CONVERT ENTERPRISE INFORMATION WITH 99% ACCURACY

GroundX for Extraction

Unleash the power of most advanced multimodal vision model AI platform to convert legacy enterprise content into accurate, structured data for operational workflows efficiency

Try GroundX
Schedule Demo

Intelligent Multimodal Document Parsing

GroundX combines a vision model with a multimodal model, fine-tuned on nearly one million pages of enterprise documents, to accurately interpret complex files. It identifies text, tables, images, and diagrams on each page so even visually dense content can be structured correctly from the start.

Semantic Object Creation

Once each document element is identified, GroundX sends it through the right processing pipeline to transform it into LLM-ready data. The system generates rich metadata, explains complex objects like tables and graphics, and creates multiple optimized chunk versions called semantic objects for stronger search and completion.

Contextualization

In a final context pass, GroundX compares each semantic object with surrounding content and a summary of the full document to improve understanding. This helps the system connect related information across a page or document, reducing downstream hallucinations and improving retrieval quality.

Fine Tune Our Vision Model to Your Documents

GroundX has the first and only vision model with fine tuning capabilities  for allowing customization for the process and extraction of enterprise  unique document sets. With unified support for source files, multimodal objects, and vectors, the platform is built for fast, flexible retrieval across complex enterprise data.

Vital Context
‍In the final pass, GroundX compares each semantic object to surrounding objects and also a summary of the entire document. The system uses the extra context to improve its understanding of each object.

This is critical when information from one part of a page is important to another. For example, a financial table might be on the bottom of a page and rich text explaining its purpose might be on the top. This context pass lets us enrich our understanding of the table from the text above. This is one of nearly a dozen techniques inside GroundX to reduce downstream hallucinations.
‍
The World's First Custom Fine Tune for Ingest Models
GroundX performs industry leading ingest automatically across a wide array of documents. However, we also make it possible to fine tune the ingest model based on your unique documents. This is a powerful capability that is totally unique to GroundX.

Try Your Docs for Free Here

Bills and Invoices

GroundX automatically converts complex, high-volume documents into accurate, structured data, reducing manual processing costs, eliminating the need for templates, accelerating workflows and enabling reliable analytics and AI-driven decision making

Complex Tables

Automatically extract tables from documents and converts them into structured, machine-readable data, eliminating manual entry, improving data reliability and enabling faster analytics and automated workflows.

Handwritting

Our handwritten text extraction converts handwritten content from forms, notes, and documents into accurate, structured digital data, reducing manual transcription, improving data accessibility, and enabling automated processing and analysis.

Illustrations, Diagrams and Schematics

Our graphical illustration extraction and interpretation automatically identifies and translates charts, diagrams and visual elements into structured, machine-readable insights, enabling organizations to unlock critical information that traditional OCR and text-based systems cannot capture

Photographs and Images

Our photographic elements extraction and interpretation analyzes images within documents to identify relevant objects, conditions, and contextual details, transforming visual information into structured insights that support automated workflows, compliance checks, and operational decision-making.

Try GroundX
Schedule Demo
FEATURED EXTRACTION CASE STUDY

How AI-Native Extraction Replaced OCR, Templates & Human Review

A utility bill processor went from 70–75% accuracy with brittle OCR pipelines to ~99% accuracy across 1,000+ document formats — including photos of physical bills — with zero templates and no human review layer.

~99%
Extraction accuracy
1,000+
Document formats
120
Fields per document
0
Templates required
The Story

How GroundX Revolutionized a Client’s Utility Bill
Processing Forever

A utility bill processing and management company came to EyeLevel with a familiar but costly problem: their document extraction workflow depended heavily on templates, manual review, and brittle extraction logic that struggled to scale.
Each new client tier, utility provider, or bill layout often required new templates to be built and maintained. As document diversity increased, the process became slower, more expensive, and less reliable. Human reviewers were deeply embedded in the workflow to catch exceptions and correct errors, but even with substantial human intervention, the system’s extraction accuracy remained only around 70% to 75%.
By partnering closely with the client, the GroundX and EyeLevel teams re-architected the extraction workflow using an AI-native, agentic approach. The result was a system that achieved 99% accuracy, dramatically reduced the need for human-in-the-loop review, and eliminated the need for templates altogether.

The Challenge

Before implementing GroundX, the client faced several operational constraints:
Template Dependency
Their extraction pipeline relied on templates that had to be created and maintained for each new utility bill format, client tier, and layout variation. This made onboarding slower and created ongoing maintenance overhead as formats changed.
Heavy Human-in-the-Loop Requirements
Because extraction performance was inconsistent, the company depended on substantial human review throughout the workflow. This manual process was both time-consuming and costly, reducing efficiency and limiting scale.
Accuracy Below Acceptable Thresholds
Even with human intervention, the overall extraction accuracy before GroundX was only 70% to 75%, well below the standard required for reliable downstream operations.
Complex Business Logic
Utility bills are highly diverse documents, and extracting the correct fields often requires applying highly specific business rules. The client had been using tedious prompting methods to try to capture these requirements, but the results were inconsistent and difficult to maintain.
THE PIPELINE IT REPLACES

The Three-Stage Document Pipeline Has Defined IDP for 20 Years

Every legacy intelligent document processing stack — from utility bills to insurance claims to medical records — depends on the same fragile assembly line. Each step compounds the error rate of the one before it.

