extractor
Use cases

Anywhere a machine has to read something a person wrote.

Extraction is rarely the interesting part of a system — it is the part that quietly ruins the interesting part. These are the places it tends to bite.

RAGformat: markdown

Feeding a knowledge base without feeding it garbage

Chunking a raw HTML dump means embedding the nav bar, the cookie notice and the footer on every page — and then watching them surface as answers. Extractor hands the pipeline the document only, with headings intact so your chunker has real boundaries to cut on.

AgentsMCP · extract_document

A research loop that can actually read

An agent that fetches a URL gets markup. Give it the MCP tool instead and it gets the page as prose it can reason over — and because the tool reports what a document cost, the agent can budget its own reading.

Contractsformat: json + schema

Pulling the same eight fields out of a hundred PDFs

Pass a schema — parties, effective date, term, notice period, governing law — and get those fields back for each document, with nulls where the contract is silent. The nulls are the useful part: they tell you which files need a human.

FinanceOCR included

Invoices and statements that arrive as scans

A PDF with a text layer is parsed directly; a photographed receipt goes through OCR at the same price. Either way you get totals, dates and line items in the shape you asked for, and a scan that turns out to be blank costs nothing.

Competitiveformat: text

Watching pages that keep changing

Pricing pages, changelogs, docs sites. Extract on a schedule, diff the plain text, and you see what actually changed instead of a wall of markup churn from a redeploy.

Archivespptx · xlsx

A decade of decks nobody can search

PowerPoint and Excel come back as markdown — slides as sections with their speaker notes, sheets as tables. That is enough structure to index, and enough to read.

A worked example

Eight fields out of a contract.

You describe the shape you want. Extractor fills it only from text that is actually in the document, and tells you which fields the document never mentioned — because a null you can see beats a value that looks plausible and isn't there.

2 credits, whatever the schema.

contract-fields.sh2 credits
curl -X POST https://extract.ounie.com/api/extract \
  -H "Authorization: Bearer xtr_live_..." \
  -F file=@msa.pdf \
  -F format=json \
  -F schema='{
    "parties": ["string"],
    "effective_date": "string",
    "term_months": "number",
    "notice_period_days": "number",
    "governing_law": "string"
  }'

→ { "json": { "parties": ["Acme Inc.", "Globex Ltd."],
              "effective_date": "2026-04-01",
              "term_months": 24,
              "notice_period_days": null,
              "governing_law": "Delaware" },
    "missing_fields": ["notice_period_days"] }

Point it at something messy.

A URL, a PDF, a deck someone emailed you. Two credits, a couple of seconds, and text you can actually use.

No subscription — pay in Ounie credits