Latest news: announcing our new RAG feature

The document AI that delivers 100% automation

Extract data from any document type with 95%+ accuracy, reduce processing time by 80%, and eliminate manual data entry.

Start For Free with 500 pages • Cancel Anytime

Continuous Learning Platform

Harness the power of RAG for your document processing needs

Retrieval-Augmented Generation

Combine the power of retrieval systems with generative AI to produce more accurate, relevant, and factual outputs for your documents.

Document Knowledge Base

Build a secure, searchable repository of document knowledge that grows and adapts with your business.

Continuous Improvement

AI models that learn from user feedback and corrections to constantly improve extraction accuracy.

Calculate your ROI

See how much time and money you can save by automating your document processing

Number of documents per month

100

1000

10,000

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Average processing time (minutes)

1 min

5 min

30 min

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Time Saved

X /month

Cost Savings

X €/month

ROI

X %

Cost saved per € invested

X

Perfect for a wide range of document types

Our AI can process virtually any document type with exceptional accuracy

Ready to transform your document processing?

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Frequently Asked Questions

Everything you need to know about our document AI platform

What is RAG in document processing?

Retrieval-Augmented Generation (RAG) is an AI technique that enhances document processing by combining information retrieval systems with generative AI. It allows the system to look up and incorporate relevant information when processing documents, leading to more accurate and contextualized results.

Are RAG models immune to hallucinations?

No, while RAG models are more grounded than standard LLMs, they can still hallucinate—especially when the retrieved documents are off-topic or the prompt isn’t specific enough.

What causes hallucinations in RAG models?

Hallucinations in RAG models often stem from poor retrieval quality, incorrect synthesis by the generator, or overconfidence in outputs that aren’t grounded in the retrieved documents.