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Industrial systems · Applied AI · Custom engineering

See the data.
Decide with confidence.
Build the next decade.

Every era has a handful of companies that redefine what manufacturing means. In the next decade, we believe factories will think for themselves: data flows on its own, machines speak for themselves, quality proves itself. Our job is to put that capability into the hands of manufacturers — industrial software we write ourselves, AI that runs on your own floor, so every factory can have its own industrial brain.

Local inference node · GPU 78% / 4 cameras
jianchuan / mes — shopfloor LIVE 01 / 04
OEE
92.4%
Machines online
128/132
97.0% connected
Output today
12,480pcs
↑ 6.2% day on day
Cycle42.6s
First pass96.1%
Utilization88.3%
Downtime12min
WIP3,240pcs
Output, last 7 daysTGT 11,000 · pcs
VISIONLine 2, station 3 — 1 scratch found, rejectedConfidence 0.982 · 09:24
First pass yield
98.6%
Lots in inspection
42lots
0 overdue
Open exceptions
3open
All assigned
Defect rate1.4%
Cpk1.12
Complaints0cases
Re-check pass99.2%
Concessions1lots
Defects by type · this week%
Scratch
Missing part
Dimension
Appearance
Other
SPCLine 3 Cpk 1.12 on dimension · below 1.33Sent to process engineering · 10:02
Collection uptime
99.2%
Data points
2,864pts
5s refresh
Active alarms
2on
1 high / 1 medium
Instant power386kW
Energy today4,120kWh
Gateways4/4
Packet loss0.02%
Scan cycle5s
Press 2 · last hour
Spindle vibration Oil temp Pressure
ALARMPress 2 oil temp 78.4°C over limit · speed reduced10:07
Slot occupancy
76.3%
Movements today
1,842orders
↑ 4.1% day on day
Kit completeness
99.1%
2 material shortages
To putaway36orders
To pick52orders
Turns6.4/mo
Count variance1
Capacity8,640slots
Zone densityA1–D12
A1A7D12
CHECKSlot A-07 mixing risk · to be recheckedMove to A-12 · 10:11
Products

One product line, one delivery standard

Four lines of business: industrial systems keep the floor running, visualization makes the numbers visible, AI covers what the eye has to judge, and custom engineering fills the gaps standard products leave. All on-premise by default.

Overcast aerial view of the plant: grey-white workshops and trucks at the loading bays
One plant, dozens of machinesMachine, order and quality data on one thread.
Two managers in front of a production scheduling dashboard in the meeting room
The whole business, on one screenOutput, delivery, quality and stock, drilled down together.
Industrial camera and ring light above a conveyor, parts passing the inspection point
Every part gets looked atModel verdict, linked reject, image archived.

Illustrative render · AI-generated

Implementation

Prove one link, then extend the chain

Neither digitalization nor AI has to land all at once. Start where delivery, cost or capacity hurts most, get data out of that one spot, then extend milestone by milestone. Five stages, each with something you can sign off.

01

Site survey and diagnosis

Output · Diagnosis report, cost estimate

Line walkthroughs, process mapping and a data audit, so the problem is pinned down first.

02

Solution design and blueprint

Output · Implementation plan, milestones

System boundaries, integration points and hardware choices settled up front, with scope you can trace.

03

Build, configure, integrate

Output · A build you can demo

Standard modules configured, custom work written, machine and ERP interfaces tested end to end.

04

Pilot run and training

Output · Manuals, training

Run in parallel and compare the numbers, train by role, and count it delivered only when people pass the check.

05

Acceptance and tuning

Output · Sign-off sheet, support plan

Go through the acceptance list item by item, then move into long-term support and tuning.

From diagnosis to sign-off, one team How we deliver