AI Control Plane
Centralize runtime governance for production LLMs with policy enforcement, traceability, audit logs, routing, and continuous visibility into output quality, risk, usage, and performance.
Deploy production LLMs with stronger oversight, faster issue detection, and enforceable controls that reduce hallucination risk before bad outputs reach users. Trussed AI helps enterprises monitor model behavior in real time, trace decisions end to end, and apply governance, audit, and reliability safeguards across apps, agents, and workflows.
Runtime controls, monitoring, and assurance services for safer, more reliable production LLM operations.
Centralize runtime governance for production LLMs with policy enforcement, traceability, audit logs, routing, and continuous visibility into output quality, risk, usage, and performance.
Control agent actions before tool calls, data access, and workflow triggers execute, reducing hallucination-driven downstream errors across multi-agent systems and automated processes.
Generate continuous evidence for every governed interaction, including policy results, model versions, timestamps, and lineage to investigate unreliable outputs quickly and confidently.
Design governance strategies, review workflows, and operating models that help teams move from AI experimentation to production-ready LLM oversight with clearer accountability.
Track and control AI spend in real time while optimizing model selection, helping teams balance hallucination mitigation, performance, and budget across production environments.
Improve resilience with intelligent routing, failover, and continuous monitoring so production LLM applications maintain service quality even when providers or models fluctuate.
Hallucination mitigation is most effective when controls operate in the live path of AI interactions, not after incidents occur. Trussed AI helps enterprises monitor outputs, enforce policies, trace model behavior, and generate audit-ready evidence across apps, copilots, and agents. The result is stronger reliability, faster root-cause analysis, and safer deployment of production LLMs in regulated and high-stakes environments.
See how enterprises improve AI reliability, governance, and audit readiness in production.
Built for enterprises that need reliable AI operations with enforceable controls.
Policies are enforced during live AI interactions, not only documented after deployment.
Every governed interaction includes logs, lineage, and evidence for faster investigation of unreliable outputs.
Supports regulated environments with SOC 2 Type II, ISO 27001, and audit-ready controls.
Drop-in proxy integration adds monitoring and guardrails without major application code changes.
Experienced founders building reliable enterprise AI infrastructure.
Co-Founder
Ajay Dankar is Co-Founder of Trussed AI and brings nearly three decades of cloud product and engineering leadership to enterprise AI reliability. His background spans Google Cloud, AWS, Adobe, PayPal/eBay, and Visa-acquired Finsphere, where he worked on scaling, load balancing, cloud cost optimization, and fraud detection. At AWS, he led product management for Elastic Load Balancing, helping drive adoption and operational savings. That experience now informs Trussed AI's focus on production-grade governance, resilience, and control for generative and agentic systems. Ajay is especially focused on helping enterprises deploy AI safely across public and hybrid cloud environments. He holds a Master's degree in Electrical Engineering and Computer Science from the University of Florida and a Bachelor of Technology from IIT Delhi.
Co-Founder
Branden McIntyre is Co-Founder of Trussed AI and focuses on infrastructure that helps enterprises deploy AI reliably at scale. Across roles at Rakuten, Cisco, JustAnswer, and Oracle, he saw the same recurring challenge: promising AI pilots often lacked the controls and operational tooling needed for production use. His work leading AI prediction initiatives and machine learning implementations sharpened his understanding of what reliable deployment actually requires, from observability to governance. At Trussed AI, Branden applies that experience to closing the gap between experimentation and production operations for LLMs and agents. He helps organizations implement AI systems with stronger oversight, safer workflows, and better operational confidence. Branden holds an MBA from UC Berkeley Haas and a Master of Science from New York University.
Co-Founder
Sunita Reddy is Co-Founder of Trussed AI, where she leads AI, operations, and partner strategy for enterprise adoption of generative and agentic AI. With more than two decades of experience across product, AI, and design, she specializes in turning emerging technologies into scalable, enterprise-ready solutions. At JustAnswer, she led initiatives that integrated large language models into core workflows, including copilots, conversational interfaces, and human-in-the-loop systems that improved engagement and accuracy. Earlier roles at Microsoft and Accellion strengthened her expertise in product innovation, partnerships, and operational scale. At Trussed AI, Sunita helps organizations identify high-impact AI use cases while building the governance and execution layers needed for dependable production deployment. She holds graduate and undergraduate engineering degrees from the University of Maryland and Osmania University.
Monitor hallucinations by combining runtime logging, policy checks, traceability, and output review signals. Effective monitoring captures prompts, model versions, responses, tool calls, policy evaluation results, and downstream actions. Teams should track exception rates, unsupported claims, citation failures, escalation frequency, and drift across models or workflows. Continuous traces make it easier to detect patterns early and investigate root causes quickly.
Talk with our team about monitoring, governance, and production safeguards.
Validated controls for secure operations.
Recognized information security standard.
Continuous evidence for governed AI.
Share your AI use case, deployment model, and governance goals. Our team will help you evaluate monitoring, mitigation, and runtime control options for production LLMs.
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To help us assist you faster, please include the reason for your message so the relevant team can reach out as soon as possible.