East India

Recommendation & Personalisation across Odisha

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

Districts
31
PIN codes
922
Cities mapped
16

Recommendation & Personalisation in Odisha

Orqent Labs builds recommendation systems evaluated by controlled experiment, reported against revenue or engagement rather than a leaderboard metric.

Odisha runs on steel and metals, mining, aluminium, ports and handloom, heavy industry, where safety monitoring and predictive maintenance carry direct cost impact. 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. We hand over with runbooks, tests and a team that knows how it works, not a dependency.

ନମସ୍କାର , Namaskāra. We work in Odia and English across Odisha.

Odisha coverage

State / UT
Odisha
Region
East India
Districts covered
31
PIN codes covered
922
Cities mapped
16
Working languages
Odia, 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 Odisha?

Yes, all 31 districts and 922 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Odisha sectors do you work with most?

Across Odisha the economy leans towards steel and metals, mining, aluminium, ports, handloom. Heavy industry, where safety monitoring and predictive maintenance carry direct cost impact.

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 Odisha

Covering all 31 districts. Tell us what you are trying to change.

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