Kaelio builds ktx, ktx Cloud, and managed Data Agents so teams can give AI systems governed business context and approved metrics.
Product family
Kaelio is the company behind the open-source engine, the hosted team workspace, and the managed agent experience.
Trusted by data teams at scaling companies
Leadership
Kaelio is built by operators who have worked where AI systems, analytics infrastructure, and enterprise trust all meet.

Co-Founder & CEO
Former data scientist and NLP engineer focused on enterprise data systems, agent reliability, and AI safety.

Co-Founder & CTO
Former AI CTO with 15+ years across data engineering, analytics platforms, and production AI systems.
How we build
We build for the teams that have to trust, operate, and defend agent-generated answers.
Reliable agents need more than database access. They need durable context that every team can inspect, improve, and govern.
The best approval workflow is one data teams can actually keep up with: clear diffs, source evidence, and human review where it matters.
Answers should show where they came from. We build for traceable metrics, cited sources, and less invisible reasoning.
SOC 2, HIPAA, access controls, and auditability are built into the product for teams that need traceable answers.
FAQ
Kaelio is the company behind ktx, ktx Cloud, and managed Data Agents. We build context infrastructure that helps AI systems answer business-data questions with approved definitions, source evidence, and governed access.
ktx is the open-source context layer you can run yourself. ktx Cloud is the hosted, multi-user version for teams that need governance, reviews, SSO, and observability. Data Agent is the managed agent experience Kaelio delivers on top of governed context for business users.
Kaelio is built for data teams, analytics leaders, and operators who want AI agents to use the same metric definitions, source logic, permissions, and business context as their trusted reporting stack.
Raw warehouse access gives an agent tables and columns, but not the business meaning behind them. A context layer gives agents metric definitions, relationships, policies, caveats, and evidence before they answer.
Get Started
Auto-built. Governed by your team. Ready for any agent.