Skip to main content
Use extraction when you want fields, not just content.

​
When extraction is the right tool

Extraction is a strong fit for:
  • company and product profiles
  • pricing or policy extraction
  • structured enrichment for downstream systems
  • repeatable data collection from public pages

​
Design the request carefully

Good extraction quality usually depends more on request design than on retry count. Keep the request:
  • narrow enough to be realistic
  • specific about what should be extracted
  • backed by a schema that downstream systems can actually use

​
Request examples

curl "$BULKGRID_BASE_URL/api/v1/extract" \
  -H 'Content-Type: application/json' \
  -H "x-api-key: $BULKGRID_API_KEY" \
  -d '{
    "urls": [
      "https://example.com",
      "https://example.com/pricing"
    ],
    "query": "Extract company name, product summary, and pricing details",
    "schema": {
      "type": "object",
      "properties": {
        "companyName": { "type": "string" },
        "productSummary": { "type": "string" },
        "pricing": { "type": "string" }
      },
      "required": ["companyName"]
    },
    "maxRetries": 3
  }'

​
Workflow

  1. submit the extraction request
  2. store the run ID
  3. poll GET /api/v1/runs/{runId}
  4. fetch GET /api/v1/runs/{runId}/results
  5. read extraction_data from the result records

​
Common quality problems

  • the schema asks for data the source does not contain
  • the query is too broad
  • the page requires interaction or access patterns the request does not account for

​
Recommendation

Start with the smallest schema that delivers value. Expand later once the output is stable.