AIMM Index
Industry Portal — AI Maturity Across 8 Verticals
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INDUSTRY COMPLIANCE TWINNING PORTAL

One standard. Eight specialized industry pipelines.

AIMM Index continuously monitors AI maturity across 8 industry domains — each with domain-specific data capture, alert thresholds, and pipeline governance. Select a vertical to explore its data pipeline, key metrics, and submit real telemetry.

AI$M = 100 × √(ACI × SIR) — universal formula, domain-specialized intelligence
8
Industry verticals
38/38
Engine tests passing
L0–L6
Universal maturity scale
37,938
MCA entities surveyed
2.3M
Farmers on QAgroTrust
⚡ Semiconductor
🔥 Sponge Iron
🌾 Agriculture
🏥 HealthTech
🚛 Automotive
🚚 Logistics
🛒 Retail
📚 EduTech
⚡ Semiconductor & Electronics

Mineral Processing · Electronics Manufacturing · Clean-room Fabrication

📍 Ispat Godavari, Raipur — HTRS/RED Mineral Separation
67%
AI$M Score
L3 — Managed
L0 Ad Hoc → L6 Production Ready  |  Current: L3 — Managed
DATA PIPELINE — 7 STAGES
1
Feed Characterisation
XRF / petro-mineralogical analysis. Ilmenite, garnet, rutile weight% per mesh size.
2
Separator Operation
HTRS (electrostatic) or RED (magnetic) with 6 key parameters: feed rate, roll speed, temperature, voltage, splitter.
3
Product Quality Assay
Recovery % and concentrate grade per batch run. Confusion matrix: TP/TN/FP/FN based on grade acceptance threshold.
4
AI/ML Model Inference
RSM or zone classifier predicts operating zone (Optimal / Transitional / Sub-optimal).
5
AIMM Telemetry Ingest
Real-time telemetry submission: drift, continuity, encryption, integration scores.
6
Domain Agent Evaluation
FAB agent checks yield variance (>5%), particle drift (>4%/8%). Raises coded findings.
7
AI$M Recomputation
AI$M = 100 × √(ACI × SIR). Score updates. Annexure generated on demand.
KEY METRICS — SEMICONDUCTOR
Ilmenite Recovery (HTRS)
Best observed
52.3%
Concentrate Grade
Ilmenite purity
90.4%
RSM Model R²
Validated
0.9887
SVR LOOCV R²
Drift detected
0.049
HTRS AI$M
L3 Managed
67%
RED AI$M
L4 Optimised
74%
Kiln AI$M
L3 Managed
71%
SUBMIT TELEMETRY — SEMICONDUCTOR
—
—
Live Demo Available
Ispat Godavari, Raipur — HTRS/RED Mineral Separation
Open Demo →
🔥 Sponge Iron & DRI Manufacturing

Direct Reduced Iron · Rotary Kiln · Steel Intermediate

📍 Ispat Godavari Ltd. — Rotary Kiln, Raipur, Chhattisgarh
71%
AI$M Score
L3 — Managed
L0 Ad Hoc → L6 Production Ready  |  Current: L3 — Managed
DATA PIPELINE — 7 STAGES
1
Iron Ore Feed Quality
Fe% total, gangue content, moisture. Upstream HTRS/RED quality directly bounds kiln ACI.
2
Rotary Kiln Operation
Temperature 950–1050°C, coal rate 1.6–1.8 T/T, kiln speed 0.4–0.6 RPM, air injection.
3
Product Quality Sampling
Metallization rate (target ≥90%), total Fe content, carbon %, sulphur %. Quality pass/fail classification.
4
Predictive Maintenance
Equipment uptime, unplanned downtime events, mechanical condition scoring. Continuity score.
5
AIMM Kiln Twin Update
Telemetry: TP/TN/FP/FN from quality batches, continuity_score from uptime, drift from temperature variance.
6
Manufacturing Agent
KILN_CONTINUITY_LOW if Cr < 0.65. KILN_UPSTREAM_CONSTRAINT if HTRS FP rate high.
7
Cascade Governance
Fixing HTRS upstream lifts Kiln AI$M ~5 pts without direct kiln intervention.
KEY METRICS — SPONGE IRON
Metallization Rate
Target ≥90%
92.4%
Total Fe Content
Premium grade
91.8%
Kiln Temperature
Range 950–1050
978°C
Coal Rate
DRI production
1.72 T/T
Production Rate
Nameplate
142 TPD
Continuity Score
Below 0.65 floor
0.61
Kiln AI$M
L3 Managed
71%
SUBMIT TELEMETRY — SPONGE IRON
—
—
Live Demo Available
Ispat Godavari Ltd. — Rotary Kiln, Raipur, Chhattisgarh
Open Demo →
🌾 Agriculture & Agri-Tech

