ScaleIQ AI
AI operations intelligence for industrial weighing and logistics
ScaleIQ AI ingests weight tickets, scale telemetry and dispatch events, then applies anomaly detection and forecasting to surface yard congestion, calibration drift and revenue leakage in real time.
- Artificial Intelligence
- Manufacturing Software
- Business Intelligence
Business problems solved
What was broken
Manual ticket reconciliation
Operators key in weights twice, creating reconciliation drift between yard and billing.
Invisible calibration drift
Scales lose accuracy gradually and nobody notices until an audit.
Unpredictable yard congestion
Peak load times are guessed at rather than forecast.
Primary features
What we built
Ticket intelligence
OCR + validation of inbound tickets with automated exception routing.
Drift detection
Statistical models flag scale calibration drift days before tolerance breach.
Throughput forecasting
Rolling forecasts of yard load by hour, lane and material type.
Revenue assurance
Cross-checks ticket weight, contract rate and invoice line to catch leakage.
Interactive demonstration
Explore ScaleIQ AI
Switch modules, step through the workflow and query the assistant. Everything runs on mock data inside this guided experience.
Tickets / day
3,180
+9%
Exception rate
1.8%
-42%
Drift alerts
7
+2
Recovered revenue
$214K
+18%
Operational trend
Tickets processed · Auto-resolved exceptions
Technology stack
What it runs on
- React
- TypeScript
- Supabase
- PostgreSQL
- AI APIs
- OCR
- Business Intelligence
- Enterprise Security
- Analytics
Development highlights
Engineering notes
- Hybrid rules + ML exception engine with human-in-the-loop feedback
- OCR pipeline tuned for low-quality yard camera captures
- Executive BI layer with drill-through to raw tickets
Future roadmap
Where it goes next
- Predictive maintenance for scale hardware
- Carrier scorecards
- Native driver mobile app
Architecture
System layers
- 01
Operator Console
- 02
Ingestion API
- 03
Authentication
- 04
Rules & ML Engine
- 05
PostgreSQL
- 06
Object Storage
- 07
Analytics
- 08
AI Services
Estimated demonstration metrics
Scope of the build
Illustrative scale for this guided experience.
0
Estimated screens
0
Estimated features
0
Estimated API endpoints
0
Estimated database tables
High
Architecture complexity
6 months
Development timeline
Performance target
Streaming ingest at 1.2K events/min
Estimated Demonstration Metrics — illustrative values for this guided experience, not measured production statistics.
Workflow walkthrough
How work moves through it
- 1
Capture
Scale telemetry and ticket images stream into the ingestion API.
- 2
Extract
OCR pulls weights, material codes and hauler identifiers.
- 3
Validate
Rules engine checks tare history, contract rates and tolerance bands.
- 4
Predict
Models score congestion risk and calibration drift.
- 5
Act
Exceptions route to operators; clean tickets post automatically.
Product walkthrough
Recorded tour
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Guided ScaleIQ AI walkthrough
Core capabilities
- OCR ingestion
- Anomaly detection
- Forecasting
- Revenue assurance
- Operations BI
Continue the tour