STEP 1
OCR
Convert images to text. Works reasonably well on clean, single-column, machine-generated copy. Falls over on scans, photos, handwritten notes, multi-column layouts, watermarked pages, and forms with overlapping fields.
Loses layout context immediately
STEP 2
Templates & Regex
Hundreds or thousands of brittle rules extract structured fields from raw OCR text. They break when a vendor changes a bill format, when a form is rotated five degrees, or when a field is handwritten in a typed box.
Requires constant maintenance
STEP 3
Human Review
Because OCR + regex tops out around 70–85% accuracy on complex documents, offshore teams of reviewers correct every extraction by hand. This is where the real cost lives — and it scales linearly with volume.
Cost scales with documents, not value
GroundX collapses these three stages into one AI-native pipeline. No OCR layer, no template library, no review army — semantic understanding from ingestion to structured JSON.
WHY IT WORKS

A Multimodal, Agentic Extraction Architecture

EyeLevel engineers spent more than three years building the agentic architecture behind GroundX. Around ten specialized agents operate on each document — you can turn them on or off, change their instructions, and describe extraction goals at a high level. The output comes back as clean, typed, machine-readable JSON.

Unified Ingestion Across ~20 Enterprise Formats
A single ingestion layer handles nearly twenty enterprise document formats. No more building separate preprocessing pipelines for PDFs, images, scans, spreadsheets, and office files.
Layout-Aware Vision-Language Parsing
Instead of flattening pages into raw text the way OCR does, the vision-language stack analyzes the page at the layout level —identifying textblocks, tables, images, diagrams, and graphics while preserving their structural and spatial relationships.
~10 Configurable Multimodal Agents
Specialized agents transform each document element into structured, LLM-ready representations enriched with normalized semantic metadata. Tables stay tables. Graphics stay meaningful. Visually complex regions never become noisy OCR strings.
Task-Aware Semantic Object Creation
Multiple optimized representations are generated from the same source content depending on the downstream task — retrieval, ranking, structured extraction, graph population, or summary generation. No single rigid chunking strategy.
Correction & Contextualization Layers
Quality-control agents compare extracted units against nearby content to repair errors. Contextualization agents resolve ambiguity by comparing each semantic object against surrounding content and document-level summaries — reducing downstream hallucinations on long, dense enterprise documents.
Handles Photos & Visually Inconsistent Documents
The anchor deployment processes mobile phone photos of physical bills submitted in uncontrolled conditions. Lighting, angle, partial occlusion, and crumpled paper no longer break extraction.
Zero-Shot Generalization to New Formats
When a brand-new bill format appears, GroundX still extracts correctly — because the agents understand the semantic meaning of the content, not the pixel coordinates of where a field appears on a specific template. There are no templates to maintain. There is no format- specific configuration at all.
Structured, Typed, Machine-Readable Output
Outputs are returned as clean JSON arrays — directly usable by downstream systems, analytics pipelines, and graph databases. No glue code stitching together OCR strings and regex matches.
THE OUTCOME

What Changed for the Utility Bill Processor

Before
70–75% accuracy with OCR + templates + manual QA
AFTER
~99% accuracy with a fully automated AI-native pipeline
BEFORE
Custom template built and maintained for every new bill layout
AFTER
Zero templates — new formats handled with zero-shot generalization
Before
Offshore reviewers correcting every extraction by hand
AFTER
Human-in-the-loop review eliminated from the workflow
Before
Tedious prompting and brittle business rules per client tier
AFTER
High-level extraction goals defined once, enforced semantically
BEYOND UTILITY BILLS

The Same Extraction Capabilities, Across Every Document-Heavy Industry

The GroundX Extract pipeline is document-agnostic. The same agents that extract 120 fields from a utility bill can populate knowledge graphs, build medical chronologies, or surface trends across thousands of free-text conversations.

Entity & Relationship Extraction for Graph Databases
Configure the agents to find every person, place, organization, and the relationships between them — and you get structured data ready to populate a knowledge graph. Unstructured document archives become queryable relationship databases for legal discovery, compliance, and investigations.
Medical Chronologies from Clinical Notes & Claims
Pull every diagnosis, medication, procedure, and date from thousands of pages of unstructured clinical notes and claims documents — a use case that previously required custom NLP pipelines and weeks of clinician review.
Insurance Claims, Repair Estimates & Policy Documents
The insurance industry runs on documents. Claims packets, photo evidence, medical records, repair estimates, and legal correspondence all flow through the same extraction pipeline — without per-carrier templates.
Trend Analysis on Unstructured Conversation Data
Run free-text consultations, support transcripts, or field reports through the workflow to extract product mentions, symptom patterns, regional trends, and outcomes automatically — turning conversation archives into structured analytics inputs.

Denver, CO

sales@eyelevel.ai
Solutions
GroundX ExtractGroundX ChatGroundX Report
Quick Links
About
HostingSecurity
rESOURCES
GroundX API DocsBlogLicense
© 2026 EyeLevel.ai — All rights reserved.