Safe AI builds the layer around the model: the harness that turns your team's daily work into AI capability that compounds. Inside your walls. Under your control. Accountable by design.
Picking a model is the easy part, and the part you'll redo every six months. What compounds is everything around the model: your workflows, your corrections, your judgment, your institutional knowledge, captured so the organization gets smarter every time work happens. That layer, not the model, is your real IP.
If any factor is zero, the product is zero. Most firms have model access, and zero scaffolding, zero feedback loops. Our three practices build the three factors.
Training. Your people's knowledge, judgment, and pattern recognition, made fluent with modern AI tools. Human capital becomes more valuable as AI capability grows, not less.
AI trainingLEGATE. The institutional harness: context, skills, tools, memory, and orchestration around any model, deployed on infrastructure you own.
The productGovernance. Audit trails, policy checks, evaluations, and continuous oversight: the instrumentation that lets the system learn and lets you prove it's safe.
GovernanceA sovereign, self-hosted AI system that stands up governed teams of AI agents inside your own infrastructure, puts them to work on a mission, and retires them on command. Frontier-grade autonomy with the opposite security model.
Runs entirely on hardware or a private cloud you own, from a single DGX desktop to a full GPU cluster. Zero data egress. Fully offline whenever you need it to be.
From one mission prompt it spawns a purpose-built team of agents, coordinates them, and cleanly retires the team when the mission is done.
Every action is policy-checked, cost-capped, written to an immutable audit log, and reversible. A human is always in command, with the right to turn it off.
Swap the model. Keep the expertise. LEGATE routes every model call through one gateway: local open-weight models by default, frontier models when you choose. Your institutional memory lives in versioned files you own, independent of any model or vendor. Change the engine; the learning system stays yours.
Each engagement builds a factor of your learning system, and each one stands on its own.
Hands-on adoption programs for small and mid-size organizations: Microsoft Copilot for office work; Claude Code, Claude Cowork, and OpenAI Codex for builders; Perplexity and agentic tools for research teams. Every program ships with an acceptable-use policy and a governance starter kit, so you adopt AI safely, not just quickly.
Right-sized governance for organizations without a compliance department: ISO/IEC 42001 and NIST AI RMF readiness, EU AI Act exposure assessment, and, where almost no one else looks, governance of agentic AI: action-level risk, human-override design, and audit instrumentation. Led by a founder with deep roots in cybersecurity.
We partner with university PIs as the AI implementation arm on NSF-funded research, turning proposals into working systems and findings into deployable capability. NSF I-Corps alumni; experienced as a research subawardee and in preparing SBIR and STTR proposals.
Governance isn't a compliance tax bolted on after deployment; it's a property of the harness. The audit log that satisfies a regulator is the same trace that feeds your evaluations. The policy check that stops a bad action is the same scaffolding that encodes your judgment. Own the layer, and safety and capability stop being a trade-off.
Founder & Principal, Safe AI LLC, a San Antonio company born out of an NSF I-Corps program (2023).
Every Safe AI engagement is led directly by its founder, a Ph.D. researcher with a professional foundation in cybersecurity that shapes how we build and govern autonomous systems. He completed the NSF West I-Corps Hub Program in 2023 and received the University of Texas at San Antonio's 2023 Annual Innovation Award for achievements in teaching and scientific research.
When you work with Safe AI, the person who designed the system is the person in the room.
Start with a scoping session: a focused conversation about your data, your constraints, and which factor of your learning system to build first.
sales@safeaihub.com · San Antonio, Texas
