Enterprise RAG platform optimized for AI agents, providing semantic search, document processing, and knowledge management with security controls.
Prepares your documents for AI with deep understanding — makes sure your AI gives accurate answers grounded in your actual content.
GroundX is an enterprise Knowledge & Documents RAG platform that delivers high-accuracy retrieval-augmented generation for AI agents working with complex documents, with on-premises deployment options and enterprise pricing tiers. It is built for regulated industries, large enterprises, and AI engineering teams that need accurate, auditable retrieval at scale.
Developed by EyeLevel.ai (founded in 2021), GroundX has been benchmarked against alternatives like LangChain and LlamaIndex with published accuracy gains of 50-120% on complex enterprise documents according to the company's tests. The platform's core differentiator is its multi-modal document parsing pipeline, which extracts and preserves context from tables, charts, diagrams, and figures within PDFs, PowerPoint files, spreadsheets, and scanned documents — content types where general-purpose vector databases typically lose fidelity. Organizations deploy GroundX either through the managed cloud API or as a fully on-premises stack, which is particularly valuable for healthcare, financial services, defense, and government customers with strict data residency requirements.
The platform exposes a complete RAG pipeline behind a single API: ingestion, semantic chunking, vector indexing, and hybrid search are bundled together rather than requiring teams to assemble Pinecone, Unstructured, and a chunking library themselves. GroundX also includes X-Ray, the company's vision-language document parser, which converts visual document elements into LLM-readable structured text. Based on our analysis of 870+ AI tools at aitoolsatlas.ai, GroundX sits at the enterprise end of the RAG market — compared to developer-focused alternatives like Pinecone, Weaviate, and LlamaIndex, it trades configurability for higher out-of-the-box accuracy and built-in compliance features. Teams that want a managed RAG layer rather than a stack of components, especially those working with visually complex documents, will find GroundX significantly reduces engineering overhead, while teams wanting full control of every retrieval parameter may prefer to assemble their own pipeline.
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Enterprise RAG platform optimized for AI agents, providing semantic search, document processing, and knowledge management with security controls.
EyeLevel's proprietary parser extracts structured information from tables, charts, diagrams, and figures inside PDFs and slides rather than discarding visual content. This is the primary technical differentiator behind GroundX's accuracy claims.
Use Case:
Indexing a portfolio of 10-K reports where critical financial data lives inside tables and charts that traditional PDF-to-text extractors flatten or skip.
Ingestion, semantic chunking, vector indexing, hybrid search, and retrieval are bundled behind one API. Teams skip the work of integrating a vector database, parser, and chunking library separately, which materially shortens time-to-production.
Use Case:
An AI engineering team launching an internal support agent in days by sending documents to the GroundX API instead of standing up Pinecone plus Unstructured plus custom chunking.
GroundX can run fully inside a customer's data center or private cloud, which is the explicit positioning on the EyeLevel website. This unlocks RAG for regulated workloads where SaaS RAG vendors are not acceptable.
Use Case:
A defense contractor running GroundX in a classified network to power document-search agents without external network egress.
Document-level permissions, audit logging, encryption, and SSO/SAML integration are built in rather than bolted on. Multi-tenant isolation lets one deployment serve multiple departments or customer tenants.
Use Case:
A bank exposing the same RAG backend to retail, wealth, and compliance teams with each group restricted to its own documents and full audit trails per query.
EyeLevel publishes head-to-head accuracy benchmarks claiming 50-120% improvements over popular RAG frameworks on complex document corpora. The methodology and datasets are documented publicly so customers can reproduce or scrutinize them.
Use Case:
An AI lead justifying RAG vendor selection to leadership by referencing reproducible benchmark methodology rather than marketing copy.
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GroundX continues to expand its enterprise RAG capabilities in 2026 with improvements to the X-Ray vision-language document parser for higher fidelity extraction from complex visual elements, expanded SDK support for Python and TypeScript, and enhanced on-premises deployment tooling for regulated industries.
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