Data Extraction Pipeline
Structured JSON from any unstructured text
Extract structured data from invoices, receipts, emails, forms, and PDFs at scale — outputting clean, validated JSON ready for database ingestion. Handles format variance and missing fields that rule-based parsers fail on.
GPT-6.1 Sol
for 5M tokens a month · 88% input / 12% output
Why this model
For this workload — 5M tokens a month, 88% of them input — it comes to about $14.8 a month, the lowest of the models suggested here. Its 1M-token context window holds long inputs in a single call, which matters for this kind of work.
Alternative models
Implementation tips
- 1
Define your JSON schema using JSON Schema Draft-07 and pass it directly in the system prompt — models with native JSON mode output schema-valid JSON on the first attempt 95%+ of the time, eliminating parsing failures.
- 2
Add a 'confidence' field to every extracted value: instruct the model to output 'high', 'medium', or 'low' confidence per field, and route 'low' confidence extractions to a human review queue rather than auto-ingesting them.
- 3
Process documents in parallel workers to maximize throughput — extraction jobs are embarrassingly parallel, and splitting a 10,000-document batch across 20 concurrent workers reduces wall-clock time by 20x without changing cost.