Northeast India
Recommendation & Personalisation across Meghalaya
Recommendations that lift the metric you care about, measured by experiment, not by offline accuracy. Covering every district and PIN code in Meghalaya.
- Districts
- 7
- PIN codes
- 65
- Cities mapped
- 3
Recommendation & Personalisation in Meghalaya
Orqent Labs builds recommendation systems evaluated by controlled experiment, reported against revenue or engagement rather than a leaderboard metric.
Meghalaya runs on agriculture, tourism, mining and handicrafts, dispersed operations where connectivity constraints shape what can realistically be deployed. Where recommendation & personalisation earns its budget here usually follows directly from that mix.
We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong. You own the code, the models where they are open-weight, and the documentation to run it without us.
Meghalaya coverage
- State / UT
- Meghalaya
- Region
- Northeast India
- Districts covered
- 7
- PIN codes covered
- 65
- Cities mapped
- 3
- Working languages
- 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 Meghalaya
Every district has a coverage page listing its PIN codes.
Other capabilities across Meghalaya
Questions
Do you cover all of Meghalaya?
Yes, all 7 districts and 65 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.
Which Meghalaya sectors do you work with most?
Across Meghalaya the economy leans towards agriculture, tourism, mining, handicrafts. Dispersed operations where connectivity constraints shape what can realistically be deployed.
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 Meghalaya
Covering all 7 districts. Tell us what you are trying to change.
Or email bd@dtrasglobal.com · call +91 74118 77878