Ertas for Data Extraction

The Challenge

Every organization drowns in unstructured documents. Invoices arrive in dozens of vendor-specific formats. Regulatory filings follow templates that change with every reporting cycle. Insurance claims, medical intake forms, shipping manifests, and legal contracts all contain critical structured information trapped inside PDFs, scanned images, and free-text fields. Traditional OCR and rule-based extraction systems are brittle — they break whenever a vendor changes their invoice layout or a form adds a new field. Maintaining hundreds of extraction templates is a full-time job that never ends.

Generic AI models can handle simple extraction tasks out of the box, but they struggle with domain-specific formats. They confuse 'invoice date' with 'due date' on non-standard layouts, misparse multi-line address fields, and fail to extract nested table structures common in financial and regulatory documents. Accuracy on edge cases — the 20% of documents that generate 80% of the manual correction workload — remains stubbornly low without domain-specific training. And for organizations handling sensitive documents like medical records, tax filings, or legal contracts, sending these documents to a third-party API for extraction creates unacceptable data exposure risks.

The Solution

Ertas lets data engineering teams build extraction models that are trained on their actual document formats and run entirely within their own infrastructure. Using Ertas Studio, teams can fine-tune foundation models on annotated examples of their specific document types — invoices with field labels, forms with extracted key-value pairs, reports with structured output mappings — using LoRA adapters for efficient, iterative training. As new document formats appear, teams simply add annotated examples and run a lightweight fine-tuning cycle, rather than building fragile template rules from scratch.

Deployment via Ertas Cloud provides private inference endpoints that integrate into existing document processing pipelines. Documents flow in, structured JSON flows out, and the entire process runs on your own servers. Ertas Hub enables teams to share extraction adapters across departments — the finance team's invoice model, the HR team's resume parser, the legal team's contract extractor — creating an organizational library of document intelligence that improves over time. Ertas Vault ensures that all training documents and extracted data are encrypted, access-controlled, and retained according to your data governance policies.

Key Features

Example Workflow

A logistics company processes 15,000 invoices per month from 300 different vendors, each with a slightly different layout. The finance operations team annotates 5,000 representative invoices — marking vendor name, invoice number, line items, quantities, unit prices, tax amounts, and payment terms — and exports them as a JSONL dataset to Ertas Vault. In Ertas Studio, the team selects a Mistral-7B base model from Hub and fine-tunes a LoRA adapter specifically for invoice field extraction. After three hours of training, the model is deployed as a private Cloud endpoint integrated into the company's accounts payable workflow. Incoming invoices are automatically routed to the endpoint, which returns structured JSON with all extracted fields and confidence scores. Invoices with high-confidence extractions (85% of volume) flow straight into the ERP system for payment processing, while the remaining 15% are flagged for human review with the model's extraction pre-filled for quick correction. Manual data entry is reduced by 80%, processing time drops from 5 days to same-day, and the team periodically adds corrected edge cases back into the training set for continuous improvement — all without any vendor invoice data leaving the company's infrastructure.