A service dedicated to regulated environments
General-purpose AI systems are not designed to operate reliably in complex legal and regulatory environments.
At BULORΛ.ai, we design custom legal datasets, specifically built for:
- AI audit and evaluation
- Controlled training of internal models
- Regulatory compliance (AI Act, AI governance)
- Reduction of legal hallucination risk
Each dataset is legally constrained, fully documented, and aligned with a clearly defined normative corpus.
A corpus-based approach — not opinion-based
Our datasets do not rely on free-form generative answers
nor on uncontrolled doctrinal interpretations.
They are built exclusively from:
- Official legal and regulatory texts
- Supervisory circulars, guidelines, and applicable standards
- Normative documents selected and validated upfront
📌 No answer is produced outside the defined corpus.
This approach makes it possible to test not only what an AI can answer,
but above all whether it knows when not to answer.
An AI-audit-oriented methodology
Each custom dataset is designed as an AI audit tool, not as a simple data collection.
It allows you to:
- Measure a model’s fidelity to a given corpus
- Identify responses outside the informational perimeter
- Compare multiple models or architectures (RAG, fine-tuning, prompting)
- Document the limitations and risks of an AI system
👉 This methodology fits directly into AI governance, internal control, and external audit frameworks.
Content of custom datasets
Depending on your needs, a dataset may include:
- Precise and legally verifiable questions
- Deliberately incomplete or ambiguous cases
- Expected answers strictly grounded in sources
- Documented refusals when context is insufficient
- Metadata exploitable by AI audit systems
📦 Delivered formats: JSONL (machine-readable), accompanying documentation
🌍 Languages: Legal French, Legal English
🔐 Versions: Frozen, traceable, version-identified
Typical use cases
🔍 AI audit & benchmarking
- Comparison of LLMs (OpenAI, Mistral, Claude, internal models)
- Evaluation of different RAG strategies
- Measurement of hallucination rates outside the corpus
🧠 Training & evaluation of internal models
- Regulation-constrained fine-tuning
- Post-training validation
- Detection of informational drift
💬 Internal legal & compliance assistants
- AML / compliance / regulatory assistants
- Decision-support tools (non-decision-making)
- Strict limitation of response scope
📊 AI governance & compliance (AI Act)
- AI risk documentation
- Proof of informational perimeter control
- Support for internal audits, regulators, and governance committees
What our datasets are not
❌ Open-web, uncontrolled datasets
❌ Training corpora for public models
❌ Unsourced legal answers
❌ Automated decision-making systems
Our datasets are designed to evaluate, test, and frame AI systems,
not to replace human legal reasoning or legal responsibility.
Contractual framework & confidentiality
Each project includes:
- A clearly defined contractual scope
- A specific usage license
- Restrictions on redistribution and public training
- Full traceability of delivered versions
➡️ Datasets can be used independently or integrated with BULORΛ.ai.
Start a custom project
Do you have a specific need (AML, insurance, employment law, finance, compliance, etc.)?
➡️ Contact us for a demo: contact(@)bulora.ai
➡️ Request an evaluation access: contact(@)bulora.ai
