Expert Agents · Industrial AI

Industrial AI that
knows your factory from day one

Generic AI can't work on a factory floor — not because the model is too small, but because it has none of the judgment behind every decision on your line. KeploreAI shows up with a decision-dependency map, so AI works on day one.

Enter · Shift + Enter ↵
Or just tell us what you make — that's enough to start
Or start from here:
keplore-agent · physicalflow-runtime
Manufacturing Vision
Defect Detection
Magnetic, visual & dimensional inspection on production lines
Robotics Integration
Vision & Motion Systems
PLC integration, robot vision calibration, hardware deployment
EV R&D
Battery & Cell Inspection
AI model deployment for EV battery quality and safety validation
Choose your path

Not sure where to start? Pick a path

All three paths enter the same chat — only the AI's first question differs

01
I have a specific AI project in mind
Already know what I need — visual inspection, robot guidance, or document extraction. Looking for a team that can actually deliver.
Start evaluation →→
02
I know there's a problem, not sure if AI can help
Manual quality checks, robots that can't see, or too much manual data entry — not sure if AI is the right answer.
Tell us your problem →→
03
I want to see what AI can do for manufacturing
Not sure if I need AI yet. Want to see real examples first.
See real cases →→
The problem

Why generic AI fails on the factory floor

It's not that ChatGPT isn't smart enough — it just doesn't have the judgment that lives on your line

It doesn't recognize your defects
Foundation models never saw the real problems on your production line. The standards your experts can't quite articulate — the AI can't either.
It can't connect to your equipment
PLCs, MES, cameras, robots — every interface needs specific knowledge. Generic AI doesn't have that wiring.
It doesn't know your pass/fail rules
What's acceptable vs. defective is two years of accumulated experience and edge cases — not in the training data.
Its accuracy drifts in the field
Morning vs. afternoon, equipment aging, lighting changes — real production conditions are harder than any benchmark.
Our approach
For every new scenario, KeploreAI starts by drawing the decision-dependency map with you — defect types, pass/fail thresholds, integration protocols, latency budgets — then the AI shows up on the line with that map. Two years in, 60% of nodes are reusable across customers, which is why cold-starting a second similar project drops from 10 weeks to 4.
Knowledge assets

Not just a concept — 1000+ industrial Skills, ready to run

Every Skill is executable code plus a validation script — not a PDF or a wiki page. Coverage spans vision, robotics, MES integration, Halcon compatibility, YOLO fine-tuning, latency optimization, and more.

1000+production-validated Skills
keplore-registry · Packages · Sample Skills (excerpt)
Skill ID
Type
Status
Size
halcon-socket-bridge
Socket bridge between Halcon and Python, zero-copy image transfer
mcp
reviewed
1.8 MB
yolov8-train-loop
YOLOv8 training loop for industrial defect datasets, early-stop + resume
mcp
reviewed
11.4 MB
model-deployment-profiler
Latency & VRAM profiler on target hardware, FP16/INT8 support
mcp
published
4.2 MB
computer-vision-industrial-manufacturing
Standard manufacturing-vision workflow: calibration, ROI, judgment
mcp
reviewed
18.7 MB
yolov8-industrial-finetune
Few-shot industrial fine-tune, frozen backbone + class reweighting
mcp
published
6.9 MB
cobra-nms-check
NMS threshold regression test — guards against missed detections after upgrades
mcp
deprecated
0.9 MB
halcon-python-api-bridge
Adapter for calling Halcon scripts directly from Python
mcp
reviewed
3.1 MB
training-code-auditor
Audits training code for data-leak and eval-bias patterns
mcp
published
2.3 MB
port-conventions
Port conventions for industrial endpoints (PLC/MES/cameras)
mcp
reviewed
0.4 MB
robotics-knowledge-pack
Robotics base pack: vision + calibration + grasping
Knowledge
reviewed
22.1 MB
halcon-to-ckcd-format-converter
Halcon annotation → CKCD training data converter
mcp
published
1.2 MB
mes-writeback-adapter
MES write-back adapter handling non-standard field mappings
mcp
reviewed
1.6 MB
Full catalog available to engineers via Keplore for Engineers.Keplore for Engineers →
Traction

Real deployments

Three paying customers across two continents — manufacturing vision, EV R&D, robotics integration.

3
Paying customers
2
Continents
Asia · North America
60%
Knowledge reusable across projects
10 → 4 weeks
Cold-start for similar projects
KA-2025-001Running
Asia · Manufacturing
Magnetic Defect Detection
Vision system deployed on production machines
See details→
KA-2025-002Signed
Asia · EV R&D
EV R&D AI Co-worker
Engineer's in-house research assistant
See details→
KA-2025-003Deploying
North America · Robotics
Robot Bucket Localization
Delivered to a system integrator in 2 weeks
See details→
How it works

The decision-dependency flywheel

Each delivery sediments reusable nodes into the knowledge base — the next similar project starts faster.

01
Scope
Scope: map the decision dependencies for this scenario with you
02
Deliver
Deliver: turn each node into an executable Skill (not a doc — code + validation)
03
Sediment
Sediment: generic nodes (Halcon, MES, PLC…) join the knowledge base
04
Reuse
Reuse: the next similar customer cold-starts in 4 weeks, not 10

Tell us your scenario — initial assessment in 5 minutes