The Probabilistic Risk Engine
for Modern Retail.
Predict operational failures 48-72 hours in advance. Sentinel uses causal ML to separate true risks from operational noise — delivering an 88% lift in precision over rule-based systems.
The Deterministic Trap
"If Inventory < Min, Order More."
- Latency: Reacts only after the shelf is empty.
- Blindness: Ignores incoming shipments (In-Transit) and velocity changes.
- Noise: Triggers thousands of false alarms for safe items.
The Sentinel Way
"What is the probability this fails in 48h?"
- Prediction: Alerts 2 days before failure.
- Context: Sees "Phantom Inventory" and stochastic demand.
- Precision: 88% lift in alert precision vs. heuristics.
The Intelligent Sidecar
Sentinel doesn't replace your ERP (Oracle/SAP). It augments it.
Ingest
Universal Adapters
Seamlessly connects to your existing ERP and WMS (Oracle, SAP, Manhattan) to ingest daily snapshots without disrupting operations.
Transform
Behavioral Signals
Converts raw data into predictive signals: Velocity Trends, Supply Reliability, and "Virtual Inventory" visibility.
Predict
Causal AI
Uses advanced causal models to separate true risks from operational noise, identifying failures 48 hours in advance.
Monitor
Automated Governance
Continuous accuracy checks ensure the model adapts to changing seasonality and trends automatically.
Validated Performance
99.5% Overall Accuracy (AUC: 0.98)
Across all SKU-locations (most of which are safe), Sentinel achieves 99.5% overall accuracy. The harder metric — alert precision on the stockout class — is 66.2% vs 35.2% for rule-based systems. That's the 88% lift that eliminates alert fatigue.
Benchmark vs. Heuristic
| Metric | Rule-Based | Sentinel |
|---|---|---|
| Precision (Trust) | 35.2% | 66.2% |
| Alert Volume | 40/day | 16/day |
*Result: 88% Lift in precision, reducing operational noise by half.
Zero Brittleness
We stress-tested with inventory errors (+/- 10%) and demand surges (+50%). Impact on model accuracy was negligible (-0.03%).
# Production Inference Check def validate_model(test_set): score = model.predict(test_set) assert score > 0.99 # Automated Governance stability = check_stability(train, test_set) if not stability: trigger_retraining() return "Production Ready" >> Output: 66.2% Stockout Precision | AUC: 0.98
From "Monday Morning Panic"
to "Sunday Night Plan."
Before Sentinel
"Our merchandising team spent Monday mornings reacting to weekend stockouts. We were losing $50k/week in missed revenue on top-sellers alone."
With Sentinel
"We flipped the script. We now get a 'Sunday Night Risk Report.' By shifting inventory proactively, we prevented 40% of our recurring stockouts and drove a 1.5% lift in same-store sales in Q1."
Built for Scale
From "Fit Gap" analysis to automated drift detection, we engineered Sentinel for the messy reality of enterprise data.
| Feature | 📉 Heuristic Rules | 🛡️ Sentinel ML |
|---|---|---|
| Decision Logic | Static Thresholds (e.g., Min < 5) | Causal Predictive AI |
| Constraint Handling | Ignores interactions (e.g. Lead Time vs Demand) | Natively handles non-linear interactions |
| Drift Monitoring | None. Rules break silently. | Automated Integrity Checks |
| Deployment | Hardcoded in SQL/ERP. | Docker Container / API. Weeks not months. |
Automated Governance
Continuous monitoring of feature distributions. If the Training Baseline deviates from Live Inference, the system auto-flags for retraining.
Invariant Scalability
Evaluated across 5 scenarios (Small to High Scale). AUC remained >98.7% in all cases. The pipeline is invariant to data scale.
Calculate Your Savings Potential
See how Sentinel's precision engines capture the most critical stockouts.
*Based on Sentinel's validated 66.2% alert precision vs 35.2% for rule-based systems. Estimated recovery assumes 40% of flagged stockout revenue is protected by early intervention.
Projected Monthly Savings
Projected Annual Savings
The 30-Day Pilot
No infrastructure changes. No live integration. Deployed in 5 business days.
What you provide
- → 30-day ERP/WMS snapshot (Oracle, SAP, Manhattan, or CSV)
- → Item master + store list
- → No live system access required
What we deliver
- → Daily risk scores for every SKU-location
- → Precision benchmark vs your current rules
- → Phantom inventory audit
- → Feature importance breakdown
Success criteria
- → ≥40% lift in alert precision vs heuristics
- → ≤2 missed stockouts in top-50 revenue SKUs
- → Full validation report with go/no-go recommendation
Pilot Pricing
30 days from $2,500 / ₹2,00,000
Fixed scope. No surprises. Full refund if we don't hit the precision target.
Ready to stop firefighting?
Contact us
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