Stratum
SOLUTIONS
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The data layer your Al stack was promised but never got.

Stratum is an AI-native data layer built to transform messy, siloed data into structured, compliant, application ready data on-premise for your Al use cases.

Read the whitepaper

PYTHON
from pathlib import Path
from stratum.parsers import parse_document

doc = parse_document(path=Path("report.pdf"))

print(doc.metadata.format) # pdf
print(doc.metadata.total_pages) # 42
print(len(doc.blocks)) # 314 blocks extracted
Stratum Solution

Structured Extraction

Multilingual support
Handles degraded scans
JSON output

VLM-based parsing identifies tables, forms, headers, and embedded images across European languages. Works on documents in the wild.

Trusted by Enterprise Innovators

Wikimedia
Expertise France
ministre de l'enseignement supérieur et de la recherche scientifique
Nvidia
Mozilla
Orange
PSL
SpineDAO
Wikimedia
Expertise France
ministre de l'enseignement supérieur et de la recherche scientifique
Nvidia
Mozilla
Orange
PSL
SpineDAO
Wikimedia
Expertise France
ministre de l'enseignement supérieur et de la recherche scientifique
Nvidia
Mozilla
Orange
PSL
SpineDAO

The Stratum Data Layer

A modular SDK built for data scientists. Handle document parsing, context preparation, and PII masking on-premise with simple APIs.

Rooted in Open Source Foundations

Common CorpusOpen, traceable dataSYNTHSynthetic data pipelinesBaguettotronSmall reasoning models

Structured Extraction

Complex PDF parsing for real documents: tables, forms, headers/footers, stamps, embedded images — multilingual, robust to low-quality scans.

Turns messy files into clean, schema-ready outputs your stack can actually use.

  • Tables, forms, headers/footers, stamps & embedded images
  • Multilingual with robust handling of low-quality scans
  • Schema-ready JSON output

Use Cases

Expertise France Funding Analysis
Structured Extraction visualization

Context Engineering

Most enrichers work without an LLM. Plug in any provider (or a local model) when you need deeper analysis.

Structure-preserving chunking + document graph: stable chunk IDs, section hierarchy, cross-references, provenance pointers.

  • Stable chunk IDs & section hierarchy
  • Cross-references & provenance pointers
  • Attributable context with evidence trails
  • Per-section summaries, outlines, classification, topic maps

Use Cases

Synthetic DataRAG For Spine Care
Context Engineering visualization

Privacy Aware Processing

Single-pass PII detection across text + document images, built for messy corpora (scans, mixed languages, template drift).

Deploy on-premise for full data sovereignty. Policy engine applies your rules with a complete audit trail.

  • Built for on-premise & sovereign infrastructure
  • Text + image PII detection in a single pass
  • Policy engine: remove, mask, or pseudonymise
  • Complete audit trail with bounding boxes
Privacy Aware Processing visualization

Benchmarks

Our evaluations for RAG Quality using MMLongBenchDoc compared with similar libraries

RAG QA with Evidence in Tables(218 Questions)

MetricStratumReductoLlamaIndex
Judge Score Mean0.110.110.10
Judge Correct Rate @0.50.180.110.11
Doc ID Accuracy0.700.640.46
Page Hit Rate0.540.420.35
Doc MRR Topk0.770.720.52

RAG QA with Evidence in Images(243 Questions)

MetricStratumReductoLlamaIndex
Judge Score Mean0.270.240.18
Judge Correct Rate @0.50.250.240.18
Doc ID Accuracy0.580.550.49
Page Hit Rate0.660.670.60
Doc MRR TopK0.630.600.54

Ship AI that works, not AI that demos

Stratum handles document processing, context engineering, and PII on-premise so your team builds the AI, not the plumbing.