File intelligence API: why companies are replacing their file agents with one API call

Every AI company builds the same thing: a file agent. It receives a PDF, spreadsheet, or URL. It routes to the right parser. It extracts fields. It validates. It reasons over the content. It handles errors. It costs six months of engineering and breaks on the 47th file format.

That's not a differentiated product. That's plumbing. And in 2026, it's plumbing you can replace with a single API call.

What is a file intelligence API?

A file intelligence API is not a document parser. Parsers convert files to text. File intelligence understands files — it extracts structured data, reasons over content, computes derived answers, cross-references multiple documents, and returns typed JSON with confidence scores and citations.

The difference:

The three levels of file understanding

Most APIs stop at level one. Production workflows need all three:

Level 1: Extraction — read what's written

Pull literal field values from any file. Vendor name, invoice total, contract dates. This is what every document extraction API does.

POST /api/v1/extract
{
  "file": "quarterly_report.pdf",
  "schema": {
    "revenue": {"type": "number", "description": "Total revenue"},
    "ceo_name": {"type": "string", "description": "CEO name"},
    "fiscal_year": {"type": "string", "description": "Fiscal year"}
  }
}

1 credit per page. Fast. Handles 107+ formats including scanned documents.

Level 2: Analysis — reason over content

Compute answers that aren't written anywhere in the document. Growth rates, cross-checks, derived conclusions. The API performs multi-step reasoning with a full computation engine.

POST /api/v1/analyze
{
  "file": "quarterly_report.pdf",
  "schema": {
    "yoy_growth": {"type": "number", "description": "Year-over-year revenue growth rate"},
    "profitable": {"type": "boolean", "description": "Is the company profitable this quarter?"},
    "strongest_segment": {"type": "string", "description": "Fastest growing business unit"}
  }
}

2 credits per page. Returns reasoning traces, source citations, and confidence scores.

Level 3: Cross-analysis — reason across multiple documents

This is what no other API offers. Compare information across 2, 5, or 20 documents simultaneously. Find contradictions. Track changes. Reconcile data from different sources.

POST /api/v1/cross-analyze
{
  "files": ["contract_v1.pdf", "contract_v2.pdf", "amendment.pdf"],
  "schema": {
    "changed_clauses": {"type": "array", "description": "Clauses that differ between versions"},
    "contradictions": {"type": "array", "description": "Terms that conflict across documents"},
    "effective_terms": {"type": "object", "description": "Final binding terms after all amendments"}
  }
}

5 credits per document + 3 per page. Handles the reasoning your agent would otherwise need dozens of tool calls to perform.

Why you don't need to build a file agent

Here's what a typical "file agent" does internally:

1. Detect the file format
2. Route to the right parser (PDF, DOCX, XLSX, images, etc.)
3. Extract raw text or tables
4. Map extracted content to a schema
5. Validate and cross-check values
6. Compute derived answers
7. Return structured output with confidence

That's six months of engineering. Format detection breaks on edge cases. Parsers return inconsistent output. Schema mapping is fragile. Validation logic is hand-coded per document type. Computation requires custom code per field.

With a file intelligence API, all seven steps are one API call. Your agent sends the file and a schema. It gets typed JSON back. The API handles format detection, parsing, OCR, extraction, reasoning, and validation internally.

Your agent doesn't need to be a file agent. It needs to call one.

Real example: financial due diligence

A due diligence workflow without file intelligence:

• Parse the 10-K (500 pages) with a PDF library
• Parse the balance sheet from XLSX
• Parse the investor presentation from PPTX
• Write custom code to compute growth rates from the 10-K
• Write custom code to cross-check the 10-K revenue against the spreadsheet
• Write custom code to validate the presentation claims against the filing
• Handle 3 different file formats, 3 different parsers, 3 sets of bugs

The same workflow with file intelligence:

# One call: analyze the 10-K
financials = client.analyze(file="10k.pdf", schema={
    "revenue": ..., "yoy_growth": ..., "debt_ratio": ...
})

# One call: cross-reference all three documents
verification = client.cross_analyze(
    files=["10k.pdf", "balance_sheet.xlsx", "investor_deck.pptx"],
    schema={
        "revenue_consistent": {"type": "boolean",
            "description": "Does reported revenue match across all docs?"},
        "discrepancies": {"type": "array",
            "description": "Any contradictions between documents"}
    }
)

Three API calls replace six months of pipeline engineering. The API handles format differences, cross-page reasoning, and multi-document comparison internally.

Who this replaces

File intelligence APIs eliminate the need for:

• Custom file agents — the API is the agent
• Format-specific parsers — 107+ formats handled internally
• Validation pipelines — confidence scores and cross-checks built in
• Multi-document orchestration — cross-analyze handles it in one call
• OCR infrastructure — scanned documents processed automatically

FAQ

Q: How is this different from sending files to an LLM directly?

LLMs have context window limits (can't handle 500-page filings), don't enforce output schemas, hallucinate computations, and provide no confidence signals. A file intelligence API uses LLMs internally but adds document navigation, computation engines, citation tracking, and typed output enforcement.

Q: What file formats are supported?

107+ formats including PDF, DOCX, XLSX, PPTX, CSV, images (JPG, PNG, HEIC, WebP), video, audio, and any URL with JavaScript rendering. See the full list.

Q: How does cross-analysis handle contradictions?

The API reads all documents, identifies corresponding claims, and flags where values or terms differ. It returns the specific contradiction, the source in each document, and a confidence score for the finding.

Q: What does this cost?

Extract: 1 credit/page. Analyze: 2 credits/page. Cross-analyze: 5 credits/document + 3/page. One credit = $0.01 on Pro. Free tier: 100 credits/month.

Try it yourself — free tier included, no credit card required.