Data Extraction Services
Production data extraction services that turn complex PDFs and documents into structured JSON with 100% accuracy. Built on AWS Textract, OpenAI, LangChain, and strict Pydantic schemas with a human-in-the-loop fallback.
Why most data-extraction projects fail (and how I fix it)
Pure OCR misses fields. Pure LLM hallucinates. The reliable answer is a layered pipeline: OCR for spatial truth, LLM for semantic normalization, schemas for guarantees, and a human in the loop for the edge cases. That is the architecture I ship — and it is why my clients trust the extracted data downstream without a second review.
The pipeline architecture
1. Layout-aware OCR
AWS Textract pulls text, tables, and forms with bounding boxes — preserves spatial relationships that matter on invoices and reports.
2. LLM normalization
OpenAI / Claude reads the raw OCR + structured layout and emits a normalized record matching your target schema (dates, currencies, addresses, vendor names, line items).
3. Schema validation
Strict Pydantic schemas with custom validators catch type errors, missing fields, impossible values, and inconsistencies (e.g. line items not summing to the invoice total).
4. Human-in-the-loop fallback
Anything that fails validation lands in a review queue with the original document, the extracted draft, and the failure reason. Reviewer fixes once; the pipeline learns the pattern.
5. Audit log
Every extraction logs the OCR text, the prompt, the raw LLM output, and the final validated record — for compliance and debugging.
6. Delivery
REST endpoint, n8n / Make.com workflow, or direct write to your Postgres / Snowflake / data warehouse — your choice.
Document types I handle
- Invoices and purchase orders
- Contracts (vendor, employment, lease, NDAs)
- Bank and credit-card statements
- Lab and medical reports
- Multi-column reports and research papers
- ID documents and KYC forms
- Real-estate listings and property descriptions
- Scanned forms (with handwriting via Textract Forms)
- Mixed-language documents
Engagement options
- Pilot pipeline on one document type, sample of 50-200 docs: $1,500 – $3,500 (2-3 weeks).
- Production pipeline with REST API + monitoring + human-review queue: $3,500 – $9,000 (3-6 weeks).
- Per-document cloud cost: typically $0.005 – $0.05 depending on document length and LLM complexity.
- Monthly retainer for new document types and pipeline tuning: $900 – $2,500.
Frequently asked questions
What are data extraction services?
Data extraction services turn unstructured documents — PDFs, scanned images, contracts, invoices, reports — into clean structured data (JSON, CSV, database rows) you can use in workflows, dashboards, and AI pipelines.
How do you reach 100% accuracy on complex PDFs?
A three-layer pipeline: AWS Textract for layout-aware OCR, an LLM (OpenAI / Claude) for normalization and field-level reasoning, and Pydantic schemas with strict validation. Anything that fails validation lands in a human review queue — never silently silently wrong data downstream.
Which document types do you handle?
Invoices, purchase orders, contracts, statements, lab reports, ID documents, multi-column reports, scanned forms, mixed-language documents. If it is a PDF or image with structure, the pipeline can handle it.
Can you process documents at scale?
Yes. Pipelines run async on AWS Lambda or a FastAPI worker pool, with batching, retries, and per-tenant rate limits. I have shipped pipelines handling thousands of documents per day with sub-cent per-page cost.
How do I integrate the data extraction service?
Two common patterns: (1) a REST endpoint your app calls with the file URL, gets a structured JSON response; (2) an n8n / Make.com workflow that watches a Google Drive / S3 folder and writes results to your database. I will recommend the right one based on your stack.
What does it cost?
Pilot pipeline (one document type, ~95-100% accuracy on your sample): $1,500 – $3,500. Production pipeline with API + monitoring + human-review queue: $3,500 – $9,000. Per-document cloud cost is typically $0.005 – $0.05 depending on document length and LLM complexity.
Ready to ship this for your business?
Send a 2-line message describing your stack and the workflow that costs you the most hours each week. Reply within 4 hours with a scope, timeline, and fixed quote.