FPO Governance · Farm Digital Twin · Crop Certification · Export Compliance

📍 QAgroTrust™ — 12,847 FPOs · 2.3M Farmers · TRL 5 Deployed
67%
AI$M Score
L3 — Managed
L0 Ad Hoc → L6 Production Ready  |  Current: L3 — Managed
DATA PIPELINE — 7 STAGES
1
Farm Digital Twin Creation
Register farm (FARM-XXXX) with GPS boundary, crop type, soil classification. Assign FPO network.
2
IoT Sensor Telemetry
NPK (XRF spectrometer), chlorophyll (NIR leaf probe), soil moisture/EC, microclimate, PAR.
3
Photosynthesis & GSI Index
GeoSpectral Intelligence computes field-level plant health score every 15 minutes.
4
Batch Tracking
Harvest → drying → cold chain → export. Blockchain QR at every handoff. FSSAI compliance check.
5
Certification Assessment
6-Annexure workflow: soil, input, crop, process, product, export Annexures generated from twin state.
6
AIMM Agri Agent
Drift ≥10%: AGRI_CROP_STRESS_DRIFT_WARNING. Sparse data (<5 events): AGRI_SPARSE_TELEMETRY info.
7
QMM Score Integration
QAgroTrust QMM Score (94.2) maps to AIMM L-layer scores. AI$M computed from QMM + IoT telemetry.
KEY METRICS — AGRICULTURE
Active FPOs
Nationwide
12,847
Registered Farmers
Direct beneficiaries
2.3M
FSSAI Businesses
Licensed
38,247
QMM Score
↑ 2.1 last batch
94.2
Cert Approval Rate
12-month avg
88%
Blockchain Traceability
QR coverage
92%
Agri AI$M
L3 Managed
67.2%
SUBMIT TELEMETRY — AGRICULTURE
—
—
Live Demo Available
QAgroTrust™ — 12,847 FPOs · 2.3M Farmers · TRL 5 Deployed
Open Demo →
🏥 HealthTech & Medical Diagnostics

SaMD · Clinical AI · Diagnostic Accuracy · Patient Safety Governance

📍 NeuroSync ERP™ — SaMD Class B · qEEG Analysis · Anna University Hospitals
78%
AI$M Score
L5 — Advanced
L0 Ad Hoc → L6 Production Ready  |  Current: L5 — Advanced
DATA PIPELINE — 7 STAGES
1
Clinical AI Model Deployment
SaMD class assignment (CDSCO Class A/B/C). Model documentation: training data, validation cohort, intended use.
2
Patient-Level Inference
Diagnostic prediction per patient encounter. Classification: TP (correct positive), FN (missed detection — highest stakes).
3
Diagnostic Accuracy Monitoring
Real-time sensitivity, specificity, AUC tracking. FN rate monitored specially — missed diagnosis = patient harm.
4
System Uptime & Integrity
DICOM connectivity, EHR integration, data encryption (PHI). Continuity floor = 0.70 (tightest of all agents).
5
AIMM HealthTech Twin
Telemetry: TP/TN/FP/FN from diagnostic records, continuity_score from system uptime, drift from accuracy trends.
6
HealthTech Agent (Tightest)
Drift ≥3%: HEALTH_MONITORING_DRIFT_WARNING. Drift ≥6%: CRITICAL. Continuity < 0.70: HEALTH_UPTIME_RISK CRITICAL.
7
Regulatory Annexure
Live .docx with diagnostic performance summary, gap findings, regulatory readiness evidence for CDSCO/NABH.
KEY METRICS — HEALTHTECH
Seizure Detection AUC
CHB-MIT benchmarked
≥0.85
Brain-Age MAE
0–99yr lifespan
<5 yrs
ERP Validation
Reference norms
P300/N400/MMN
Hospital Deployments
Affiliated hospitals
Anna University
Drift Threshold
Tightest agent
3%/6%
Uptime Floor
Patient safety critical
0.70
HealthTech AI$M
L5 Advanced
78%
SUBMIT TELEMETRY — HEALTHTECH
—
—
Live Demo Available
NeuroSync ERP™ — SaMD Class B · qEEG Analysis · Anna University Hospitals
Open Demo →
🚛 Automotive & EV

