Before a defect
leaves the line,
AI finds it first
LineAI adds process-tailored customization on top of our own deep-learning core — detecting equipment anomalies, weld defects and surface flaws in real time, and closing the loop into process control automation. Add an enterprise AI assistant for the office, and the factory-to-desk rollout averages just 4–8 weeks.
Do these quality problems sound familiar?
The three problems we hear most often on the factory floor — and LineAI has a clear answer to each.
The limits of visual inspection
Verdicts vary with inspector skill and fatigue, and micro-defects are simply beyond the human eye.
Escaped defects and recall risk
One defect found after shipment hits customer trust and cost hard. Reacting after the fact is always expensive.
"We don't have enough defect data"
"AI needs thousands of defect samples," they say — a dead end for new lines and low-defect processes.
We don't just sell solutions —
we complete your factory's AI Transformation (AX)
From diagnosis to PoC validation, from build to operational excellence — LineAI owns the entire AX journey as a single partner. Leaving results, not just tools: that is what an AX company does.
Five products, proven on the floor
From line-side anomaly detection to an office AI assistant — see how each product solves real problems, through representative cases and use cases.
Five days before the stop, the process sent a signal
Time-series data from reactors, piping and rotating equipment — temperature, pressure, flow, vibration — is learned in real time, catching subtle deviations before they become incidents or quality drift. Especially effective in continuous processes like chemicals and refining, where one stop means major loss.
Unexplained drift in a continuous process
Intermittent quality drift and equipment trips kept forcing full-batch write-offs, and root-causing took four hours on average. Scheduled inspections showed no warning signs.
Anomaly scores from normal-pattern learning
- 24 channels (temp, pressure, flow, vibration) collected read-only from the existing DCS/PLC
- Model trained in two weeks on normal operation data only
- Instant alerts to owners when anomaly scores cross thresholds
Fewer stops, faster response
Early warnings averaging 5.2 days ahead enabled planned maintenance; unplanned stops fell 37%. Downtime savings alone paid back the investment in seven months. Now extended beyond detection into golden-batch correction proposals (LV.1).
Six months, zero escaped defects
Laser-weld imagery from battery module/pack assembly is fused with process signals to classify porosity, undercut, spatter and cracks in real time. Location and severity are quantified into cell-level quality history.
Visual inspection fatigue — and escapes
Skilled inspectors checked every weld by eye, yet micro-porosity and undercut surfaced downstream 2–3 times a month. In batteries, one weld defect maps directly to fire and recall risk.
Image + signal fusion verdicts
- Bead imagery and current signals analyzed together
- Defect location, type and grade auto-logged to weld history
- Cameras added only — no robot or fixture rework
One inspection standard for everyone
Zero escapes in the six months since launch. Inspectors moved to verdict auditing and process improvement, and weld history now serves as customer-audit evidence.
Started with 30 defect samples, on the line in 4 weeks
Scratches, pinholes, particles and thickness variation on electrode coatings are found at pixel level. Trained on normal images only, it starts even on new lines with almost no defect data.
No defect data to train on
A brand-new line with only 30 defect samples — and "we need thousands" answers from AI vendors nearly killed the project.
Learn normal, flag everything else
- Anomaly model trained on normal product images only
- The 30 defect samples used for validation only
- Model continuously improved with defects found in operation
Overkill and escapes, both under control
Deployed in four weeks at 99.2% accuracy and 1.8% overkill. Never-collected novel defects were still flagged as "different from normal," preventing first escapes.
Time to awareness: 43 minutes to 8 seconds
Process cameras and CCTV are analyzed in real time to detect work anomalies, safety risks and process deviations — with instant alerts. Built on our video-analytics QC/QA core, it unifies quality and safety monitoring in one system.
Dozens of CCTVs, only checked after the fact
People watched dozens of monitors, yet most incidents were confirmed in recordings after the fact — average awareness took 43 minutes.
Real-time stream analysis + instant alerts
- Existing CCTV streams connected as-is
- Abnormal behavior and process deviations detected in real time
- Mobile alerts to owners with event clips attached
Monitoring shifts from watching to responding
Average time to awareness dropped to 8 seconds, with zero safety incidents since launch. Control staff now focus on response, not repetitive watching.
Account A, Q3: 2 contracts in progress ($120K total), owner Sujin Lee, receivable $18K (due in 12 days). HR SystemERP · SalesFinance
All company knowledge, one question away
A RAG-based AI assistant connected to your policies, accounts, HR and finance data. Scattered internal information is found in one conversation, every answer carries its source documents — and full on-premise installation means company data never leaves.
Information scattered everywhere
Policies on the intranet, accounts in spreadsheets, leave in groupware, minutes in shared folders. Finding one fact meant digging through three systems or asking whoever might know.
Modern RAG + permission-isolation architecture
- Hybrid search + GraphRAG handles compound questions in one pass
- Role/department RBAC — answers only from data the user may see
- Encrypted at rest and in transit, with an on-premise LLM for confidentiality
From asking around to just asking
Repetitive lookups on policies, accounts and schedules disappeared, and the assistant now handles most onboarding questions. Source links on every answer also cut misinformation.
We don't build from scratch.
We build your process on a proven core.
Sensor streams & process video
Existing PLCs, sensors and cameras connect as-is. No line rework required.
