Templates and recipes to build with Reducto
Browse ready-to-use, copy-pastable examples for parsing, extracting, splitting, and more.
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Split a law review article into sections and extract any part
A law review article is long and highly structured: an abstract, a table of contents, and dozens of numbered sections, often across a hundred or more pages. Pulling one piece, say the abstract, first means finding where it lives before you can read it. With Reducto you parse the whole article once, split it into its constituent parts, and then run a targeted extract against only the pages that hold what you want. Three endpoints, one pipeline: Parse turns the PDF into clean structured text, Split maps the article into its sections, and Extract pulls the exact field you asked for with citations back to the source.
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Parse any healthcare document into clean, structured text
Healthcare documents are some of the messiest inputs in any pipeline. A single patient's file might include a handwritten intake form, a scanned chart with checkboxes and body diagrams, a casualty card filled out under pressure, and a lab report full of dense tables, each laid out differently and often photographed or faxed. Retyping that by hand is slow, error prone, and does not scale. Reducto parses the entire document in one call and returns it as clean, structured text, handwriting transcribed and tables preserved, so your code or your model works with the data instead of the scan.
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Parse a loss run report into clean claims tables
Loss run reports are how carriers report claims history, and every one is laid out differently across dozens of pages of dense tables. Reducto parses the whole report in one call into clean, structured tables you can load straight into code.
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Pull every field from a handwritten property loss form
First notice of loss forms come in handwritten, dozens of fields packed into a tight grid with corrections scribbled over the originals. Reducto reads the handwriting, skips the crossed-out edits, and returns every field as structured JSON with a citation on each value.
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Turn a brokerage statement into structured account data
Brokerage statements bury account numbers, types, and balances in dense, multi-account tables that look different at every firm. Reducto parses those tables cleanly, then returns every account as structured JSON with a citation on each value.
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Reconcile every transaction on a bank statement
Bank and brokerage statements arrive as PDFs and scans that spreadsheets can't read. Reducto's Extract turns one into a clean, typed list of every transaction, ready to load into a ledger, dashboard, or audit trail.
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Extract structured data from patient intake forms
Patient intake forms arrive as scans, faxes, and phone photos, every one laid out differently. Reducto's Extract reads them the way a nurse would, pulling demographics, insurance, and medication history into clean JSON with a source citation on every field.
Reducto + Databricks: a friendly parsing comparison
Nine real cases where Reducto's parsing and Databricks ai_parse_document diverge, from spreadsheets and charts to signatures, tracked changes, and handwriting, and why those last-mile details decide whether downstream AI lands the right answer.
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Pull every redline from a contract
Redlined contracts are exactly the long-tail complexity that breaks template-based pipelines. Reducto reads every strikethrough, underline, and annotation as structured tags, so your team surfaces all 500+ revisions in code instead of page by page.
