Central India
Recommendation & Personalisation across Chhattisgarh
Recommendations that lift the metric you care about, measured by experiment, not by offline accuracy. Covering every district and PIN code in Chhattisgarh.
- Districts
- 20
- PIN codes
- 272
- Cities mapped
- 11
Recommendation & Personalisation in Chhattisgarh
Orqent Labs builds recommendation systems evaluated by controlled experiment, reported against revenue or engagement rather than a leaderboard metric.
Chhattisgarh runs on steel and sponge iron, coal and mining, power generation and agriculture, power and metals, where plant-level data already exists and is simply not being used. 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. Six weeks to something running in production, not six quarters to a strategy document.
नमस्ते , Namaste. We work in Hindi and English across Chhattisgarh.
Chhattisgarh coverage
- State / UT
- Chhattisgarh
- Region
- Central India
- Districts covered
- 20
- PIN codes covered
- 272
- Cities mapped
- 11
- 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
Districts of Chhattisgarh
Every district has a coverage page listing its PIN codes.
Other capabilities across Chhattisgarh
Questions
Do you cover all of Chhattisgarh?
Yes, all 20 districts and 272 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.
Which Chhattisgarh sectors do you work with most?
Across Chhattisgarh the economy leans towards steel and sponge iron, coal and mining, power generation, agriculture. Power and metals, where plant-level data already exists and is simply not being used.
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 Chhattisgarh
Covering all 20 districts. Tell us what you are trying to change.
Or email bd@dtrasglobal.com · call +91 74118 77878