LineAI Deep-Learning Core
Our own core engine spanning anomaly detection, video QC/QA, time-series analysis and enterprise LLM (RAG) — globally proven deep-learning architectures, optimized for manufacturing data.
Process-tailored customization layer
Dedicated models for welding, surfaces, chemical reactions and battery manufacturing; edge optimization; MES/PLC/DCS/SCADA integration; and closed-loop automation that turns verdicts into control — all owned by LineAI.
Real-time verdicts & quality dashboard
Real-time verdicts start on the line within 4–8 weeks, accumulating into quality data.
That's why rollout is fast — and results are certain
Training generic AI from scratch for every project burns time and data. LineAI focuses on customization on top of a field-hardened core engine — lowering development risk while raising field fit.
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Shorter time to deploy
No core to rebuild — PoC to production averages just 4–8 weeks.
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Lower failure risk
Starting on a field-proven core removes the "we tried, it didn't work" scenario.
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A model that fits your process
No off-the-shelf models — defect characteristics, line speed and lighting are all tuned to your floor.
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Natural systems integration
Linked to MES, PLC and SCADA, inspection data flows straight into your quality systems.
From AI that answers,
to AI that works.
The LLM agent inside LINE ASSIST doesn't stop at finding documents. It calls your MES, ERP, and equipment systems as tools — querying, analyzing, and drafting actions that wait for your approval. People make the judgment calls; the agent does the repetitive work.
From question to system query, analysis, and drafted action — the agent plans and executes on its own. Hand over shift briefings and daily quality reports entirely.
Queries and analysis run autonomously, but any action that changes the real world requires explicit approval. Every step is written to an audit log.
MES, ERP, and quality systems connect through a standard tool interface. Adding a system means adding a connector — the architecture stays the same.
* LINE ASSIST does not replace your MES — it is an agent layer that drives your MES and ERP as tools. Your existing system investment stays intact. Fully on-premise · zero external data transfer.
This screen is how verdicts happen on the line
A faithful demo of AI inspecting products as they flow down the conveyor. Good parts pass; defects are flagged on the spot with coordinates, type and confidence.
* This demo reproduces how the verdict logic behaves. Production screens are configured per process and product.
Defects people miss,
AI reports with coordinates and grades
Beyond pass/fail signals: defect type, location, size and confidence are recorded as structured data. Inspection becomes quality data, and MES integration puts the whole line's quality flow in one view.
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Start with normal data only
No mass defect collection — anomalies are detected from normal-image learning alone.
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Location & grade auto-logging
Each defect is localized and severity-graded, accumulating into quality history.
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Real-time at line speed
Edge-optimized lightweight models keep pace with your conveyor.
Detection is not the end.
Verdicts become control.
The core technology that finds anomalies is the starting point of automation. LineAI implements closed-loop automation — detection wired into equipment control — step by step, from chemical batch processes to assembly lines.
Sense
Real-time collection from sensors, cameras and DCS/PLC
Analyze
Anomaly detection · quality inference · batch end-point prediction
Decide
Correction values computed · action priorities set
Act
DCS/PLC writes · equipment actuation · work orders issued
Golden Batch operation
Temperature, pressure and feed profiles from your best batches become the reference trajectory. When a running batch drifts, corrections are computed and written to the DCS — batch-to-batch quality converges to the golden batch.
Batch end-point prediction
Reaction progress data predicts completion in real time. No habitual buffer time, no over-reaction — batches end exactly when they should.
Virtual quality sensors (soft sensors)
Quality values once known only from lab analysis — concentration, viscosity, composition — are inferred in real time from process sensors. Quality deviations feed control immediately, no waiting for the lab.
Vision verdicts → equipment control
Inspection results drive rejectors and sorting robots to eject defects automatically. Recurring defect patterns feed back into upstream parameter adjustments — reducing the cause itself.
Predictive maintenance → automated workflow
Detected warning signs auto-generate CMMS work orders — with related parts, maintenance history and recommended scheduling attached. Maintenance becomes planned work.
LLM operations agent
When anomalies occur, relevant SOPs and similar past cases are retrieved with step-by-step response procedures. Shift-handover reports are drafted automatically from process data.
Advise
AI proposes corrections and actions; operators execute. The early stage where trust in AI judgment is verified.
Approve
One-click operator approval applies the control. AI judges; humans make the accountable execution decision.
Autonomous
Corrections apply automatically within the validated operating envelope. Outside it, interlocks trigger instantly and operators are called.
Four steps to deploy, zero line rework
Your existing equipment and cameras stay as they are. Each stage is verified before moving on — no leap of faith required.
Free consult · data diagnosis
We listen to your process and inspection goals, then assess feasibility on your existing data.
PoC validation
A tailored model is built on sample data so you see real performance in numbers.
Deployment
Models are installed on edge/servers and integrated with your systems; real-time verdicts begin.
Operate & improve
Dashboards track results while field data keeps sharpening the model.
If PoC results miss the target, we do not proceed to deployment. The process is designed so you see the numbers first, then make the investment decision.
For every line where precision matters
Deployed where a single defect moves quality and cost.
Visit us
Drop by anytime
Bring your field data and we will review feasibility together on the spot. Let us know before you visit and we will be ready.
Will it work on your line?
A 15-minute call will tell.
Tell us your process and inspection goals — we will reply with a feasibility summary and expected impact. PoC consultation on your sample data is free.