Document intelligence that checks its own work
EvolveDocs reads every page of every file — scanned, unsorted, degraded — classifies and reassembles them, checks the extracted record against the documents themselves, and hands your reviewers the findings with the evidence behind each one. Fields checked against the document, not just extracted from it.
page 31 flagged — amount disagrees with the note. That is the mark doing its job.
For mortgage QC and audit
Drop a closing package in. Every page is split, recognised and classified; TRID/RESPA fields checked against the pages they came from; date sequences, amount mismatches and missing signatures flagged with page-level evidence; the exception report rolled up by defect category — the deliverable your QC process is measured on.
For litigation and review teams
Classify and reassemble the record; tag privileged, responsive and non-responsive at keyboard speed with every designation audited; redactions burned in — never merely drawn — and privileged material withheld from production sets by default, with a bates-stamped manifest you can cite.
Agency post-closing QC is built on sampling — Fannie Mae's Selling Guide requires a minimum 10% random sample of originations, or a statistical sample at a 95% confidence level with 2% precision (D1-3-01). Sampling exists because full-file review of every loan was never economically possible.
EvolveDocs reads every page of every file — and your sampled reviews start from a record that has already been checked, with the exceptions rolled up by defect category. Your QC obligations stay exactly what the Guide says they are; what changes is what reviewing the rest costs.
Review platforms are generally sized in batches — a ceiling on how many files go in at once, and a ceiling on how large any one of them can be. If that is how your current tool works, you already know the cost: a package arrives as one thing, gets split to fit, and someone has to remember what was in which batch and reconcile it afterwards.
EvolveDocs takes the package as it arrived and reports on it as one record. Every page gets a mark, the reassembly is shown rather than assumed, and anything skipped is named — the ledger above is that record for a real package, not an illustration of one. Nothing is sampled to fit a limit, because there is no batch to fit.
Compliance coverage, stated plainly: templated regime rules ship for HIPAA, SOX, GDPR and PH-DPA, with PCI-DSS detection and retention floors. Lending-compliance regimes — GLBA, HMDA, ECOA, FCRA, BSA/AML, TRID — are on the roadmap and not yet templated. EvolveDocs never relieves you of a regulatory obligation; it changes what meeting it costs.
What you see below is the product's own components rendering the sample matter — the chrome is the status line, which is why there is no dashboard page.
The rail — every stage, every page
Processing is complete — every stage is accounted for.
Collapsed panels still tell the truth
The ledger — one honest mark per page
Every document runs through five domain rule sets before findings reach a reviewer. Output is checked against document-internal evidence — not just generated.
Adversarial multi-perspective deliberation over one shared record, relationship and timeline analysis across thousands of documents, and citation-checked Q&A with closed-loop knowledge retrieval.
The difference between a platform that connects the evidence across the whole record — and shows its work — and one that retrieves documents mentioning matching keywords. Your analysis; better raw material.
Everything is scanned, split, recognised and run through the document-level checks that need no domain schema — dates that don't sequence, amounts that disagree across pages, missing signatures. On top of that, five families carry deep schemas and rule sets. A document outside them is read at general depth and says so — reduced depth is never presented as full depth.
| Family | What it reads | The depth that matters | Rule set |
|---|---|---|---|
| Mortgages | Loan packages from unsorted scans — 14 form variants | TRID/RESPA compliance fields in the schema; package reassembly into a reviewer-grade report | cross-document consistency signals + FIN-* |
| Accounting | Journal entries, balance sheets, statements | Double-entry validation, balance-sheet consistency, cross-statement reconciliation | FIN-* |
| Tax law | Returns (1040, 1120, 1120-S, 1065, 990) + IRS authorities | Authority taxonomy built in — rev_rul, rev_proc, PLR, TAM, CCA distinguished | TAX-* |
| Case law | Opinions, briefs, motions, orders — 10+ jurisdictions | Citation validation against source; quote-not-in-source; overruled-citation propagation | LEG-CL-* |
| Patents | Grants, office actions, amendments, IDS, assignments | Per-jurisdiction claim grammars — US, EP, CN, JP, KR, PCT | PAT-* |
Insurance claims, procurement contracts, HR files — read at general depth today: full ingestion, OCR, generic extraction and the family-independent rule checks, honestly labelled. Deep schemas are added family by family.
Validated documents flow back into the reference corpus, so classification gets more precise with use — no per-customer training labels, no weeks of manual setup.
Feed in a 2,500-page scanned loan package — unsorted, mixed, degraded. Get back a working report for your review: pages classified and reassembled into their logical documents, expected fields checked, cross-document inconsistencies flagged with the evidence behind each finding.
The note principal disagrees with the application, 1098 and closing disclosure.
The closing is dated before the note it closes.
01
Upload anything — scanned PDFs, office docs, email archives. Every page rendered, OCR'd, and accounted for.
02
Fused-signal classification — image similarity, text similarity, form codes, model judgement — against a reference corpus that grows with every validated document.
03
The matched type drives its domain schema: claim elements, TRID fields, IRS authorities, holdings.
04
Five domain rule layers interrogate the extracted record before a reviewer ever sees it.
05
The loan file report — every finding evidence-cited to the page it came from, every page in the package accounted for.
Every file shows the pages it metered, in the app, per file and per matter. What a run cost is a number you can read off the portfolio view, not a quote.
10,000 loan files at your average page count is a multiplication, not a negotiation. Volume pricing follows the meter.
When the meter is the work, adding a reviewer costs nothing. The incumbent charges you for the person; we charge for the pages the machine read.
Bring a matter. The platform classifies it, extracts it, interrogates it, and hands your reviewers the finished report.