West India

Recommendation & Personalisation across Maharashtra

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

Districts
34
PIN codes
1,583
Cities mapped
40

Recommendation & Personalisation in Maharashtra

The popularity baseline is humbling and necessary. Plenty of sophisticated systems fail to beat 'show what is selling', and you should know that before deploying one.

Maharashtra runs on financial services, pharmaceuticals, automotive, media and entertainment and chemicals and petrochemicals, regulated finance and pharma, where every AI system has to carry an audit trail before it carries a benefit. 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.

नमस्कार , Namaskār. We work in Marathi and English across Maharashtra.

Maharashtra coverage

State / UT
Maharashtra
Region
West India
Districts covered
34
PIN codes covered
1,583
Cities mapped
40
Working languages
Marathi, 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 Maharashtra?

Yes, all 34 districts and 1,583 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Maharashtra sectors do you work with most?

Across Maharashtra the economy leans towards financial services, pharmaceuticals, automotive, media and entertainment, chemicals and petrochemicals. Regulated finance and pharma, where every AI system has to carry an audit trail before it carries a benefit.

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 Maharashtra

Covering all 34 districts. Tell us what you are trying to change.

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