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What is Vedana?

Your organization’s information is dispersed across multiple formats, including PDFs, spreadsheets, wikis, and ERP exports. While traditional retrieval augmented generation (RAG) methods can retrieve the top-K related snippets, this is sufficient for brief overviews but falls short when detailed and thorough responses are necessary. Vedana implements a structure-first approach that allows you to establish your domain through anchors, attributes, and connections. It methodically integrates your data into a well-organized knowledge graph, which permits AI agents to explore it progressively by executing graph queries, vector searches, and compiling responses directly from authentic data. In this architecture, the language model processes the information while the actual data retains its status as the primary source. The benefits you receive encompass: - Exact figures: acquiring specific prices, dates, and statuses directly from the graph - Inclusive results: ensuring that all relevant records are considered without any omissions - Multi-step reasoning: effortlessly linking product details to categories, regulations, and documents - Traceability: confirming that every response is connected back to particular nodes, edges, and data segments - Consistency: producing the same outcomes with identical queries and operations Moreover, the system features built-in evaluation using gold-standard datasets and is compatible with any language model. It can be implemented as open-core, managed cloud, or on-premises solutions, with pilot projects available in just four weeks. This innovative methodology not only improves accuracy but also nurtures a profound understanding of the interconnected data ecosystem, ultimately empowering your organization to make better-informed decisions.

What is Superlinked?

Incorporate semantic relevance with user feedback to efficiently pinpoint the most valuable document segments within your retrieval-augmented generation framework. Furthermore, combine semantic relevance with the recency of documents in your search engine, recognizing that newer information can often be more accurate. Develop a dynamic, customized e-commerce product feed that leverages user vectors derived from interactions with SKU embeddings. Investigate and categorize behavioral clusters of your customers using a vector index stored in your data warehouse. Carefully structure and import your data, utilize spaces for building your indices, and perform queries—all executed within a Python notebook to keep the entire process in-memory, ensuring both efficiency and speed. This methodology not only streamlines data retrieval but also significantly enhances user experience through personalized recommendations, ultimately leading to improved customer satisfaction. By continuously refining these processes, you can maintain a competitive edge in the evolving digital landscape.

Media

No images available

Media

Integrations Supported

Python

Integrations Supported

Python

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Epoch8

Date Founded

2017

Company Location

Georgia

Company Website

e8.team/

Company Facts

Organization Name

Superlinked

Date Founded

2021

Company Location

United States

Company Website

superlinked.com

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