North India

Recommendation & Personalisation across Rajasthan

Recommendations that lift the metric you care about, measured by experiment, not by offline accuracy. Covering every district and PIN code in Rajasthan.

Districts
32
PIN codes
992
Cities mapped
29

Recommendation & Personalisation in Rajasthan

Offline accuracy and revenue are different things. We measure recommendations by experiment against the business metric, because that is the only number that pays anyone.

Rajasthan runs on mining and minerals, tourism, textiles, cement, handicrafts and solar energy, mining and utility-scale solar, where asset monitoring and field-data capture are the recurring problems. Where recommendation & personalisation earns its budget here usually follows directly from that mix.

Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners. You own the code, the models where they are open-weight, and the documentation to run it without us.

नमस्ते , Namaste. We work in Hindi and English across Rajasthan.

Rajasthan coverage

State / UT
Rajasthan
Region
North India
Districts covered
32
PIN codes covered
992
Cities mapped
29
Working languages
Hindi, English

What is included

  • Event tracking design, since most projects start with inadequate data
  • Baseline popularity model to beat
  • Hybrid collaborative and content-based ranking
  • Cold-start handling for new users and new items
  • A/B testing framework with proper statistics
  • Business-metric reporting, not just offline accuracy

Questions

Do you cover all of Rajasthan?

Yes, all 32 districts and 992 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Rajasthan sectors do you work with most?

Across Rajasthan the economy leans towards mining and minerals, tourism, textiles, cement, handicrafts, solar energy. Mining and utility-scale solar, where asset monitoring and field-data capture are the recurring problems.

How much data do we need?

Less than people assume to start. A content-based approach works from day one; collaborative filtering improves as interaction volume grows.

How do you handle new products?

Content-based features carry new items until interaction data accumulates, with deliberate exploration so new items get a fair chance to be seen.

How do we know it is working?

Controlled A/B tests measured on revenue or engagement, with proper statistical treatment rather than eyeballing a dashboard.

Recommendation & Personalisation in Rajasthan

Covering all 32 districts. Tell us what you are trying to change.

Or email bd@dtrasglobal.com · call +91 74118 77878