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AI ANALYZE API

A little intelligence.
A lot more clarity.

Turn unstructured text into well-defined labels, extracted fields, and reviewable summaries. Start with the question. Keep the evidence close.

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WHAT GOES IN

The right input.
The right question.

Supplied text, documents, and a task-specific rubric.

KEEP THE SCOPE CLEARExtraction is not verification. A field can be correctly extracted from a document that contains an inaccurate claim.

WHAT COMES OUT

A report you can use.

  • Extracted fields with source evidence
  • Classification labels and review notes
  • Task-focused summaries and unresolved questions

Keep the evidence, interpretation, and next action separate. Document missing inputs instead of filling them with guessed values.

A PRACTICAL WORKFLOW

Organize a content review queue

A team receives product descriptions in different formats. Define the fields that matter, extract only supported details, and route incomplete records for review instead of filling in the blanks.

Explore integration patterns

LOOK INSIDE THE OUTPUT

Evidence, not a mystery score.

An illustrative report for this workflow. Download the JSON to inspect the complete structure.

THE ANALYSIS PLAYGROUND
Interactive sample

Text extraction example

Sample product description

Extract what is present. Leave missing values unresolved.

✓
A product name was extracted

The sample text explicitly names Sample Notebook.

✓
The category is supported

The description calls the item a paper notebook.

!
The material is unspecified

No cover material appears in the provided description.

report.json
{
  "schema_version": "1.0",
  "report_id": "sample-ai-analysis",
  "sample": true,
  "analysis_type": "ai-analysis",
  "status": "complete",
  "target": {
    "label": "Sample product description",
    "input_kind": "Text extraction example"
  },
  "summary": "Extract what is present. Leave missing values unresolved.",
  "findings": [
    {
      "id": "f01",
      "outcome": "pass",
      "title": "A product name was extracted",
      "evidence": "The sample text explicitly names Sample Notebook.",
      "action": "Retain the original source span."
    },
    {
      "id": "f02",
      "outcome": "pass",
      "title": "The category is supported",
      "evidence": "The description calls the item a paper notebook.",
      "action": "Use the approved stationery label."
    },
    {
      "id": "f03",
      "outcome": "review",
      "title": "The material is unspecified",
      "evidence": "No cover material appears in the provided description.",
      "action": "Leave the field unknown and ask for evidence."
    }
  ],
  "not_measured": [
    "Independent claim verification",
    "Model accuracy across unseen inputs"
  ]
}
Illustrative data. No live analysis or uploads.Download JSON Explore workflow

TAKE IT FURTHER

A little context goes a long way.

Turn prompt changes into focused experiments. Compare paired cases and catch the regressions an average can hide.

Read the full guide

Before you connect a real source

Confirm that you are authorized to use the input. Preserve its collection context and version, agree on an acceptance rule, and decide how incomplete results should be reviewed.

  • Validate the report before consuming it.
  • Keep secrets out of public browser code.
  • Show what was—and was not—measured.

Make your next move
a clearer one.

Explore the workflows. Inspect the evidence. Build something useful.

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