We brought in Viston to personalize merchandising, improve search, and forecast demand across festive seasons for our mid-market retail brand.

Business challenge

• Low search conversion and high stockouts in Tier-2/3 cities.
  
• Limited personalization across languages and channels (app, web, WhatsApp).
  

Our approach and solution

• Built a multilingual retrieval-augmented search and recommendations layer.
  
• Implemented demand forecasting by micro-region with supply planning triggers.
  

AI applications and benefits delivered

• Personalized recommendations, similar items, and bundles.
  
• Multilingual semantic search (English, Hindi, Tamil) with query rewrite and RAG.
  
• SKU-level demand forecasting feeding replenishment and markdown optimization.
  

Cost of implementation

• USD 39,000 including cloud and data work.
  

Tools and technologies used

• GCP BigQuery, Vertex AI, Recommendations AI, LangChain, FAISS, Cloud Run, Dataflow, dbt, Looker, Redis, Cloud Armor, GitHub Actions.
  

Quantitative outcomes

• +0.9 percentage point increase in overall conversion.
  
• +14% average order value.
  
• -18% customer acquisition cost.
  
• -22% stockouts on top 1,000 SKUs.
  
• 2.1x improvement in search click-through.
  

Key performance indicators (KPIs) tracked

• Conversion rate, AOV, CAC, CTR, add-to-cart rate, stockout rate, forecast MAPE, latency p95.
  

Pre- and post-implementation metrics

• Conversion: 2.4% → 3.3%.
  
• AOV: INR 1,480 → INR 1,689.
  
• Stockout rate: 13.5% → 10.5%.
  
• Search CTR: 8.2% → 17.3%.
  
• Forecast MAPE (top SKUs): 28% → 14%.
  

Stakeholder quotes or testimonials

• “Personalization finally works in Hindi and Tamil—our Tier-2 growth unlocked.” — Chief Growth Officer.
  
• “Planners trust the forecasts; we buy smarter pre-Diwali.” — Head of Merchandising.
  

Regulatory or compliance considerations

• India DPDP Act 2023, consent-based profiling, opt-out mechanisms, PII tokenization, audit logging, model fairness checks for language bias.