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Models That Keep Learning on Your Device
Not downloading a frozen AI — raising one that's yours. We've proven on-device continual learning end-to-end on real hardware. The model grows smarter with use. Your data never leaves.
Who we are
Full-stack edge AI infrastructure
From C++ inference kernels to on-device app templates — six layers, all built in-house. We don't wrap someone else's API. We don't sell cloud compute. We give developers and enterprises AI that evolves on their own devices, with data staying on user-controlled devices.
Core Breakthrough
We solved the hardest problem in on-device AI
Running large models on-device is no longer the challenge. The entire industry is stuck on the real question: can on-device AI evolve with its user? We've proven the full loop end-to-end on real devices.
✕
Prompt stuffing / RAG
More context = more memory; it's sticky notes, not real memory
✕
Fine-tuning / weight modification
Catastrophic forgetting — learns you, forgets everything else
Neural Imprint
We bypassed both walls. Zero weight modification, no fine-tuning, no on-device training compute — yet the model "knows you." The user profile is imprinted directly into the model's internal state, not pasted into context as a sticky note. This capability is protected by 12 filed invention patents.
You use your apps normally — finance, chat, photos, reading
The device locally understands your behavior patterns, forming a cognition of "who you are"
That cognition is imprinted into the model's internal state — next boot, the model recognizes you instantly
The entire process keeps data on your device, never reaching any third-party server
Evidence, not stories
Done / Validating / Vision
We used our own capital to prove the hardest part. Every ✅ below is backed by real device logs and data.
200
200 consecutive conversation rounds on three real devices, zero crash, zero OOM
16/16
8 life domains × 2 real devices = 16/16 scenarios passed
1,000+
Six-repo full-stack
0
Real data flows via encrypted device-to-device channel, zero third-party servers
Neural Imprint full loop end-to-end on real devices
8 life domains × 2 real devices = 16/16 scenarios passed
200 consecutive conversation rounds on three real devices, zero crash, zero OOM
User calibration loop: correct → regenerate → model updates, dogfood verified on real device
Personalization profile extraction running on real devices, measurable improvement observed
Real data flows via encrypted device-to-device channel, zero third-party servers
Six-repo full-stack · 1,000+ automated tests · 5-layer test pyramid
🔭 What's next
Smart routing in shadow mode, real decisions transitioning gradually
Every device ships with a continual learning engine — phones, robots, AR glasses, cars, industrial terminals all need a brain that thinks and evolves locally. We're building it.
The learning layer for embodied AI — robots generate massive private real-time data that can't all be uploaded to the cloud. What we've proven on phones is the exact infrastructure embodied AI can't bypass.
Privacy becomes architecture, not policy — data stays on user-controlled devices and never reaches third-party servers. When every device learns locally, the center of gravity in the data economy shifts from "who hoards the most data" to "whose device best understands its owner."
Moat
Why only we can do this
01
Cloud giants won't
OpenAI / Google / Anthropic make money selling compute — on-device AI evolution directly undermines their business model. They are structurally disincentivized to build this.
02
Open source can't fully
Not a single algorithm breakthrough — requires owning six layers simultaneously, from C++ inference kernel to end-user app. We have not seen a public stack that owns all six.
03
Three-layer moat
Open layer (Apache 2.0 + patent grant) · Patent layer (12 invention patents) · Closed layer (Edge Runtime + trade secrets)
Six-repo full stack: C++ inference kernel → on-device SDK → self-learning orchestration → app scaffold → optimization workbench → Python core algorithms
Product Matrix
Free tools + Paid engine
Free tools (attract developers)
Edge Studio
Desktop workbench: model analysis → quantization → pruning → distillation → benchmark → export
Free tools (attract developers)
Edge Scaffold
Open-source on-device app scaffold (Apache 2.0), Apple-first with cross-platform expansion
Free tools (attract developers)
Vanilla
LLM inference visualization (live)
Revenue core: Edge Runtime
The only runtime that correctly loads our optimized models and restores Neural Imprint personalization state. On-device inference avoids cloud token costs and has software-infrastructure margin characteristics.
Dailyn (finance), Narrus (reading), Mealens (food), Ururu (companion), SpriteSpeak (kids' stories) — all free, no ads, no data sales. They're algorithm research testbeds, not the company's core business.
Market Opportunity
$50–80B market, no infrastructure player
TAM
$50–80B
Global edge AI software + applications (2028)
SAM
$100–115B
China edge AI + information security software (2027)
Sources: IDC, Canalys/Omdia, MarketsandMarkets, and other third-party public data
Four-person founding team
From ByteDance, Baidu, JD.com, Weibo, Huawei, and Fortune 500 — combining original research, international perspective, and sharp product execution.
Shuong
Founder
Siqi Chen
Co-Founder & Chief Scientist
Jay Tian
Co-Founder & Chief Product Officer
Xi Wu
COO & Government Relations
We face risks head-on
Personalization quality doesn't reach production grade
Baseline revenue doesn't depend on personalization — toolchain + inference engine can sustain
Platform vendors open on-device SDK
Structural conflict (control UX vs. open SDK); we're cross-platform and vendor-neutral
Competitors replicate
12 patents + six-layer full stack + trade secrets
The chance to define on-device AI
We've already proven the hardest technical viability with our own capital. Now we need investment to accelerate commercialization — turning our six-layer full stack into the industry standard. Full BP, patent portfolio, and live device demos available under NDA.