Document Parser by Contextual AI
Multimodal document parser designed for RAG systems
78 followers
Multimodal document parser designed for RAG systems
78 followers
A document parser designed for RAG use cases, which achieves superior accuracy and reliability by excelling in the following areas: 1. Document-level understanding 2. Minimized hallucinations 3. Superior handling of complex modalities
Free Options
Launch Team / Built With
Document Parser by Contextual AI
Hi Product Hunters,
We're the team at Contextual AI.
We're excited to announce our document parser that combines the best of custom vision, OCR, and vision language models.
There are a lot of parsing solutions out there—here’s what makes ours different:
Document hierarchy inference: Unlike traditional parsers that process documents as isolated pages, our solution infers a document’s hierarchy and structure. This allows you to add metadata to each chunk that describes its position in the document, which then lets your agents understand how different sections relate to each other and connect information across pages.
Minimized hallucinations: Our multi-stage pipeline minimizes severe hallucinations while also providing bounding boxes and confidence levels for table extraction to simplify auditing its output.
Superior handling of complex modalities: Technical diagrams, complex figures and nested tables are efficiently processed to support all of your data.
We’ve tested it against our customer evaluation datasets, and we’ve found that capturing document hierarchy can dramatically improve end-to-end RAG performance
Getting started
The first 500+ pages in our Standard mode (for complex documents that require VLMs and OCR) are free if you want to give it a try. Just create a Contextual AI account and visit the Components tab to use the Parse UI playground or call the API directly.
Documentation:
📃 /parse API docs
📃 Python SDK
📃 Code examples
Happy to answer any questions about how our document parser works or how you might integrate it into your RAG systems!
Hey Jay, this tool looks super useful—especially for anyone building serious RAG workflows. You should consider listing it on Aixyz .Co, it’s free and a great way to get in front of more AI-focused users. Would love to see it there!
Focusing on document-level understanding and hallucination reduction addresses key RAG pain points. The handling of complex modalities could be particularly valuable for enterprise use cases with diverse document types.
Mukh.1
This looks like a solid advancement for RAG pipelines. Congrats on the launch. We’re building Mukh.1, where AI agents handle structured automation across tools. Check it out.