Axiom Legal Data

Build unbreakable legal AI, faster. TSTR-validated synthetic data for reliable training without the legal risk.

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Made with care for legal AI builders.
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Build Unbreakable Legal AI, Faster

Our TSTR-validated synthetic data is empirically proven to perform under pressure, eliminating the model failures and ethical risks common with other data sources.

Stop paying Big Law rates of $1,000+/hour for training data generation. Build your legal AI tools 10x faster with production-ready synthetic datasets that pass the industry's most rigorous validation standards.

Built for AI Innovators

Every feature addresses the specific challenges facing legal tech CTOs: speed-to-market, budget constraints, and the risk of using real client data.

VLM-Powered Processing

Go Beyond Simple OCR

VLM-Powered Processing

Go Beyond Simple OCR

Our Vision Language Models understand document structure, layout, and context, capturing nuances that other systems miss.

  • Preserves legal document hierarchy and relationships
  • Understands complex formatting and annotations
  • Maintains semantic meaning across document types

PII Shield Protection

Train with Zero Risk

PII Shield Protection

Train with Zero Risk

Our dual-model NER pipeline detects and pseudonymizes all sensitive data, ensuring complete legal and ethical compliance.

  • 99.9% PII detection accuracy
  • Safe harbor from privacy violations
  • Maintains data utility while removing all risk

TSTR Validated Quality

Empirically Proven Performance

TSTR Validated Quality

Empirically Proven Performance

We use the 'Train on Synthetic, Test on Real' methodology to guarantee our data performs on real-world tasks.

  • Industry gold-standard validation approach
  • Benchmarked against LegalBench standards
  • Published validation reports for transparency

A note from our founders

We're currently in pre-dataset development. Our first public methodology + benchmark update is targeted for Q2 2026. We'll share results publicly and invite scrutiny. Join the waitlist to get updates.

The Axiom Data Refinery

Our sophisticated pipeline transforms raw legal documents into production-ready synthetic datasets with uncompromising quality and privacy protection.

Trusted Sources

EDGAR • RECAP • & More

Curated legal documents from trusted repositories

VLM Processing

Seed Data Refinery

Vision Language Models extract structure, context, and legal nuances

PII Shield

Dual-Model NER

Advanced detection and pseudonymization of all sensitive data

Quality Gate

TSTR Validated

Train-on-Synthetic, Test-on-Real methodology ensures performance

Final Output

Production-Ready Synthetic Dataset

Clean .jsonl files ready for immediate AI training and deployment

Don't Just Take Our Word for It

We prove our data's quality with objective, empirical validation using industry-standard methodologies. Every claim is backed by verifiable results.

Transparent

TSTR Methodology

Industry Gold Standard

Train on Synthetic, Test on Real validation proves our data's utility with the same rigor used by leading AI research labs.

  • 95%+ performance retention vs. real data
  • Validated across multiple ML architectures
  • Published validation reports available
Validated

LegalBench Benchmarked

Standardized Legal Reasoning

Our models aren't just generating text; they're benchmarked against standardized legal reasoning tasks to ensure real-world capability.

  • Tested on 162 legal reasoning tasks
  • Cross-validated performance metrics
  • Objective, empirical proof of quality
Verifiable

Complete Validation Reports

Don't Just Take Our Word

We prove our data's utility with comprehensive testing and will publish detailed validation results for full transparency.

  • Detailed TSTR performance analysis
  • Task-specific benchmark results
  • Methodology documentation available

Get Early Access to Our Validation Reports

Join our pilot program to receive exclusive access to our complete TSTR validation report and LegalBench scores once development is complete.

The Axiom Quality Standard

Behind our simple .jsonl output files lies a sophisticated pipeline engineered for uncompromising quality and legal compliance.

Multi-Modal Data Sources

EDGAR filings, RECAP court documents, and curated legal repositories

SEC 10-K/10-Q filings with comprehensive financial disclosures
Federal court dockets from PACER and RECAP archives
Public legal databases with verified document authenticity
Real-time document ingestion with automated quality checks

From the Founder

Josh Brackin, Founder of Axiom Legal Data

A Relentless Focus on Quality

My career was forged over a decade of managing people and systems for corporate America, where 'good enough' is never an option. You learn to build systems and processes that are reliable, scalable, and deliver an unimpeachable user experience. When I began my deep dive into AI, I saw that the foundational data used in legal tech didn't meet that standard. It was brittle, legally risky, and often failed the customer before the product was even built. Axiom was born from a simple idea: to bring an exceptional standard of quality and reliability to the foundational data that powers legal AI.

What’s Next

Methodology preview

Initial technical memo and approach overview.

Initial dataset (v0)

First synthetic dataset for early partners.

First comparison report

TSTR benchmarks and baselines vs. alternatives.

FAQ

When is the first dataset releasing?

We are targeting the first public v0 update in Q2 2026.

How will you validate quality (TSTR)?

We apply Train-on-Synthetic, Test-on-Real methodologies with transparent benchmarks and ablations.

How do you protect PII?

We combine VLM redaction with policy filters and QA review for defense-in-depth.

Will you share comparison reports?

Yes, we plan to publish comparisons versus public baselines and proprietary alternatives where possible.

Can we join the pilot?

Yes, apply via the pilot program for early access and feedback cycles.

What use cases are supported first?

Litigation workflows: response drafts, discovery summarization, and document synthesis.

Exclusive Pilot Program

What to Expect in the Pilot Program

Join 50 pioneering legal tech companies in shaping the future of synthetic data. This isn't just early access—it's a strategic partnership.

Early Access

Receive the first tranche of our Litigation Response Library dataset before public release.

Direct Founder Access

Work directly with our founder to provide feedback and shape the future of our data offerings.

Complete Transparency

Receive exclusive access to TSTR validation and LegalBench benchmark reports once development is complete.

Preferential Pricing

Lock in a 25% lifetime discount on all future datasets.

Limited Opportunity

We're accepting only 50 companies to ensure personalized attention and meaningful collaboration.
Applications close when we reach capacity.

Join the Next Generation of Legal AI

Get exclusive early access to our Litigation Response Library dataset and receive your complete TSTR validation report once development is complete.

Limited to 50 pioneering legal tech companies

Insights & Analysis

Deep dives into legal AI, synthetic data validation, and the future of legal technology.

Sep 5, 2025

Train on Synthetic, Test on Real: Our Commitment to Unimpeachable Quality

How can you be sure that synthetic data is actually good? It’s a fair question, and one that should be asked of any synthetic data provider. In a field as precise as law, "looks okay" is not enough.

Read Article
Sep 5, 2025

Introducing Our 'PII Shield': A New Standard for Ethical Data in Legal Tech

Legal documents are, by their nature, filled with sensitive data—names, addresses, case numbers, and financial details. For innovators in legal tech, this presents a massive barrier.

Read Article
Sep 4, 2025

Why Most Legal AI is Built on a Shaky Foundation: The Data Problem

The legal tech world is buzzing with the promise of Artificial Intelligence. From automating contract review to predicting litigation outcomes, AI is poised to revolutionize the practice of law...

Read Article
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