Yunjue Technology · 云玦科技
Your Life, Rendered as Context
What we build
An agent that grows with you, not a mirror you stare at
Mainstream wearables are mirrors: they reflect heart rate, steps, and sleep numbers — but never tell you why, and never grow with you. Yunjue works in two layers: the always-on hardware is an external sensory system — continuously watching, listening, and measuring without interrupting your conscious flow; the cloud AI is an external prefrontal cortex — turning raw signals into reflection, review, and personalized decision support, so the agent actually understands you and grows with you.
Three-layer moat
From signal to application, layer by layer
Early Fusion multimodal
Not text-then-concat
Self-Evolving Agent
In-Situ Self-Evolving · 5 benchmarks, 3 SOTA / 2 runner-up
Zero-Skill personalization
Each card is a standalone HTML
Each layer depends on the one below. Without Early Fusion the agent sees no dense signal; without self-evolution the application stays one-size-fits-all. Full reasoning on the tech page.
From Early Fusion to Human-Centric World Model
What we can do today, where we're headed
Today · Early Fusion
Mainstream multimodal AI uses Late Fusion: audio, heart rate, vision are each compressed to text first, then concatenated — losing all temporal and intensity details. Yunjue aligns 7 modalities at the raw-signal layer, making conclusions precise to event, moment, and individual.
Self-report vs. body
You say "felt fine," but the heart-rate curve says stressed. What you report in words and what your body actually shows are aligned in the same window — the gap language never catches is surfaced.
Same activity, across days
The same sit-and-type session, today vs. the 14-day baseline, can look very different physiologically. "Today vs. normal" becomes a measurable quantity.
Multimodal causal chains
"Heart rate rose 3 seconds before that sentence was spoken" — heart rate, voiceprint, and vision aligned at the raw layer construct causal chains language alone never produces.
Long-term goal · Human-Centric World Model
Today we borrow the alignment capabilities of mainstream multimodal models to run Early Fusion while accumulating dense "unusual modalities → human behavior outcome" labeled data. When the data flywheel matures, Yunjue's long-term goal is to train a truly human-centric multimodal foundation model — aligning heart rate, IMU, audio, vision, dialogue, profile, and relationship graphs at the raw-signal layer to build a world model that genuinely understands the person, not a compressed version of general internet knowledge.
Roadmap
Three tracks running in parallel
Now — Always-on hardware → iOS → cloud main loop is production-stable; the full self-evolution loop is live; deep internal trial is underway.
Near term — Scale the self-evolution sandbox from small-batch precision tuning to industrial operation at the thousand-user public-beta scale, with quality gates moving into automation.
Mid term — In-house multimodal hardware v1 ships; the always-on device becomes a true external sensory system. End / edge privacy split goes live, making raw-data residency a user choice.
Long term — Three tracks converge: the Self-Evolving Agent delivers per-individual personalization at scale; the always-on hardware becomes a true external sensory system; the data flywheel matures into a self-trained Human-Centric World Model that understands people, not just the internet.
Join us
We're building a team that can lead long-horizon tracks end-to-end
Hardware, on-device multimodal, Realtime, Self-Evolving Agent, self-trained Human-Centric World Model — for every track, we're hiring people who can take full ownership end-to-end. Especially welcome: peers who already live with quantified-self, smart wearables, or personal AI.