What we optimize for.
Production risk before novelty
We look at failure modes, controls, evaluation coverage, escalation paths, and cost exposure before choosing an agent framework or model stack.
Governed systems from the start
RAG, GraphRAG, tool use, gateways, and guardrails are designed with ownership, auditability, regulatory context, and security boundaries from the start.
Operations your team can take over
When we finish, your team has the code, runbooks, eval suites, control maps, and operating model needed to run the system without us.
Senior people for advanced AI systems.
We're a focused team with backgrounds in machine learning research, production engineering, security-minded architecture, and operations consulting. The work usually sits between model behavior, infrastructure, governance, and business process, so we keep senior people close to the implementation.
We keep the team small on purpose. It's the only way to keep advanced agent, RAG, gateway, and compliance work senior from end to end.
Production AI needs operating judgment, not imported demo playbooks.
Advanced AI programs now have to reconcile model capability with data residency, vendor exposure, EU AI Act risk classes, Vietnam and regional privacy rules, security review, and executive governance. That work does not fit inside a vanilla chatbot build.
We started Trannell AI for teams that want to move beyond prototype agents without pretending the hard parts are only prompt engineering. We help decide when a basic implementation is enough, when a platform should be bought, and when custom architecture is justified.
"If an AI system can't be evaluated, governed, and operated, it isn't production yet."
Want to see if we're a fit?
Start with an architecture review. We'll tell you where the production risks are, whether a smaller implementation is enough, and what should be governed before anything ships.