Intelligent agents that survive outside the demo.
From hardware-embedded agents that sense the physical world to enterprise agents that transform how teams work — we design, train, and refine intelligent agents that fit seamlessly into their environment, and keep getting better.
Agents are not chatbots with extra steps. They are systems that reason with context, act on real business state, and survive production pressure. Building them well requires infrastructure, not just prompts.
We don't build AI features. We build agents.
We build the foundational Agent layer for real business environments — model call orchestration, prompt composition, tool invocation, context management, and end-to-end log tracing. This is the chassis everything else runs on.
For each core user scenario, we design dedicated Agent Workflows that dynamically invoke different capability modules based on user state, device state, history, and business rules. Not one prompt to rule them all — a structured system that reasons.
We build prompt template management and scenario configuration systems — supporting rapid experimentation across different user segments and task objectives. Paired with tool-calling and contextual memory, agents connect to real systems rather than staying in demo mode.
We establish mock data environments, integration test flows, exception handling, log replay, and version iteration mechanisms — so agents can survive and continuously improve in real product environments, not just pass a demo.
Four agents, four environments — each one shaped by the specific constraints of its context.
Embedded agent for a premium smart fishing gear brand — identifies fish species in real time, delivers personalized fishing advice, and proactively monitors weather conditions.
Agent built into premium ski equipment — analyzes skiing habits and skill level from sensor data, then delivers personalized coaching that evolves with every run.
Agent embedded in a high-end smart cosmetic mirror — tracks skin health over time and delivers personalized skincare recommendations that adapt to seasonal changes.
Deployed an AI agent across an entire organization via Feishu — automating data analysis, synthesizing user research, and building a living knowledge base.
Bring us a real scenario, a real constraint, and a real user. We'll bring the rest.