Capability
Recommendation & Personalisation across India
Recommendations that lift the metric you care about, measured by experiment, not by offline accuracy.
- Industries
- 12
- Stack options
- 7
- Typical first release
- 6 weeks
What recommendation & personalisation means when we build it
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.
We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
Multi-model by default, so a provider outage is a routing decision rather than an incident. We hand over with runbooks, tests and a team that knows how it works, not a dependency.
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
Who this is for
We usually work with e-commerce heads, product managers, growth leaders and content platforms, the people who own the outcome rather than the tooling decision.
Recommendation & Personalisation by industry
Each sector changes the constraints, regulation, systems of record, and what a wrong answer costs.
- Recommendation & Personalisation for E-commerceconsumer protection e-commerce rules
- Recommendation & Personalisation for Retailconsumer protection rules
- Recommendation & Personalisation for Media & Entertainmentcopyright law
- Recommendation & Personalisation for Education & EdTechDPDP Act 2023
- Recommendation & Personalisation for Travel & Tourismtourism ministry guidelines
- Recommendation & Personalisation for HospitalityFSSAI for food service
- Recommendation & Personalisation for Financial ServicesRBI guidelines
- Recommendation & Personalisation for BankingRBI master directions
- Recommendation & Personalisation for SaaS & TechnologySOC 2
- Recommendation & Personalisation for TelecommunicationsTRAI regulations
- Recommendation & Personalisation for InsuranceIRDAI regulations
- Recommendation & Personalisation for Real Estate & Construction TechRERA compliance
Recommendation & Personalisation, stack options
We pick per workload. Each page states the honest trade-off.
- Recommendation & Personalisation with Pythonframework
- Recommendation & Personalisation with PyTorchframework
- Recommendation & Personalisation with PostgreSQLdata
- Recommendation & Personalisation with pgvectordata
- Recommendation & Personalisation with TypeScriptframework
- Recommendation & Personalisation with Snowflakedata
- Recommendation & Personalisation with Databricksdata
Questions we get asked
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.
Considering recommendation & personalisation?
Tell us the workflow and the constraint. We will tell you honestly whether it is worth building.
Or email bd@dtrasglobal.com · call +91 74118 77878