Build intelligent recommendation systems that understand user preferences, solve cold start challenges, and scale to millions of users.
A recommendation engine is an ML system that predicts user preferences and suggests relevant items. It works by analyzing user behavior (collaborative filtering), item attributes (content-based filtering), or both (hybrid systems). Modern engines use neural networks to learn embeddings—dense vector representations where similar users and items are close together.
Product recommendations, similar items, frequently bought together, personalized search.
Video/article recommendations, personalized feeds, continue watching, discovery sections.
Job matching, property suggestions, service provider matching, two-sided recommendations.
Feature discovery, content suggestions, user onboarding flows, engagement optimization.
People you may know, group suggestions, content from network, interest matching.
Product recommendations, personalized offers, next-best-action suggestions.
We build modular recommendation systems designed for iteration. Start simple, measure, and add complexity where it provides measurable lift.
Understand your data, establish baseline metrics, and implement simple models that often work surprisingly well.
Choose the right mix of collaborative, content-based, and hybrid approaches based on your data and use case.
Deploy with proper A/B testing, monitoring, and feedback loops to continuously improve recommendations.
Deep-dive articles on building production recommendation systems, from algorithm selection to scaling.
Understand the core recommendation algorithms and when to use each approach.
Read articleStrategies for recommending to new users and surfacing new items.
Read articleHow neural embeddings and ANN search power modern recommendations.
Read articleWhen to pre-compute vs. generate recommendations on the fly.
Read articleDesign experiments that measure true recommendation quality.
Read articleArchitecture patterns for millions of users and items.
Read articleProduction recommendation systems use a funnel architecture that balances latency with accuracy.
Fast retrieval of 100-1000 candidates from millions of items
Score and order candidates using complex models
A basic recommendation system (popularity + content-based) can be built in 2-4 weeks. Collaborative filtering with decent accuracy takes 1-2 months. A production-grade system with real-time features, A/B testing, and scale optimization typically takes 3-6 months.
Minimum viable data: user-item interactions (views, clicks, purchases) with timestamps. Better results with item attributes (categories, tags, descriptions), user attributes (demographics, preferences), implicit signals (dwell time, scroll depth), and contextual data (device, location, time).
Offline metrics include precision, recall, NDCG, and coverage. Online metrics include CTR, conversion rate, revenue per session, and long-term retention. Also measure diversity and novelty. The best metric depends on business goals.
Based in Bangalore, we help enterprises across India and globally build recommendation systems that drive measurable engagement and revenue lift.
We start with your data, establish baselines, and iterate on algorithms that provide measurable lift—not theoretical improvements.
Our systems handle real-world scale with proper latency budgets, caching strategies, and failover mechanisms.
We set up A/B testing frameworks and feedback loops so your recommendations get smarter over time.
Share your project details and we'll get back to you within 24 hours with a free consultation—no commitment required.
Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002