Three recommendation services, each scoped to your platform
We work from a focused service catalogue. Each offering covers a distinct phase or need in the recommendation infrastructure lifecycle — from initial build through ongoing optimisation.
Back to HomepageData audit first
Every engagement begins with a review of your existing data — interaction logs, product catalogue structure, user identifiers — to determine what's available and what modelling approach fits.
Build and test together
We develop models iteratively and review them with your team before integration. A/B testing is set up before go-live so you have a baseline comparison from day one.
Documented handover
Deliverables include technical documentation your developers can use independently. We walk through the system in a structured handover session at the end of each project.
Product Recommendation Engine
Development of AI recommendation systems for e-commerce and content platforms, using collaborative filtering, content-based, and hybrid approaches. The service covers user behaviour data modelling, recommendation algorithm selection and training, A/B testing setup, and integration with existing product catalogues. Built for Malaysian online retailers and marketplaces.
What's included
- User behaviour data audit and feature engineering
- Algorithm selection: collaborative, content-based, or hybrid
- Model training and offline evaluation
- A/B testing framework setup
- Product catalogue integration and API delivery
- Technical documentation and handover session
Process
Data scoping call
Review your existing data sources, catalogue structure, and integration environment.
Feature engineering & model selection
Prepare user-item interaction data, evaluate algorithm options, agree on architecture.
Training, evaluation, refinement
Train models, measure offline metrics, iterate based on your team's feedback.
Integration & A/B go-live
Deploy to your environment, set up A/B test split, confirm metric tracking is working.
Handover and documentation
Deliver documented codebase and walkthrough session with your technical team.
Typical duration: 8–12 weeks · Best for: e-commerce, marketplaces, retail
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Content Personalisation System
Building personalisation layers that adapt website content, article feeds, and media suggestions to individual user preferences and browsing patterns. The engagement includes user profiling model development, content tagging automation, and real-time serving architecture setup. Suitable for media companies, publishers, and content platforms.
What's included
- User profiling model development
- Automated content tagging pipeline
- Real-time personalisation serving architecture
- Session and preference signal ingestion
- Front-end integration guidance
- Documentation and handover
Process
Content audit and tagging strategy
Review your content catalogue and agree on a tagging taxonomy for personalisation signals.
User profiling model development
Build implicit interest profiles from browsing, engagement, and history data.
Tagging automation setup
Implement automated content classification to maintain tag quality as new content is published.
Serving layer and integration
Deploy personalisation API and integrate with your front-end content rendering.
Typical duration: 6–10 weeks · Best for: media, publishers, content platforms
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Recommendation Analytics & Tuning
Ongoing analysis and optimisation of deployed recommendation systems through metric tracking, model performance evaluation, and feedback loop refinement. Services include click-through and conversion analysis, cold-start problem mitigation strategies, and diversity-relevance balance tuning. Designed to maintain recommendation quality as user bases and catalogues grow.
What's covered each month
- CTR, conversion, and coverage metric tracking
- Model performance evaluation and drift monitoring
- Feedback loop review and refinement
- Cold-start mitigation for new products or users
- Diversity-relevance balance tuning
- Monthly performance report in plain language
Ongoing engagement · Best for: active platforms with growing catalogues and user bases
Discuss This ServiceCompare services and find the right starting point
| Feature | Product Rec. Engine RM 7,500 |
Content Personalisation RM 5,800 |
Analytics & Tuning RM 3,500/mo |
|---|---|---|---|
| Collaborative filtering | Partial | Monitoring | |
| Content tagging automation | — | — | |
| A/B testing framework setup | Review only | ||
| Real-time serving architecture | — | ||
| Cold-start mitigation | |||
| Monthly performance reports | — | — | |
| Best for | E-commerce, marketplaces | Media, publishers | Active platforms, post-build |
Many clients begin with Service 01 or 02 and transition to Service 03 once live. Talk to us about sequencing.
Technical principles across all services
Security & Privacy
Data scoped to purpose. Anonymisation applied where identifiable data is not required for modelling. PDPA 2010 alignment throughout.
Performance Benchmarks
Serving latency targets agreed before integration. Model quality measured using standard metrics: precision, recall, NDCG, and coverage.
Responsive Support
During active engagements, questions are responded to within one business day. Post-handover support is available under the Analytics & Tuning service.
Agreed Scope Documents
Every engagement is governed by a written scope document. Changes are discussed and agreed before work begins, not managed ad hoc.
Clean, Maintainable Code
We write for the engineers who will maintain the system after handover. Code is documented, linted, and structured for clarity over cleverness.
API-First Delivery
All recommendation outputs are served via documented REST APIs, making front-end integration straightforward regardless of your platform stack.
Clear pricing, no hidden additions
Product Rec. Engine
- Full model build and training
- A/B testing setup
- Catalogue integration
- Documentation + handover
Content Personalisation
- User profiling model
- Content tagging automation
- Real-time serving setup
- Documentation + handover
Analytics & Tuning
- Monthly metric review
- Model health monitoring
- Cold-start + diversity tuning
- Plain-language report
Not sure which service to start with?
A brief conversation about your platform and data environment is usually enough to identify the most sensible starting point. There's no obligation.
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