Docugami is a Seattle-area document engineering company that applies breakthrough artificial intelligence to unstructured and semi-structured information, enabling organizations to radically improve productivity, compliance, and insight. The company was founded in March 2018 by Jean Paoli and four other senior engineering leaders from Microsoft.

Docugami is Generative AI for business documents. 

Our patented AI is designed and trained exclusively for business scenarios, to automatically unlock the critical information in your company's documents and use that information to generate reports, insights, new documents, and data streams to power your business processes. 

Docugami provides a family of Large Language Models (LLMs) trained on millions of Business Documents, ranging from 2.7B to 20B parameters. 

Docugami is designed to provide immediate value to front-line business users and managers, without costly investments in IT projects or training. Docugami turns your unique business documents into data; all content generated is derived exclusively from your own business documents -- not random content on the Internet -- minimizing any potential for erroneous or inappropriate content generation. 

Each customer's data, feedback, and generated content are completely separate and secure. 

Docugami is cross-segment and in the market today in a variety of vertical industry segments, including Commercial Insurance, Commercial Real Estate, Technology, a wide range of Professional Services sectors, and more. 

Hiring Machine Learning PhD/MS Students & Brilliant Developers. 

Contact Jean Paoli at jeanpa@docugami.com

National Science Foundation

Samsung NEXT

SignalFire

NextWorld Capital

Grammarly

Barry Abbott

Bob Muglia

Ellen Levy

Hank Vigil

Sarah Imbach

Sunil Chhaya

Thomas Reardon

Docugami

In case you missed our KG-RAG webinar, or want to see it again – it’s now available on-demand. Docugami #KG-RAG is #RAG based on a full #XML #KnowledgeGraph of your unique business documents, powered by Redis for unmatched precision and no #AI hallucinations.
https://hubs.li/Q02l5vRg0

Docugami is honored to be called out by Gartner for how our #DocumentEngineering solutions and business-focused #LLMs enable superior performance and address a wide range of #security, #privacy, and accuracy issues.

In a recent report, Gartner cited Docugami as an example of a technology company that was using Generative AI to innovate far beyond traditional Intelligent Document Processing models. The report called out Docugami as an LLM-enabled solution that allows users to easily convert #unstructured content like long-form documents into structured data for a wide range of business purposes, without the security and privacy concerns that generic #AI can create.

We appreciate the positive comments from Gartner, and will continue to advance our industry-leading Document Engineering technologies. 

Learn more: https://hubs.li/Q02kq5tF0

REMINDER: Our KG-RAG webinar starts at 12pm ET/9am PT.
Docugami KG-RAG is #RAG based on a full #XML #KnowledgeGraph of your unique business documents, powered by Redis for unmatched precision and no #AI hallucinations.
Join us for a great discussion!
https://lnkd.in/ghHeTEMM

Docugami KG-RAG is Retrieval-Augmented Generation w/Redis based on a full XML Knowledge Graph of your own business documents. With our loader and dataset on the LlamaIndex hub, you can tap Docugami KG-RAG for your business. Learn more at our Feb 7 webinar: https://lnkd.in/ghHeTEMM

Retrieval-Augmented Generation (RAG) is getting a lot of well-deserved recognition for its ability to improve the accuracy of #Generative AI, but all #RAG is not created equal. Docugami’s KG-RAG (Knowledge Graph-RAG) outperforms other RAG approaches because it uses exclusive hierarchical semantic #chunking to create a comprehensive #XMLKnowledgeGraph from your organization’s unstructured documents in their entirety, enabling far more accurate RAG. Docugami has worked with LangChain LlamaIndex and others to publish templates and other resources to make it easy for organizations to use Docugami KG-RAG in their business processes. 
We are excited about the potential for Knowledge Graph Retrieval-Augmented Generation (KG-RAG) to deliver deep business value for companies. Let us show you how. 
https://hubs.li/Q02jK96f0

Docugami’s Knowledge Graph Retrieval-Augmented Generation delivers #GenerativeAI business can trust. And our LangChain integration makes it easy for anyone to access the power of Docugami KG-RAG. All #RAG is not created equal -- Docugami's KG-RAG provides greater precision and accuracy, with no AI hallucinations.
Learn more at our webinar with Redis on Feb 7!
RSVP here: https://hubs.li/Q02jBC7C0

Want to learn more about Retrieval-Augmented Generation (RAG)? Docugami KG-RAG is #RAG based on a full XML Knowledge Graph of your own unique business documents, powered by Redis for unmatched precision and no #AI hallucinations. 
RSVP and join us Feb 7!
https://hubs.li/Q02jBrrR0

Generative AI was THE tech story of 2023, but many business leaders struggled with how to embrace AI, and whether the risks outweighed the benefits. 
#AIhype will become #AIproductivity in 2024, with responsible and trustworthy enterprise-ready solutions. 
#DocumentEngineering #AIforDocuments #ResponsibleAI

From Hype to Productivity: How AI is Transforming Business in 2024 https://hubs.li/Q02h8ZKY0

We worked with LangChain to publish a #RAG template so you can create a complete XML knowledge graph of your own documents. 
Customers who use their Docugami #XML #KnowledgeGraph as input to #LLMs will see exceptional accuracy and precision. 
Benchmarks with LangSmith found Docugami KG-RAG outperforms OpenAI Assistant RAG by a wide margin. 
Check it out here: https://hubs.li/Q02dxGCx0

Towards More Advanced QA Datasets 

A big issue with evaluating RAG is that the datasets are oftentimes too simple - the questions are derived from a single chunk, and the source corpus is a single document.

Taqi Jaffri (Docugami) just contributed a fantastic dataset that addresses some of these concerns: a Multi-Doc SEC 10Q Dataset. The questions range in difficulty:

✅ Single-Doc, Single-Chunk RAG: Questions where answer is in a contiguous region of a single-doc.
✅ Single-Doc, Multi-Chunk RAG: Questions where answer can be found by combining multiple non-contiguous regions of a single doc.
✅  Multi-Doc RAG: Questions where answer can be found through combining multiple non-contiguous regions of multiple docs.

Check it out! https://lnkd.in/d2S6_tDb

Check out the source repo by Docugami here: https://lnkd.in/d7GSPWj6

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