Automated Document Extraction
Stop paying humans to do data entry. We build AI pipelines utilizing advanced OCR and multi-modal LLMs to instantly extract critical data (names, dates, line items, totals) from unstructured invoices, legal contracts, and medical records, feeding them directly into your database.
Core Features
Layout-Aware Parsing
Traditional OCR destroys the layout of a document. We use vision models that understand columns, tables, and complex formatting in PDFs.
Key-Value Extraction
Using LLMs to read the messy text and extract specific data points (e.g., 'Vendor Name', 'Total Amount') regardless of where they appear on the page.
Handwriting Recognition
Integrating advanced models to accurately transcribe handwritten notes, forms, and signatures on scanned documents.
Confidence Scoring & Human-in-Loop
The AI flags low-confidence extractions (e.g., a blurry total) and routes only those specific documents to a human for review.
Our Process
Document Auditing & Schema Definition
Week 1Analyzing the variety of documents you receive (e.g., 50 different invoice formats) and defining the exact JSON schema we need to extract.
OCR & Vision Pipeline Setup
Week 2-3Implementing layout parsers (Unstructured.io, AWS Textract) to convert PDFs and JPEGs into machine-readable markdown or text blocks.
LLM Extraction Engineering
Week 4Writing the strict prompt chains and utilizing models like GPT-4o or Claude 3.5 Sonnet to accurately extract the target fields from the OCR text.
Confidence & Routing Logic
Week 5Building the middleware that assigns a confidence score to the extraction. High confidence goes to the database; low confidence goes to a human queue.
Database & ERP Integration
Week 6Connecting the pipeline output directly to your ERP, CRM, or custom database, completely automating the data entry process.
Technologies We Use
FAQ
Why is this better than traditional OCR template software?
Can it extract line items from complex tables?
What happens if a document is blurry or unreadable?
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