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Divinci is the safety, security and governance layer for custom language models and agents. Evaluate before you ship, release with human sign-off and instant rollback, and hand regulators verifiable evidence for the EU AI Act, GDPR, HIPAA and NIST AI RMF.
“Is chicken healthy?”
Safety, security and governance controls, built into the development platform your team already ships on.
Multi-stakeholder review and sign-off for ML engineers, QA teams and compliance officers, with every approval recorded against the release it governs.
Centralized version control for custom LLMs with automated rollback capabilities, A/B testing frameworks, and comprehensive model lineage tracking.
Conduct comprehensive voice-based model evaluation with automated test case generation and performance benchmarking across modalities.
Monitor hallucination rates, unsafe-output rates, bias, latency and custom accuracy metrics with real-time dashboards and alerting.
EU AI Act · Annex IV readyMost AI compliance stops at access logs and prompt-level guardrails. vIndex goes inside the model. Every entity association is a queryable feature in the vIndex; every edit is a portable, auditable rank-1 patch. GDPR Article 17 right-to-erasure with verifiable proof. EU AI Act Annex IV technical documentation generated from the vIndex itself. The model becomes the audit trail.
See how it works →Accelerate model deployment cycles with parallel testing pipelines, automated staging environments, and zero-downtime rollouts.
Domain-specific test suites and adversarial scenarios for healthcare, finance and legal LLMs, with bias, jailbreak and leakage detection.
Automated training, evaluation and deployment workflows with safety gates, approval steps and progressive rollout strategies.
Native connectors for Weights & Biases, MLflow, Kubeflow, and enterprise model serving platforms with unified observability.
Govern thousands of model versions across distributed teams with horizontal scaling, multi-region deployment and federated policy enforcement.
We publish vIndexes — queryable feature databases extracted from open transformers — for every model we use. Twelve live on Hugging Face today, spanning four model families and four organizations, plus two natively-trained 1-bit dissolution controls.
The viewer lets you orbit the FFN feature space in 3D. The blog series walks the universal-constants to surgical-edits.
Gemma
Qwen
Llama
Mistral
Microsoft
Three numbers that hold within ±15% across 8 models from 4 organizations. One vanishes the moment you go to 1-bit weights.
A single rank-1 weight patch removes one association from the index at +0.02% perplexity. What the model says afterwards is measured separately, not assumed.
Two natively-trained 1-bit models, two organizations, same anomaly: structure gone, behavior survived.
Meet the innovators shaping the future of human-AI collaboration.
CEO & Founder
Visionary with 15+ years in enterprise technology, leading the conversational AI revolution.
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CTO & Co-Founder
Product strategist with a track record of delivering user-centric AI solutions for enterprise teams.
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CPO - Chief Project Officer
Systems architect with expertise in scalable AI infrastructure and natural language processing.
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COO - Chief Operations Officer
Cornell graduate with extensive experience in financial services and entrepreneurship. Former M&A Associate and BrainPOP Clean Energy founder, bringing business and operations expertise to AI scaling.
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Data Scientist
Data scientist with 10+ years programming experience, specializing in machine learning and Python development. He builds reinforcement-learning and multi-agent systems, with an M.S. from UC San Diego.
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AI Safety and Ethics Advisor
AI governance leader and independent advisor specializing in vendor management and risk. Founded AI LA Responsible AI Group and brings expertise from Google AI, Deloitte, and CVS Health.
ConnectSee how teams evaluate, release and evidence custom models and agents on Divinci. Book a short walkthrough.
We'll find a time that works — a short walkthrough of the platform.
How Divinci handles safety evaluation, secure deployment and governance evidence for custom language models and agents. Clear answers to the questions your risk, security and engineering teams ask.
Every release passes a multi-layered validation gate: automated regression, safety, performance and security testing, plus expert human review. Nothing reaches production without the evaluation record and the approval that goes with it.
Our platform supports a broad spectrum of large language models, from open-source to proprietary architectures. Flexible integration enables seamless deployment across diverse frameworks and enterprise environments, ensuring adaptability to evolving business needs.
Continuous monitoring, scheduled audits and real-time tracking of safety and quality signals. Drift, unsafe outputs and policy violations are flagged early, and the trail of what happened is retained for auditors and regulators.
Divinci is a development platform first. Robust APIs, an SDK, a CLI and MCP tools slot into your existing release and development pipelines, so governance is enforced where your engineers already work rather than bolted on afterwards.
