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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Your organization's knowledge is scattered across various formats such as PDFs, spreadsheets, wikis, and ERP exports. Traditional retrieval augmented generation (RAG) techniques can only extract the top-K similar snippets, which is adequate for brief summaries but inadequate when precise and comprehensive answers are required. Vedana adopts a structure-first methodology, allowing you to define your domain through anchors, attributes, and connections. It systematically ingests your data into a categorized knowledge graph, enabling AI agents to investigate it incrementally: performing graph queries, vector searches, and compiling answers from actual data. In this setup, the language model interprets the information while the data remains the authoritative source. The advantages you gain include: - Precise figures: obtaining specific prices, dates, and statuses directly from the graph - Comprehensive outcomes: guaranteeing that all relevant records are included without omissions - Multi-step reasoning: connecting product information to categories, regulations, and documents seamlessly - Traceability: ensuring that every answer is linked back to specific nodes, edges, and data segments - Consistency: delivering the same results with identical queries and processes Additionally, the system comes with built-in evaluation using gold-standard datasets and is compatible with any language model. It can be deployed as open-core, managed cloud, or on-premises solutions, with pilot implementations available in just four weeks. This approach not only enhances accuracy but also fosters a deep understanding of the interconnected data landscape.

Description

Create a natural language interface to your data. NLP is the heart of modern enterprise data processing. We provide developers the tools they need to quickly and efficiently build NLP systems that are ready for production. Our open-source framework allows for API-driven, scalable NLP application architectures. We believe in sharing. Our software is open-source. We value our community and make modern NLP accessible, practical, scalable, and easy to use. Natural language processing (NLP), a branch in AI, allows machines to interpret and process human language. Companies can use human language to interact and communicate with data and computers by implementing NLP. NLP is used in areas such as semantic search, question answering (QA), conversational A (chatbots), text summarization and question generation. It also includes text mining, machine translation, speech recognition, and text mining.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

No images available

Screenshots View All

Integrations

Pinecone No 
Unremot No 

Integrations

Pinecone Yes 
Unremot Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Epoch8

Founded

2017

Country

Georgia

Website

e8.team/

Vendor Details

Company Name

deepset

Country

Germany

Website

www.deepset.ai/

Product Features

Product Features

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

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