Parsewise

Most parsers stop at the edge of a single file. Parsewise connects the same person, account or figure across every document in a package, which is what real multi-document analysis requires. That matters when a KPI is stated one way in a summary and another way in the supporting spreadsheet, because a single-document tool would report both numbers without noticing they conflict. The platform catches those mismatches and shows both sources side by side, so an analyst sees the disagreement instead of inheriting it. Big difference.

Every extracted value ties back to word-level coordinates in the original page. API endpoints return those coordinates, so you can build a review screen that highlights the exact source region. For a claims or underwriting analyst, that's the difference between trusting a number and being able to defend it when a manager or a regulator asks where the figure came from and why it should be believed.

You don't write extraction rules in code first. You ask Navi in plain English, and it auto-generates the custom agents needed for the job. The same assistant handles quick analysis, document summaries and portfolio analysis. Simple. It's as easy to use as a chat tool, which lowers the bar for business users.

Results come back as JSON, CSV or Excel. Beyond raw data, Parsewise fills DOCX, PDF and XLSX templates deterministically, so a report template comes out populated rather than half-formatted and you skip the error-prone copy-paste stage that usually follows an extraction run. Anyone who has spent an afternoon copying extracted figures into a report template by hand knows exactly how much time that single feature can return over a busy quarter.

Agents can write and run code, and search the web to backfill missing values or verify what they pulled. This helps with gaps, like when a document references a figure it never states outright. Not every field in a real dossier is spelled out. This feature covers some of those holes, which matters because a missing value that gets silently guessed is worse than one that gets flagged for a human to check.

Once a corpus is processed, you can run follow-up questions against it without re-ingesting or re-extracting anything. Investigations rarely end after the first pass. Being able to query the same processed set again means a second round of questions doesn't cost another round of processing, which keeps a long investigation from turning into a long processing bill every time a new angle appears.

Parsewise encrypts data at rest and in transit and states it doesn't train on your data. Enterprise plans add VPC and on-prem deployments, bring-your-own-models, regional data residency and SSO with SAML. That's a lot. For regulated teams, that set of controls is often the deciding factor.