North India

Recommendation & Personalisation across Punjab

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

Districts
22
PIN codes
527
Cities mapped
22

Recommendation & Personalisation in Punjab

Cold start is where most recommendation systems disappoint, new users and new products are exactly the cases where a good recommendation matters most.

Punjab runs on agriculture and agri-machinery, textiles and hosiery, sports goods, light engineering and food processing, agri supply chains and SME manufacturing, where the practical win is workflow automation rather than frontier models. Where recommendation & personalisation earns its budget here usually follows directly from that mix.

Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move. You own the code, the models where they are open-weight, and the documentation to run it without us.

ਸਤ ਸ੍ਰੀ ਅਕਾਲ , Sat Sri Akaal. We work in Punjabi and English across Punjab.

Punjab coverage

State / UT
Punjab
Region
North India
Districts covered
22
PIN codes covered
527
Cities mapped
22
Working languages
Punjabi, 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 Punjab?

Yes, all 22 districts and 527 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Punjab sectors do you work with most?

Across Punjab the economy leans towards agriculture and agri-machinery, textiles and hosiery, sports goods, light engineering, food processing. Agri supply chains and SME manufacturing, where the practical win is workflow automation rather than frontier models.

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 Punjab

Covering all 22 districts. Tell us what you are trying to change.

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