AI · Recommendation · Malaysia

Smarter suggestions.
Better engagement.

Infrawise develops AI-driven recommendation and personalisation systems tailored for Malaysian e-commerce platforms, content publishers, and digital marketplaces.

+60 3-5748 2316

[email protected]

Kuala Lumpur, MY

Three core recommendation services

Each engagement is scoped around your platform's specific data landscape and user behaviour patterns. No off-the-shelf modules — we build for your context.

RM 7,500

Product Recommendation Engine

Custom AI recommendation systems for e-commerce and content platforms. Covers collaborative filtering, content-based, and hybrid approaches — from data modelling through A/B testing and catalogue integration.

Collaborative & hybrid filtering
User behaviour data modelling
A/B testing setup included
Catalogue integration support

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RM 5,800

Content Personalisation System

Personalisation layers that adapt article feeds, media suggestions, and website content to individual browsing patterns. Includes user profiling, automated content tagging, and real-time serving architecture.

User profiling model development
Content tagging automation
Real-time serving architecture
Media & publisher-ready design

RM 3,500

Recommendation Analytics & Tuning

Ongoing analysis and optimisation for deployed recommendation systems. Covers click-through and conversion tracking, cold-start mitigation, and diversity-relevance balance tuning as your catalogue and user base expands.

Performance metric tracking
Cold-start problem strategies
Feedback loop refinement
Diversity-relevance tuning

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Built for Malaysian platforms, not generic deployments

Domain-specific modelling

We model recommendation logic around the actual product categories and content formats your platform uses — not a generic template applied across industries.

Malaysian user behaviour focus

Local shopping patterns, bilingual content signals, and regional marketplace dynamics are factored into every model we develop for MY-based platforms.

Integration-first architecture

Systems are scoped to fit your existing technology stack. We work alongside your team and hand over documented, maintainable infrastructure.

A/B testing from the start

Recommendation quality is measured, not assumed. Every engagement includes structured experimentation so you can observe what actually improves your metrics.

Responsible data handling

User data is handled in line with PDPA Malaysia requirements. We document data flows clearly and support your compliance obligations at every stage.

Transparent performance reporting

You receive clear reporting on recommendation impact — CTR, conversion lift, coverage metrics — so the value is visible and traceable over time.

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Your recommendation layer is worth getting right

Whether you're starting from scratch or looking to improve an existing system, a conversation with our team helps clarify what's realistic for your platform.

[email protected] · Kuala Lumpur, Malaysia

Questions about working with Infrawise

What types of businesses are a good fit for a recommendation system?

E-commerce platforms with product catalogues of 50+ SKUs, content publishers with regular article or video output, and digital marketplaces with multiple vendors tend to see the most meaningful improvement. The more user interaction data available, the stronger the recommendations become over time.

How much data do we need before starting?

There's no fixed minimum, but richer historical data (user sessions, click events, purchase records) leads to more accurate initial models. For newer platforms with limited data, we can begin with content-based approaches and transition to collaborative filtering as your data grows. We'll discuss what's realistic during the scoping call.

How long does a typical engagement take?

A Product Recommendation Engine engagement typically spans 8–12 weeks from data scoping to integration testing. Content Personalisation projects are usually 6–10 weeks. Analytics & Tuning is an ongoing arrangement, often reviewed on a monthly cycle. Timelines depend on data readiness and your internal integration capacity.

Does our team need AI or ML knowledge to work with you?

No. We handle the modelling and algorithmic work. Your team needs to be able to provide access to relevant data and support API or front-end integration. We document everything clearly and walk your developers through how the system operates.

What happens after the initial build is complete?

You receive a fully documented system and a handover session. If you'd like ongoing support, the Analytics & Tuning service covers model performance monitoring, feedback loop refinement, and adjustments as your catalogue and user base evolve. Many clients begin with a build engagement and move to ongoing tuning once the system is live.

How is user data managed and kept secure?

We work within the constraints of Malaysia's Personal Data Protection Act (PDPA 2010). Data handling responsibilities and boundaries are agreed in writing before any project begins. We do not retain client user data beyond the agreed project scope.

What is the pricing structure?

Pricing for each service is listed on our solutions page: Product Recommendation Engine from RM 7,500, Content Personalisation System from RM 5,800, and Analytics & Tuning from RM 3,500. Final scoping may affect the total — contact us to discuss your specific situation.

Our Location

40 Jalan Imbi, 55100 Kuala Lumpur, Malaysia

Let's talk about your platform

Tell us about your current setup and what you'd like recommendation AI to do for you. We'll get back within one business day.

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