Vijan.AI
Case StudiesManufacturingControl Automation

Manufacturing

Quality Control Automation

4 autonomous agents inspect, classify, diagnose, and correct quality issues in real-time. 90% defect reduction.

4 Autonomous Agents90% Defect Reduction
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Agentic AI Workflow

4 autonomous agents ensure zero-defect production at scale

The Challenge

Manual inspection was missing defects that reached customers and damaging the brand

An electronics manufacturer had a 2.8% defect rate on its flagship product line, with customer returns costing $4.5M annually. Manual visual inspection caught only 65% of defects, and inspectors suffered from fatigue-related accuracy decline after 2 hours of continuous inspection.

When defects were found, root cause analysis took 3-5 days because process parameters from MES had to be manually correlated with defect patterns. By then, thousands of potentially defective units had already shipped. Corrective actions were implemented through manual machine adjustments that varied by operator skill level.

The manufacturer needed real-time defect detection with automated root cause analysis and corrective action.

The Solution

Agents that inspect every unit, classify defects, find root causes, and correct processes

Vijan.AI deployed 4 agents on the production line. The Vision Inspector analyzes high-resolution images from 12 production line cameras, detecting defects at 99.5% accuracy. The Defect Classifier categorizes each defect by type, severity, and location. The Root Cause agent correlates defect patterns with process parameters from MES (temperature, pressure, speed, material batch) in real-time, identifying causal relationships within minutes. The Corrective Action agent automatically adjusts machine settings for parameter-driven defects or alerts operators for mechanical issues.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Inspection

Quality Inspection Agent

Automated Visual Inspection

Performs high-speed visual inspection using computer vision models to detect surface defects, dimensional variances, and assembly errors, autonomously routing defective units for rework and conforming units to statistical process control.

Input

Product images and sensor measurements from inline inspection stations

Output

Pass/fail decisions with defect classifications and coordinates

  • Invokes vision API with multi-angle product images for defect detection using trained CNN models
  • Queries defect database to classify anomalies by type, severity, and root cause category
  • Autonomous decision: accept, rework, or scrap based on defect severity and repairability
  • Routes accepted units to SPC Controller and rejected units to Bottleneck Analyzer for process insights

Step 2 · Control

Quality Control Agent

Statistical Process Control

Monitors process variation using real-time SPC charts, autonomously detecting out-of-control conditions and triggering process adjustments to prevent defect propagation before reaching compliance review.

Input

Measurement data from accepted units with timestamps and batch IDs

Output

Process control status with corrective action recommendations

  • Calls SPC engine to update X-bar and R charts with rolling window of measurement data
  • Computes control limits dynamically based on process capability indices (Cp, Cpk)
  • Autonomous decision: continue production, halt line for adjustment, or flag for engineering review
  • Sends stable process confirmations to Compliance Tracker for final certification

Step 3 · Analysis

Bottleneck Detector

Defect Root Cause Analysis

Analyzes rejected units to identify process bottlenecks and systematic failure modes, autonomously recommending process improvements and merging findings with compliance data for holistic quality assessment.

Input

Defect data with station IDs, timestamps, and failure modes

Output

Root cause reports with process improvement recommendations

  • Queries process map tool to identify upstream stations contributing to defect clusters
  • Analyzes cycle time data to detect correlation between throughput pressure and quality degradation
  • Autonomous decision: recommend process parameter tuning, maintenance, or design changes
  • Merges root cause insights with compliance tracking for comprehensive quality gate decision

Step 4 · Certification

Compliance Tracker

Regulatory Compliance Certification

Validates product batches against ISO 9001, industry-specific standards, and customer specifications, autonomously issuing certificates of conformance and maintaining audit-ready traceability records.

Input

SPC confirmations and root cause analysis merged at quality gate

Output

Certificates of conformance with full traceability documentation

  • Calls ISO standards API to verify all inspection points meet required tolerances and procedures
  • Queries certification database to generate CoC documents with batch genealogy and test results
  • Autonomous decision: release batch, hold for additional testing, or reject with disposition codes
  • Stores complete audit trail including inspection images, SPC data, and corrective actions taken

Results

Measurable impact within 90 days of deployment

90%

Defect Reduction

Outbound defect rate reduced from 2.8% to 0.28%. Customer returns dropped 85%, saving $3.8M annually.

99.5%

Detection Accuracy

AI vision inspection catches 99.5% of defects vs. 65% for manual inspection. Inspects 100% of units, not samples.

< 5min

Root Cause Time

Root cause identification reduced from 3-5 days to under 5 minutes through automated parameter correlation.

Zero

Batch Escapes

No defective batches have reached customers since deployment. Real-time correction prevents defect propagation.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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