merely because an AI model said it saw something.
PAi Claims does not begin by asking AI to summarize a folder of documents.
The original files become part of the claim workspace first. For a new insurance claim, the policy packet is required before the claim workspace is completed.
Documents are stored with the claim so later evidence records point back to files that actually travel with that claim.
When a document is attached, PAi Claims copies the file into the claim workspace before building evidence from it.
A cryptographic content hash is recorded for the stored file. That gives the system a way to recognize identical content even when the second copy has a different filename.
This matters because evidence strength should come from independent support, not from counting the same document twice.
The ingestion engine does not send every document straight to a language model.
It first tries to obtain text deterministically from the file itself.
Plain text, structured text, HTML, spreadsheet-style delimited text, DOCX and ODT content can be read directly from their stored document data.
PAi reads the PDF's embedded text layer page by page and preserves page boundaries so later values can be traced back to the page where they appeared.
Pages without enough usable embedded text can be rendered and passed through OCR. OCR confidence remains attached to the resulting reading.
A mixed policy packet may contain twenty clean digital pages and one scanned endorsement.
PAi Claims handles the document page by page. Clean text can remain readable while the scanned page is separately marked as requiring OCR or another reading method.
The ingestion record keeps track of the page count, which pages required OCR, the reading method, confidence information, and whether anything still needs visual review.
OCR reconstructs characters from pixels. That makes it useful, but fundamentally different from reading text that was embedded directly in the original document.
PAi Claims keeps that distinction.
When AI is available, PAi Claims can ask the model to perform a second reading.
The task is intentionally narrow: reproduce the document text as read. The model is not being asked to improve it, summarize it, fix spelling, normalize numbers, or decide what the document probably meant.
The characters supplied directly by the digital file when available.
A reconstruction from the page image, carrying OCR confidence information.
An additional reading used for comparison, not permission for the AI to rewrite the source.
PAi Claims reconciles independent readings of the same page.
When readers agree, that agreement can strengthen the resulting text. When they disagree, PAi does not resolve the problem by asking which value looks more reasonable.
This is especially important for numbers: policy limits, deductibles, dates, form numbers, quantities, invoices and dollar amounts.
An unresolved reading remains unresolved.
The user can be shown the conflicting values, the page where the conflict occurred, and the surrounding context needed to locate it.
A human confirmation resolves the immediate reading problem, but the confirmation remains identified as a direct human check rather than being magically promoted into multiple independent sources.
Sometimes neither a usable text layer nor OCR can read a page.
A local model can still help transcribe or describe that image. But PAi Claims does not treat a model-only transcription the same way as a directly readable document.
This is where ingestion becomes more than document storage.
If readers disagree on unresolved text, or a model-only page is still waiting for human review, the estimation-readiness check can identify those pages as blocking.
The idea is simple: do not perform arithmetic on a number nobody has actually established.
The ingestion result does not end as anonymous text.
PAi Claims can retain information such as:
The current ingestion pipeline can directly handle common claim and business-document formats including:
| Source | Primary reading method |
|---|---|
| Embedded text first; scanned pages can fall back to OCR. | |
| PNG, JPG, TIFF, BMP, WebP and other common images | OCR where available; visual transcription can be requested when needed. |
| DOCX | Document text extracted directly from the stored file structure. |
| ODT | Document text extracted directly from the stored file structure. |
| TXT, Markdown, logs, JSON, XML, YAML | Direct text reading. |
| CSV and TSV | Direct delimited-text reading. |
| HTML | Readable text extracted while decoding document entities. |
Modern AI can process an enormous amount of claim material very quickly.
That power becomes much safer when the AI is reasoning over a record whose source, reading method, confidence, conflicts and provenance already exist outside the model.
The AI can then compare, organize, analyze, research and draft against the claim record while the evidence architecture remains responsible for what is allowed to become established.
The complete claim lives here because the evidence never loses its connection to where it came from.