The Third Modality in Factory Intelligence

AI Can See. AI Can Hear.
AI Has Never Felt.

Factories track everything — machines (MES), materials (ERP), warehouses (WMS). The one thing they don’t track: the 300 million workers on the floor. $500B lost every year to the last blind spot in manufacturing.

precision_manufacturingPerception Intelligence for Factory Operations

The Signal

We Found It in the Wrist.

33 published datasets. 15 million recorded actions. 1.15 million participants across independent studies. One finding: temporal patterns in wrist acceleration encode behavioral states — fatigue, attention, skill proficiency, stress.

33Datasets
15M+Actions
1.15MParticipants
0.737AUC
analytics

What AUC 0.737 means: out of every 10 judgments, the system correctly distinguishes fatigued from normal over 7 times. Camera systems: 0.60–0.68. We match their accuracy at $6 vs $200,000.

33 published datasets analyzed

What the System Actually Does

14:15 — Zhang Wei, Plating Line B.

Fatigue score 0.73 (personal baseline 0.45). Plating rhythm slowed from 4.2s/board to 5.1s/board — 21% below his normal pace.

I-01

Verbal Reminder

paymentsZero costschedule5 seconds

Shift leader’s watch buzzes: ‘Plating B — Zhang Wei, pace dropped 21%.’ Leader walks over, quick check: ‘Watch the copper thickness.’

I-05

Target QC

paymentsZero costschedule30 seconds

Downstream QC inspector gets notification: ‘Prioritize Plating Line B output, 14:00–14:30.’ Random sampling becomes targeted inspection. Each defect caught here saves ¥420. Caught at final test: ¥2,000+. Reaches customer: ¥5,000+.

I-03

Insert Break

paymentsLow costschedule15 minutes

If fatigue score hasn’t recovered after 15 minutes, system recommends a break. Shift leader decides. After break: copper thickness deviation returns from ±8.3μm to ±3.1μm.

These three actions exist in every factory today. Shift leaders already do them — by gut feel. We turn gut feel into data. 31 transferable decisions × 4 categories × 4 deployment phases.

See all 31 decisions →arrow_forward
The Hardware
watch

A $5.82 Wristband That Looks Like a Factory ID.

Workers won’t refuse to wear it because it looks like what they already wear.

visibility

Layer 1: Dashboards — $3–5/worker/month

See what’s happening. Fatigue scores, attention levels, shift patterns.

lightbulb

Layer 2: Action Intelligence — $8–15/worker/month

Know what to do about it. The system searches 31 possible actions and tells you which one saves the most money right now.

← This is the product
MethodCostAccuracy
Camera
$50K–200K/lineAUC 0.60–0.68
EEG
$200–500/unitAUC 0.75–0.80
Self-report
~$0Unreliable
iFactory
$5.82AUC 0.737
Founder-Market Fit

We Didn’t Cold-Email Our Way Into Manufacturing.

format_quote

My father has run a factory in Dongguan for 28 years. He makes caps for Hennessy, Chanel, and Dior. I grew up on that floor. This isn’t a thesis project — this is the problem I was born into.

trending_upThe Window

1

Edge AI chips hit $3 — nRF52832 runs inference in <50ms on a $2.40 chip

2

PIPL (2021) made cameras a legal liability — factories need an alternative

3

Labor costs tripled in a decade — the pain is acute now

DZ

Daizhe Zou

Founder & CEO

UC Berkeley, 19. Father’s factory: Wankun ihome Solutions, Dongguan, 28 years. F-1 visa (equity only).

HX

Hongyu Xu

Co-Founder & CTO

UIUC CS + Neuroscience. ML pipeline architecture.

person_addNext hires: Embedded engineer, Time-series ML researcher, China BD lead

pin_dropBuilt at Berkeley. Tested in Dongguan.

One Month In

Founded March 2026. Here’s What One Month Looks Like.

business

Company founded

Delaware C-Corp, 22.5M authorized shares

science

Research complete

9 internal papers, 33 published datasets analyzed

gavel

Patents drafted

11 USPTO provisionals, v7 final

payments

$380K raised

Two SAFEs at $5M post-money cap (Family & Friends)

memory

Product built

SDK AUC 0.737, FastAPI backend, hardware specs finalized

handshake

Pipeline active

Fushi (300852.SZ): proposal sent. Father’s factory: Phase 0 approved.

Key Metrics

Raised$380K
Patents Drafted11
Papers9
Datasets33
Customer Conversations4
format_quote

Samsara proved that instrumenting a workforce’s tools creates a $20B company. We instrument the workforce itself.

Pre-Seed

$500K — $1M

Pre-money $5–10M · Already raised $380K via SAFE ($5M cap) · Monthly burn $3–5K · Runway 6+ years

Use of Funds

15%
File 11 provisional patents
30%
100-unit prototype production
25%
Fushi factory pilot (50 workers)
20%
First full-time hire
10%
WFOE registration (Shenzhen Qianhai)
Q2–Q3 2026

File Patent #1, Phase 0 pilot at father’s factory, register WFOE, hardware v1

Q4 2026–Q1 2027

Submit Paper A & B, Fushi Phase 1 deployment, 100-unit production

Q2–Q3 2027

5–10 factory deployments, SDK licensing conversations, Series A prep

Your Money Buys:

Filed patents. Working hardware. Real factory data. First paying customers.

maildaizhe@tangerineintelligence.ai

Berkeley, CA & Dongguan, China