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Trusted AI Governance for Legal & Regulatory Needs
Audit, test, and strengthen the compliance of your artificial intelligence systems.
Our tools test the logic, ethics, and robustness of your models using a clear and verifiable methodology.
BULORΛ helps you govern your AI in a world where compliance is no longer optional.
The AI that truly understands the law.
→ Our tests are based on real-world cases from the legal field (HR, GDPR, criminal law, etc.). You’re not testing AI in a vacuum, but in realistic legal situations validated by humans.
Readable results, not black boxes.
→ Each evaluation includes criteria of neutrality, transparency, and ethics. Biases are detected, explained — and then reviewed by experts. You understand why the AI fails… or succeeds.
A private space in your name
→ Your tests and reports are hosted on a secure, personalized interface — ready to impress an auditor or a partner.
Your AIs — tested, scored, and validated by humans
→ You receive clear dashboards with expert annotations and comments. Because serious AI evaluation can’t be 100% automated, we keep humans in the loop.
⚖️ Legal & Compliance / Private Sector
🎯 Main Motivation:
Reliability of reasoning + evidence to attach to legal work
🔧 Key Modules:
✅ Multi-turn – consistency of responses across multiple related questions
✅ Source – validity, format, and credibility of legal references
✅ Adversarial – comparison between two AI models answering the same question
"I want to be able to verify and prove that the AI I use doesn’t make up its references and actually reasons correctly."
🏛️ DPO / Public Institution / Regulatory Authority
🎯 Main Motivation:
Compliance with the AI Act + traceable documentation of all tests
🔧 Key Modules:
✅ Ethics / Bias – detection of systemic or discriminatory risks
✅ A/B Testing – transparent comparison of internally deployed models
✅ Comprehensive Audit – traceability, timestamping, scoring, and PDF certification
"I need to be able to prove that the AI used within my organization complies with regulatory requirements."
🚀 AI Startups / AI Product Teams / Technical Labs
🎯 Main Motivation:
Product quality + competitive benchmarking (Claude vs GPT vs fine-tuned AI)
🔧 Key Modules:
✅ A/B – multi-model comparative testing
✅ Robustness – resistance to prompt variations and stress-testing
✅ Temporal – model stability over time and across versions
"Before launching our model, we want to seriously compare it to GPT and verify its robustness."
🎓 Teachers, Researchers, and Academics in Law or AI
🎯 Main Motivation:
Educational illustration + creation of reproducible case studies
🔧 Key Modules:
✅ Multi-turn – scripting of legal case scenarios
✅ Scenario-Based – construction of structured reasoning
✅ All Modules – for comparing AI and human performance in an academic setting
"I want to use BULORΛ.ai in the classroom to show what AIs can — and can’t — do."
Evaluates the legal logic, argumentative structure, and deductive capacity of an AI model.
**Use cases: competitions, multiple-choice exams, legal case studies, and educational simulations.
Compares two models or two versions of the same prompt to assess their relevance and clarity.
**Use cases: LLM selection, technological benchmarking.
imulates a real case with progressive steps and dynamic interactions.
**Use cases: dismissal procedure, employee support, formal notice.
Compares a model’s responses from two opposing viewpoints.
**Use cases: litigation, arbitration, structured debate.
Submits “trap” prompts to detect regulatory flaws or harmful biases.
**Use cases: GDPR, manipulation, disinformation.
ests conversational consistency across multiple exchanges.
**Use cases: HR chatbot, legal support, contractual dialogue.
Evaluates the model’s ability to respond accurately despite degraded or imprecise language.
**Use cases: non-lawyer users, accessibility, digital inclusion.
Checks whether the model takes legal developments into account (dates, versions, deadlines).
**Use cases: new laws, procedural deadlines, legal reforms.
Verifies the reliability of legal foundations: cited laws, case law, and compliance with current legislation.
**Use cases: generated documents, legal opinions, substantive validation.
Evaluates neutrality and the absence of sensitive biases (gender, origin, social situation).
**Use cases: criminal law, labor law, discrimination.
Identifies semantic, reasoning, or tone divergences and detects critical flaws.
**Use cases: compliance, regulatory consistency checks, validation of internal generative agents.
At BULORΛ.ai, we firmly believe that auditing an artificial intelligence system can never be 100% automated.
That’s why our method is based on a dual, cross-evaluation process — our greatest strength.
This combination of AI and expert human insight enables the production of actionable, well-reasoned, and credible audits — far beyond simple “automatic scores” or technical dashboards.
Structured, reproducible prompts (CSV)
Evaluation of clarity, consistency, and relevance
Automatic detection of biases or hallucinations
Each test is also reviewed by a lawyer, legal expert, or compliance professional able to:
Identify flaws in reasoning or legal logic
Detect problematic or ambiguous wording
Interpret the consequences of a response in a real-world context (litigation, HR, contracts, etc.)
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Get direct guidance on AI governance.
Expert answers to your questions.
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Accurate answers on AI compliance, ethics, and robustness.
Comply with legal and ethical standards to reduce risks and strengthen trust.
Our modules measure neutrality, fairness, and transparency through regular audits.
Non-compliance: sanctions, financial losses, and reputational damage.
Dedicated and secure access for each client, with guaranteed confidentiality.
An explainable module justifies every decision, making auditing easier.
Integrated legal updates to ensure continuous compliance.
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