CONSUMER_PACKAGED_GOODS_AI
AI-DrivenDemandForecasting& ConsumerExperience
GenAI + ML-powered Consumer Intelligence Platform integrating probabilistic demand forecasting, reinforcement learning pricing, and personalized consumer experiencesat scale for CPG optimization.
Retail-Gym
ForecastNet
PriceRL
DSSTNE
GenAI4Retail
FORECAST_ERROR_%
40%
Current volatility
INVENTORY_WASTE_$B
$400B+
Annual losses
MARKETING_ROI
1%
Current engagement
CPG_VOLATILITY_CHALLENGE
CPG sector faces extreme volatility
Traditional analytics cannot handle SKU-level granularity
01
Demand Uncertainty
Forecast errors up to 40% in promotions and seasonal peaks
IMPACT
$400B+
02
Inventory Waste
$400 B+ lost annually from overstock / out-of-stock mismatch
IMPACT
40%
03
Marketing Inefficiency
Generic campaigns yield <1% engagement ROI
IMPACT
<1%
04
Channel Fragmentation
Online/offline data silos prevent unified decision-making
IMPACT
Data silos
GenAI + ML-Powered Consumer Intelligence Platform
Probabilistic
Demand forecasting
Reinforcement
Dynamic pricing & promotion
Customer-level
Personalization
Generative
Product content automation
Real-time
Data pipelines
2024–2025 REAL FOUNDATION
Real-World Foundation
01
Retail-Gym
2025
Open reinforcement-learning environment for retail pricing and promotion optimization
SOURCE
github.com/retailgym/retail-gym
02
ForecastNet
Meta, 2025
Transformer-based demand forecasting model trained on multi-retailer SKU data
SOURCE
Meta AI Open Research
03
PriceRL
Stanford, 2024
Multi-agent RL framework for price elasticity and promotion interaction modeling
SOURCE
arXiv:2409.01054
04
Amazon DSSTNE
revived 2025
Large-scale recommender framework optimized for sparse retail data
SOURCE
Amazon Science
05
GenAI4Retail
Hugging Face 2025
Text-to-image product generator fine-tuned for e-commerce visuals
SOURCE
Hugging Face Hub
06
RetailFuse Dataset
2025
Unified 500 GB dataset linking POS, promotions, web traffic, and sentiment streams
SOURCE
RetailAI Consortium
Tensorblue's platform stitches together these proven open projects into one end-to-end intelligent commerce engine
INTELLIGENT_COMMERCE_ENGINE
End-to-End Architecture
01
Data Sources
POS
E-commerce
Loyalty
Social Sentiment
Logistics
Media Spend
↓
02
Data Fabric Layer
ETL + FHIR-style product schema
Delta Lake
Feature Store
↓
03
AI Core
ForecastNet: demand forecasting (Transformers)
PriceRL: reinforcement pricing
DSSTNE: recommender
GenAI4Retail: product content creation
↓
04
Decision Layer
RL Optimizer for promo calendars
Bayesian optimizer for supply allocation
↓
05
Applications
Web dashboards
mobile merchandiser app
marketing copilot
MODULE_A
Demand Forecasting
ForecastNet Integration
Architecture
Hierarchical Transformer (H-Trans)
Modeling SKU-store-day hierarchy for comprehensive demand prediction
Inputs
Past sales
Promotions
Holidays
Weather
Sentiment
Outputs
Probability distribution (P(y_{t+h}|X_t))
WMAPE ↓ by 29% vs LSTM baselines
100K SKUs coverage
Performance
Accuracy: WMAPE ↓ by 29% vs LSTM baselines across 100K SKUs
MODULE_B
Dynamic Pricing & Promotion Optimization
Key Features
Multi-agent RL (Retail-Gym + PriceRL)
Cross-elasticities between SKUs
Competitor response learning
Revenue optimization
Stockout penalty minimization
Price volatility control
Results
Profit uplift
+11%
Inventory variance
-7%
Simulated markets
Validated
Reward Function
R = α × Revenue - β × StockoutPenalty - γ × PriceVolatility
Agents learn cross-elasticities between SKUs and competitor response patterns
MODULE_C
Personalized Recommenders
DSSTNE Deep Autoencoder
Fine-tuned on loyalty data
