Master GroundX with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Contact GroundX sales
: Reach out for enterprise pricing and deployment consultation as GroundX focuses on enterprise customers with specific security requirements
Assess document scope
: Catalog your organization's document types, volumes, and access requirements to plan the knowledge base architecture
Plan security controls
: Define user roles, access permissions, and compliance requirements that GroundX will need to implement for your deployment
Design agent integration
: Work with GroundX to design API integration patterns for your existing or planned AI agent applications
Pilot with key documents
: Start with a subset of critical documents to validate retrieval quality and agent performance before full deployment
💡 Quick Start: Follow these 10 steps in order to get up and running with GroundX quickly.
Explore the key features that make GroundX powerful for knowledge & documents workflows.
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.
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.
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.
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.
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.
An AI lead justifying RAG vendor selection to leadership by referencing reproducible benchmark methodology rather than marketing copy.
GroundX is a managed end-to-end RAG service, while Pinecone is a vector database and LlamaIndex/LangChain are orchestration frameworks. With GroundX you send documents to one API and get accurate retrieval back; with the alternatives you assemble parsing, chunking, embedding, indexing, and retrieval yourself. EyeLevel has published benchmarks claiming 50-120% accuracy gains on complex enterprise documents versus those frameworks. Choose GroundX when you want speed-to-production and accuracy on visually complex documents; choose the alternatives when you need full control over every component or have unusual retrieval requirements.
Yes. GroundX is one of the few RAG platforms that supports a fully on-premises deployment, which is its primary positioning per the EyeLevel website ("RAG on-Prem"). This makes it suitable for healthcare, financial services, defense, and government customers who cannot send documents to a third-party SaaS. The cloud-hosted API is also available for teams without strict data residency requirements. On-prem deployments require your team to provision and operate the underlying infrastructure.
GroundX ingests PDFs, Word documents, PowerPoint, Excel, HTML, plain text, and scanned images. Its X-Ray parser is a vision-language model that extracts structured information from tables, charts, diagrams, and figures, converting them into LLM-readable text rather than discarding visual content as most parsers do. This is the main reason GroundX outperforms generic RAG stacks on documents like financial reports, technical manuals, and scientific papers where critical information lives inside visual elements.
GroundX uses enterprise pricing and does not publish a public price list — quotes are scoped to document volume, query volume, deployment model (cloud vs on-prem), and security requirements. Compared to the roughly four other enterprise RAG and knowledge platforms in our directory, this is typical: vendors targeting regulated industries usually price per deployment rather than per seat. Smaller teams looking for transparent pay-as-you-go pricing will likely find Pinecone or LlamaIndex Cloud more accessible.
GroundX is built by EyeLevel.ai, a RAG-focused company founded in 2021 and backed by enterprise customers in regulated industries. The company maintains the X-Ray document parser, the GroundX API, and the on-premises distribution. Their public technical content includes head-to-head benchmarks against LangChain and LlamaIndex, and they position the product specifically for enterprise AI engineering teams rather than the broader developer market.
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Tutorial updated March 2026