We work across the layers that turn models into useful products: tools, knowledge, evaluation, interaction, infrastructure, and platform design.
We design AI agents that use tools, follow workflows, inspect context, and complete tasks in real software.
PrincipleWe build reliable agent workflows, not vague “autonomy.” The goal is controlled execution, useful results, and clear human review.
We build systems that retrieve the right information, remember useful context, and connect ideas in knowledge graphs.
PrincipleWe aim for grounded answers that show their sources and how concepts connect.
We build evaluation and observability loops that reveal failures and guide steady improvement.
PrincipleWe treat AI quality as an engineering loop: observe, test, measure, and prevent regressions.
We build realtime AI interfaces that combine speech, text, images, and interaction.
PrincipleWe focus on latency, language support, and interaction quality. Voice systems fail when they feel slow or unnatural.
We build video AI agents that analyze live or recorded streams and connect relevant events to video management systems.
PrincipleVideo AI should help people find and review relevant events without flooding them with alerts or hiding uncertainty.
We test local and low-latency AI infrastructure for faster responses, stronger privacy, and lower costs.
PrincipleWe optimize the full pipeline: input, inference, tool calls, output, latency, and deployment.
We design AI platforms that move from prototype to production by separating models, tools, data, evaluation, and deployment.
PrincipleScalability is more than traffic. Systems must be easy to maintain, monitor, extend, and operate safely.