Tens of millions of users
Multimodal embeddings
Product images (CLIP) + text descriptions
Cold-start solution
Zero-shot user profiling via GenAI embedding similarity
Recommendation Pipeline
Input:
User behavior + product catalog + contextual signals
Processing:
Deep learning embeddings + similarity matching
Output:
Personalized product recommendations with confidence scores
MODULE_D
Generative Content
GenAI4Retail
Capabilities
Stable-Diffusion XL variant trained on 5M branded visuals
Auto-generates product shots, ads, lifestyle imagery
Brand-consistent palettes and styling
Language layer (Retail-GPT): headlines, copy, A/B ad variants
Results
Creative production cycle
2 weeks → 15 minutes
Cost reduction
-93%
Content generation
3000 ads in 48h
Cuts creative production cycle from 2 weeks → 15 minutes
MODULE_E
Operational Intelligence
Predictive Stock Alerts
CatBoost regressors for inventory optimization
TECHNOLOGY
Machine Learning
NLP Anomaly Detection
Retailer feedback analysis for negative-review spikes
TECHNOLOGY
Natural Language Processing
Root-Cause Correlation
Dashboard linking social buzz to sales anomalies
TECHNOLOGY
Analytics
Web / Mobile Layer
Web (Next.js + Plotly)
Interactive forecasting graphs
Promo simulation
Campaign ROI visualization
Mobile (React Native)
Field-sales app showing price elasticity tips
Inventory heat-map
Competitor scanning via camera OCR
Copilot Chat (RAG + LLM)
"Which SKUs are over-discounted in North region?"
Auto-runs SQL + RL simulation
Explains in natural language
Infrastructure & MLOps
| LAYER | STACK |
|---|---|
Data Lake | Delta Lake / Snowflake |
Feature Store | Feast |
ML Training | Ray Tune + PyTorch Lightning |
Model Registry | MLflow |
Orchestration | Airflow |
Serving | FastAPI + Triton Inference |
Monitoring | Evidently AI + Grafana |
Continuous retraining loop updates forecasts daily and retrains RL agents weekly from live POS streams
Evaluation Metrics
| METRIC | BASELINE | TENSORBLUE AI | GAIN |
|---|---|---|---|
Forecast WMAPE | 18.7% | 13.1% | -30% error |
Stockout Rate | 9.8% | 6.1% | -38% |
Gross Margin | — | +8.4% | ↑ |
Creative Cost | $1M / mo | $70K | -93% |
Promo ROI | 1.18× | 1.42× | ↑ 20% |
Model Latency | — | < 2s forecast | Realtime |
Case Studies
01
Dynamic Pricing for Beverage CPG
8 retail chains, 24,000 SKUs
Profit
+11.7%
Waste reduction
-14%
RL engine retrained weekly. Seasonal surge handled via Probabilistic ForecastNet ensemble.
02
AI-Generated Product Imagery
Cosmetics brand
Generated ads
3,000 in 48h
CTR improvement
↑ 61%
Used GenAI4Retail pipeline. Human review pipeline ensured brand tone + compliance.
03
Shelf-Level Demand Prediction
Smart shelf IoT integration
Waste prevention
$2.3M monthly
Source
Spoilage prevention
Integrated smart shelf IoT + ML forecasting for real-time inventory optimization.
Business Impact
Revenue Growth
+6–12% top-line via dynamic pricing + promo optimization
Cost Reduction
30–40% less inventory waste
Speed
90% faster go-to-market campaigns
Brand Agility
Real-time consumer insight loop
Sustainability
Demand-aligned production reduces carbon footprint
Intelligent commerce engine
Tensorblue fuses demand science, creative GenAI, and RL commerce optimization into a single closed loop for multi-brand platform supporting hundreds of SKUs per minute inference.
REFERENCES_2024_2025
Retail-Gym
ForecastNet
PriceRL
DSSTNE
GenAI4Retail
RetailFuse
Scientific core: physics-based forecasting + RL pricing engines. Creative layer: GenAI content generator + LLM marketing copilot. Operational loop: unified data lake + real-time retail connectors.