Retail & E-commerce
Fair Demand AI for Quick Commerce
Customer Profile
A leading quick commerce company operating in 40+ Indian cities, promising 10-minute delivery through a network of dark stores. The company was rapidly expanding from metros to Tier 2 and Tier 3 cities.
Challenge
The company’s AI systems, trained primarily on metro city data, were failing in newer markets:
Demand Forecasting Bias
- Models trained on Mumbai/Delhi/Bangalore data
- Tier 2/3 city demand patterns fundamentally different
- Festive and regional event patterns not captured
- 40% higher stockout rates in expansion cities
Customer Service Language Gap
- Support AI only reliable in English and Hindi
- Southern and Eastern India customers underserved
- 60% of non-Hindi complaints escalating to human agents
- Customer satisfaction 25% lower in non-metro markets
Dark Store Placement
- Location algorithms optimized for metro density patterns
- Tier 2 cities with different urban layouts
- Catchment area assumptions incorrect for smaller cities
- Delivery radius calculations based on metro traffic patterns
Inventory Allocation
- SKU preferences varying significantly by region
- Metro-centric assortment pushed to all cities
- Local preferences not reflected in stocking
- Wastage high for items with regional demand variation
Solution
Rotavision deployed AI fairness infrastructure for equitable expansion:
Vishwas for Demand Forecasting Fairness
- Bias detection in demand models across city tiers
- Regional event calendar integration (local festivals, functions)
- Performance parity monitoring by geography
- Continuous model retraining for expansion cities
Sankalp for Sovereign Customer AI
- On-premise deployment for customer data sovereignty
- Multi-lingual support AI across 8 Indian languages
- Regional context understanding for queries
- Compliance with DPDP Act for customer data
Regional Intelligence Layer
- City-tier specific demand patterns
- Local festival and event impact modeling
- Regional taste and preference mapping
- Weather-adjusted demand (monsoon variations by region)
Inventory Fairness
- Assortment recommendation by city profile
- Regional SKU performance tracking
- Stockout parity across geographies
- Wastage reduction through localized prediction
Language Deployment
| Language | Cities | CSAT Score |
|---|---|---|
| Hindi | All 40+ | 4.2/5 |
| English | All 40+ | 4.4/5 |
| Tamil | Chennai, Coimbatore, Madurai | 4.1/5 |
| Telugu | Hyderabad, Vijayawada, Vizag | 4.0/5 |
| Kannada | Bangalore, Mysore, Hubli | 4.2/5 |
| Bengali | Kolkata, Siliguri | 4.1/5 |
| Marathi | Mumbai, Pune, Nagpur | 4.3/5 |
| Malayalam | Kochi, Trivandrum | 4.0/5 |
Implementation
| Phase | Focus | Duration |
|---|---|---|
| Assessment | Bias audit of demand and service AI | Week 1-3 |
| Data | Regional data collection and integration | Week 4-6 |
| Model | Fairness-aware retraining | Week 7-10 |
| Language | Multi-lingual support deployment | Week 8-12 |
| Monitoring | Guardian deployment for ongoing parity | Week 11-14 |
Results
| Metric | Before | After | Change |
|---|---|---|---|
| Tier 2/3 Forecast Accuracy | 58% | 81% | +40% |
| Stockout Rate (Tier 2/3) | 12% | 7.8% | -35% |
| Non-Hindi CSAT | 3.2/5 | 4.1/5 | +28% |
| Human Escalation Rate | 60% | 22% | -63% |
| Wastage (Expansion Cities) | 8% | 4.5% | -44% |
Geographic Parity Dashboard
Real-time monitoring ensures fair performance across markets:
| Metric | Metro | Tier 2 | Tier 3 | Variance |
|---|---|---|---|---|
| Forecast Accuracy | 86% | 81% | 78% | 8% |
| Stockout Rate | 5% | 7.8% | 9% | 4% |
| Delivery Success | 97% | 95% | 93% | 4% |
| CSAT | 4.3 | 4.1 | 3.9 | 0.4 |
Business Impact
- Tier 2/3 expansion accelerated with confidence in AI performance
- 8 new cities launched in 6 months post-implementation
- Customer acquisition cost reduced 20% in expansion markets
- Regional partnerships enabled with fair demand forecasting
Regional Insights Discovered
The fairness analysis revealed valuable patterns:
- South India: Higher demand for fresh produce, lower for frozen
- East India: Sweet and snack categories outperform national average
- Festival impact: Regional festivals drive 3x demand spikes (vs 2x for national)
- Weather: Monsoon patterns affect demand differently by region
Operations Feedback
“We were copying our Mumbai playbook to every city and wondering why it wasn’t working. The fairness analysis showed us that Lucknow and Jaipur have completely different demand patterns. Now we plan for each city’s unique profile.” — Head of Expansion
What’s Next
The company is extending fairness infrastructure to:
- Delivery partner allocation equity
- Pricing fairness across geographies
- Dark store worker scheduling optimization
- Sustainability metrics by region
Rotavision is powered by Rotascale’s globally-proven AI trust platform.
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