[ RANKINGS ]Infratex Achieves #1 Rank on PulseBenchTab Document Understanding

Document Context Layer
for Enterprise Agentic AI

We turn unstructured PDFs, complex multi-page tables, and hand-written scans into structured JSON and Markdown your LLMs and AI agents can seamlessly reason over.

Read the docs

source_document_v2.pdfPAGE 01 / 01

Q4 Consolidated Performance

[TEXT · 99.8%]

METRICTARGETACTUAL

ARR Growth+14.5%+18.2%

Net Retention110.0%114.5%

LTV:CAC Ratio3.5x4.2x

f(x) = ∫ ψ(t) · e^{-i ω t} dt (Eq. 2.1)

infratex_pipeline_out.json● PARSING STREAM

"header": {"text": "Q4 Consolidated Performance",
"bbox": [24, 48, 254, 76],
"confidence": 0.998
}
"table": {"dimensions": "3x3",
"columns": ["metric", "target", "actual"],
"data": [{ARR: "+18.2%", confidence: 0.994},
{NRR: "114.5%", confidence: 0.992}
]
}
"formula": {"latex": "f(x) = ∫ ψ(t) · e^(-i ω t) dt",
"type": "integral_equation",
"confidence": 0.989
}
"chart": {"type": "bar_chart",
"keys": ["Q1", "Q2", "Q3", "Q4", "Q5"],
"confidence": 0.975
}

SCHEMA BINDINGS ACTIVE4 STAGES ALIGNED

[ PROBLEM STATEMENT ]

Enterprises run on documents, not databases.

GRID DETECTED

● ACTIVE

[SIG]

[1]

80%

of enterprise data is unstructured. Most of it sits in PDFs, scans, and spreadsheets your LLMs cannot natively parse.

[2]

6+ months

spent stitching OCR endpoints, chunking pipelines, and post-processors that instantly shatter when formats evolve.

[3]

15%

of in-house document ingestion tools actually reach production. The vast majority stall in pilot phases.

/ Document Context Pipeline

One document becomes the context layer your agents can trust.

Follow one run as a raw PDF moves through Infratex: visual page understanding, layout regions, Markdown and JSON, semantic chunks, a retrieval index, and finally a cited answer.

PDFA document enters intact

Infratex EngineThe engine reads the page visually

Layout RegionsRegions become addressable

Markdown + JSONClean structure is emitted

Chunks + IndexContext becomes retrievable

Cited AnswerAgents answer with evidence

run_7f29.document_context00% synced

source.pdf

Infratex Engine

raw page

layout graph

segment encoders

schema output

layout_regions

heading / table / formula / chart

context.json

bbox, page, schema, markdown

{
  "page": 1,
  "bbox": [24, 48, 254, 76]
}

semantic_chunks

ordered, cited, retrievable

vector_index

hybrid search + metadata filters

cited_answer

answer tied to source regions

PulseBench-Tab

Infratex Standard ranks #1 on table extraction.

We scored Infratex Standard on the released PulseBench-Tab dataset using the official T-LAG scorer. Across 1,820 multilingual table samples, Infratex leads on mean score and severe-failure rate.

Read benchmark report

0.9636Mean T-LAG

9 / 10Languages won

100%Coverage

6<0.50 failures

Overall T-LAGScore

1Infratex Standard100.0% coverage0.9636

2Pulse Ultra 2100.0% coverage0.9347

3LlamaParse Agentic94.0% coverage0.7977

4Reducto Agentic78.8% coverage0.7953

5Extend91.9% coverage0.7626

6Azure Document Intelligence92.0% coverage0.7614

7AWS Textract98.5% coverage0.6034

8Unstructured100.0% coverage0.3603

/ COMPREHENSIVE CAPABILITIES

The all-in-one agentic document platform.

A complete suite of tools designed to transform messy, unstructured real-world documents into clean data for your AI models.

rawcontext

Spatial Layout Parsing

Handle massive tables, multi-column layouts, and nested hierarchies while perfectly preserving bounding boxes and structural intent.

あ

语

rawcontext

Global Multilingual Support

State-of-the-art parsing across 140+ languages, effortlessly handling mixed-language documents and complex character sets.

