Extract structured data from any document with AI agents

The Problem

• Manual data entry from PDFs and documents is slow, error-prone, and soul-crushing work. A single invoice might have 15-20 fields to extract, and when you're processing hundreds per day, even a 2% error rate means dozens of incorrect records flowing into your systems.
• Regex-based and template-matching extraction breaks every time a vendor changes their invoice layout, a client updates their contract template, or a new document format enters the pipeline. Your engineering team spends more time maintaining brittle extraction rules than building new features.
• Unstructured data is trapped in emails, attachments, scanned documents, and handwritten forms with no systematic way to unlock it. Critical business information — order details, compliance records, customer requests — sits in formats that your systems can't ingest or query.
• Outsourced data processing through BPO firms means 24-48 hour turnaround times, quality control overhead, and costs that scale linearly with volume. Every new document type requires retraining offshore teams, adding weeks of delay to already slow processes.

How It Works

1. 1Upload a handful of sample documents to train extraction schemas. The agent learns the semantic structure of your documents — not just where fields are on the page, but what they mean — so it can handle layout variations, different vendors, and even handwritten annotations.
2. 2Define your output structure with typed fields, validation rules, and business logic constraints. Specify that invoice totals must match line item sums, that dates must be in ISO format, and that vendor names should resolve to your master vendor list.
3. 3The agent parses new documents using learned patterns combined with reasoning — it understands context, resolves ambiguities, and handles edge cases like multi-page tables, merged cells, and inconsistent formatting that would break traditional OCR pipelines.
4. 4Extracted data streams directly to your database, data warehouse, or downstream API in real-time. Failed extractions are flagged with specific confidence scores and reasons, so your team only reviews the documents that genuinely need human judgment.

Results

• 95%+ extraction accuracy across document formats, including formats the agent has never seen before. Unlike template-based approaches, the agent generalizes from examples and adapts to new layouts without requiring retraining or rule updates.
• Process thousands of documents per hour with consistent accuracy that doesn't degrade with volume or fatigue. What used to take a team of data entry specialists an entire week now completes overnight in a single automated run.
• Self-improving accuracy through a human-in-the-loop correction workflow. When a reviewer fixes an extraction error, the agent learns from that correction and applies it to all future documents — accuracy compounds over time instead of staying flat.
• Handles format variations, new vendors, and evolving document templates without manual rule updates. When a vendor redesigns their invoice or a client switches contract formats, the agent adapts automatically based on semantic understanding, not rigid templates.