CV Manufacturing · Parts Lifecycle · Assembly-Line AI · Battery Governance

📍 Leading Indian CV Manufacturer — Parts Lifecycle Prediction (Survey ongoing, NDA)
65%
AI$M Score
L4 — Optimised
L0 Ad Hoc → L6 Production Ready  |  Current: L4 — Optimised
DATA PIPELINE — 7 STAGES
1
Assembly-Line Vision Inspection
AI camera systems classify components: acceptable / reject / rework. TP/FP/FN from quality gate decisions.
2
Parts Lifecycle AI Prediction
ML model predicts remaining useful life (RUL) for engine, brake, suspension components from sensor data.
3
Battery/Drivetrain Monitoring (EV)
Cell voltage variance, thermal gradient, state-of-health (SoH) tracking. Battery drift = critical EV signal.
4
Predictive Maintenance Trigger
Anomaly detection on vibration, temperature, current signatures. MTBF / MTTR calculation.
5
AIMM Automotive Twin
EV context auto-detected from domain. Battery drift threshold: 5% warning / 10% critical.
6
Automotive Agent
AUTO_DEFECT_RATE if FP+FN/total > 6%. AUTO_EV_BATTERY_DRIFT_ALERT if thermal drift ≥10%.
7
Multi-Plant Comparison
AIMM Growth Comparison plots AI$M trajectories across assembly plants for benchmarking.
KEY METRICS — AUTOMOTIVE
Defect Rate Threshold
Triggers alert
>6%
Battery Drift Alert
Critical threshold
10%
Parts RUL Accuracy
Industry target
±12%
Line Uptime Floor
Continuity minimum
0.55
Survey Status
NDA pending
Ongoing
Domain
Auto-detected
Automotive/EV
Auto AI$M
L4 Threshold
65%
SUBMIT TELEMETRY — AUTOMOTIVE
—
—
Live Demo Available
Leading Indian CV Manufacturer — Parts Lifecycle Prediction (Survey ongoing, NDA)
Open Demo →
🚚 Logistics & Supply Chain

Fleet AI · ETA Prediction · Cargo Integrity · Last-Mile Governance

📍 QAgroTrust™ Batch Tracker — Harvest-to-Export Supply Chain
63%
AI$M Score
L3 — Managed
L0 Ad Hoc → L6 Production Ready  |  Current: L3 — Managed
DATA PIPELINE — 7 STAGES
1
Route & ETA AI Model
ML model predicts delivery ETA per shipment. Classification: on-time (TP), delayed (FP), missed (FN).
2
Cargo Condition Monitoring
IoT: temperature, humidity, shock/vibration, GPS. Cold-chain breach detection.
3
Fleet Utilisation Tracking
Vehicle availability, idle time, fuel efficiency, driver behaviour scoring.
4
Last-Mile Exception Handling
Failed delivery attempts, re-routing, customer complaint classification.
5
AIMM Logistics Twin
ETA skew direction matters: FN > FP = under-estimating transit time (late delivery risk).
6
Logistics Agent
LOGISTICS_ETA_SKEW if |FN-FP|/total > 15%. LOGISTICS_DAMAGE_RATE_ALERT if cargo drift ≥10%.
7
Supply Chain Annexure
FSSAI/APEDA export compliance evidence. Cold chain integrity certificate from twin state.
KEY METRICS — LOGISTICS
On-Time Delivery Rate
Last 30 days
87.3%
Cargo Damage Rate
Below 2% target
1.2%
Fleet Utilisation
Active vehicles
78%
ETA Accuracy
Last-mile
±22 min
Drift Alert
Warning/Critical
5%/10%
Cold Chain Breach
Shipments
0.3%
Logistics AI$M
L3 Managed
63%
SUBMIT TELEMETRY — LOGISTICS
—
—
Live Demo Available
QAgroTrust™ Batch Tracker — Harvest-to-Export Supply Chain
Open Demo →
🛒 Retail & MSME