{}

rawcontext

Intelligent Schema Extraction

Leverage hybrid retrieval and smart chunking to extract highly accurate, schema-conditioned JSON from any document structure.

rawcontext

LLM-Ready Formatting

Context-aware document chunking, markdown conversion, and embedding optimizations designed specifically for agentic workflows.

rawcontext

Robust Vision OCR

Advanced optical character recognition for scanned pages, low-quality faxes, and handwritten content with exceptional accuracy.

{}

rawcontext

The Complete Data Pipeline

Everything you need to transform raw, unstructured files into structured, production-ready assets for your LLMs.

/ PLATFORM CAPABILITIES

Everything your documents need to interact with AI.

Q3 Executive Summary

Revenue grew 18% YoY driven by enterprise API adoption. Operating margins expanded by 200bps.

ProductQ1Q2

SaaS1.2M1.5M

API0.8M1.1M

# Q3 Executive Summary
Revenue grew 18% YoY driven by enterprise API adoption...

| Product | Q1 | Q2 |
| ------- | -- | -- |
| SaaS    | 1.2M | 1.5M |
| API     | 0.8M | 1.1M |

INVOICE RECORD & LEDGER

Account: 0984-2394-111
Routing: 092000021
Date: Oct 2026

DATEIDDESCDEBITCREDIT

10/12A1AWS SVC$12,000-

10/13A2GCLOUD$8,400-

10/14A3APEX CORP-$142,500

10/15A4PAYROLL$84,000-

10/15A5RENTAL$15,000-

Schema
{ vendor, credit }

{
  "vendor": ""
  "credit": 0
}

REVENUE (M) vs REGION

1.51.00.50.0

EMEA

MARKET SHARE

60%40%

# Extracted Visual Data

RAW CHUNKS

</>Embedding
Model

VECTOR ARRAY

[ 0.024,-0.193, 0.884]
[ 0.112, 0.442,-0.091]
[-0.551, 0.772, 0.113]

/ INFRATEX ENGINE

VLM native.
Highly accurate outputs.

Hybrid GNN-CNN Architecture

Graph Neural Networks fused with CNNs map complex spatial hierarchies before tokenization, preventing critical data loss.

Segment-Dedicated Pipelines

Pages are dynamically routed. Specialized Vision Encoders process dense tables, charts, and textual blocks independently.

Near Zero-Hallucination Schemas

Strict schema conditioning forces the VLM layer to map raw pixels directly to deterministic JSON and Markdown arrays.

raw pagelayout graphsegment encodersschema output

[ TABLE EXTRACT ]

{
  "entity": "Infratex",
  "accuracy": 0.999,
  "schema": "valid"
}

See Infratex work on
your own documents.

Tell us about your pipeline bottlenecks and upload sample documents. We'll run them through our engine and show you custom structured schemas live on our call.

"The table parsing accuracy alone is lightyears ahead of other pipelines we benchmarked. It was the only solution that reliably parsed multi-page PDFs with nested grids without breaking."

— Head of AI Infrastructure, public fintech bank

Document Context Layer
for Enterprise Agentic AI

Enterprises run on documents, not databases.

One document becomes the context layer your agents can trust.

Infratex Standard ranks #1 on table extraction.

The all-in-one agentic document platform.

Spatial Layout Parsing

Global Multilingual Support

Intelligent Schema Extraction

LLM-Ready Formatting

Robust Vision OCR

The Complete Data Pipeline

Everything your documents need to interact with AI.

Parse

Extract

Document Interpreter

Agentic RAG

Answer

VLM native.
Highly accurate outputs.

Hybrid GNN-CNN Architecture

Segment-Dedicated Pipelines

Near Zero-Hallucination Schemas

See Infratex work on
your own documents.

Book a structured demo

Frequently Asked

Document Context Layer for Enterprise Agentic AI

Enterprises run on documents, not databases.

One document becomes the context layer your agents can trust.

Infratex Standard ranks #1 on table extraction.

The all-in-one agentic document platform.

Spatial Layout Parsing

Global Multilingual Support

Intelligent Schema Extraction

LLM-Ready Formatting

Robust Vision OCR

The Complete Data Pipeline

Everything your documents need to interact with AI.

Parse

Extract

Document Interpreter

Agentic RAG

Answer

VLM native.Highly accurate outputs.

Hybrid GNN-CNN Architecture

Segment-Dedicated Pipelines

Near Zero-Hallucination Schemas

See Infratex work on your own documents.

Book a structured demo

Frequently Asked

Document Context Layer
for Enterprise Agentic AI

VLM native.
Highly accurate outputs.

See Infratex work on
your own documents.