EyeLevel provides a platform for building Retrieval-Augmented Generation (RAG) applications that utilize enterprise data to deliver accurate and secure AI solutions. By enabling companies to ingest, store, and search complex documents, EyeLevel addresses the challenge of generating reliable outputs from large language models, achieving up to 95% accuracy in various applications across industries.
Funding
$3.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
BVFounders
Product
Problem
Enterprises struggle to build Retrieval-Augmented Generation (RAG) applications due to the challenges of ingesting, storing, and searching complex documents, which leads to unreliable outputs from large language models (LLMs). Existing RAG solutions often lack the accuracy, security, and scalability required for enterprise-grade applications, especially when dealing with visually complex documents and regulated data environments.
Solution
EyeLevel offers GroundX, a platform designed to streamline the development of enterprise-grade RAG applications that require accuracy, security, and scalability. GroundX provides a suite of interlocking systems for ingesting, storing, searching, and evaluating enterprise data, ensuring that LLMs generate reliable and contextually relevant outputs. The platform's ingestion pipeline uses a vision model to identify and transform tables, forms, and diagrams into LLM-ready data, while its search capabilities blend text, vector, and micro-graph search techniques. GroundX can be deployed on-premises, in air-gapped environments, or in a managed cloud, providing flexibility for organizations with strict data governance requirements.
Target Audience
EyeLevel targets enterprises across industries such as insurance, legal, healthcare, and finance that require accurate, secure, and scalable RAG solutions for building AI applications.
Features
- Vision Model: Fine-tuned model trained on enterprise documents to identify tables, forms, and diagrams.
- MultiModal Transform: Converts diagrams, tables, and forms into LLM-ready data.
- Dynamic Chunker: Breaks content into blocks that LLMs can readily process.
- Secure, Encrypted Storage: Data is encrypted at rest and in transit within a VPC, with customer-controlled keys.
- Hybrid Search Approach: Combines text, vector, and micro-graph search with a fine-tuned re-ranker model.
- RAG Eval Tools: Visual tools to evaluate document parsing and transformation into LLM-ready data.
- Retrieval Viewer: Allows users to view search retrievals and metadata before sending to an LLM.
- Completions + Sources: Enables users to interact with documents via completions and source retrievals.