Demand Forecasting · Fraud Detection · Inventory AI · D2C Commerce

📍 Indian Retail MSME — Demand Forecast AI Governance
58%
AI$M Score
L2 — Repeatable
L0 Ad Hoc → L6 Production Ready  |  Current: L2 — Repeatable
DATA PIPELINE — 7 STAGES
1
Demand Forecasting Model
Time-series AI (LSTM/Prophet) predicts SKU-level demand 7/14/30 days out. Critical for inventory planning.
2
Forecast Accuracy Measurement
Actual vs predicted. TP: forecast within 10% of actual. FP: over-forecasted (overstock). FN: under-forecasted (stockout).
3
Fraud & Return Drift Detection
Return fraud rate, cart abandonment drift, payment anomaly detection. RETAIL_FRAUD_RATE alert.
4
Inventory Continuity Monitoring
Stockout events, overstock days, warehouse capacity utilisation. Continuity score proxy.
5
AIMM Retail Twin
Forecast skew direction called out: positive skew (FN > FP) = stockout risk. Negative = dead stock.
6
Retail Agent
RETAIL_FORECAST_SKEW if |FN-FP|/total > 15%. RETAIL_FRAUD_RATE_ALERT if fraud drift ≥12%.
7
MSME Governance Report
AI$M score benchmarked against MCA survey (sector avg 50%). Annexure for GST/compliance filing support.
KEY METRICS — RETAIL
Forecast MAPE
Mean absolute % err
8.4%
Stockout Rate
SKUs/month
3.2%
Overstock Days
Avg carrying days
12
Return Fraud Rate
Flagged transactions
1.8%
Drift Alert
Warning/Critical
6%/12%
MCA Sector Avg AI$M
L2 Baseline
50%
Retail AI$M
L2 Repeatable
58%
SUBMIT TELEMETRY — RETAIL
—
—
Live Demo Available
Indian Retail MSME — Demand Forecast AI Governance
Open Demo →
📚 EduTech & Learning Platforms

At-Risk Learner Detection · Assessment Integrity · Dropout Prevention

📍 Higher Education AI Governance — Dropout Risk & Assessment Integrity
72%
AI$M Score
L4 — Optimised
L0 Ad Hoc → L6 Production Ready  |  Current: L4 — Optimised
DATA PIPELINE — 7 STAGES
1
Learner Behaviour Analytics
Login frequency, video completion, assignment submission, time-on-task. Early warning indicators.
2
Dropout Risk Classification
AI model classifies at-risk learners. FN = missed at-risk student (most critical — intervention not triggered).
3
Assessment Integrity Monitoring
Answer pattern analysis, response time anomalies, plagiarism score drift. Assessment integrity index.
4
Platform Continuity
LMS uptime, video delivery latency, mobile accessibility. Continuity floor 0.45 (lower than industrial agents).
5
AIMM EduTech Twin
Intentionally looser thresholds: 8%/16% drift bands. Seasonal variance (exam periods) tolerated.
6
EduTech Agent
EDU_DROPOUT_MISS_RATE if FN/total > 8%. EDU_INTEGRITY_DRIFT_ALERT if assessment drift ≥16%.
7
Institutional Report
AI$M Annexure for NAAC/NBA accreditation evidence. Learning outcome governance documentation.
KEY METRICS — EDUTECH
At-Risk Detection Rate
Sensitivity
91.4%
Dropout Miss Rate
Below 8% threshold
4.2%
Assessment Integrity
Clean submissions
96.8%
Platform Uptime
LMS availability
99.1%
Drift Alert
Warning/Critical
8%/16%
Intervention Success
At-risk students
73%
EduTech AI$M
L4 Optimised
72%
SUBMIT TELEMETRY — EDUTECH
—
—
Live Demo Available
Higher Education AI Governance — Dropout Risk & Assessment Integrity
Open Demo →
AIMM Index · IS360 Technologies & Services Pvt. Ltd. · Chennai, India · Patent 202641007225 · aimmindex.com