Build intelligent search and recommendation systems that understand user intent, learn from behavior, and deliver relevant results. Our Bengaluru team implements semantic search, collaborative filtering, content-based recommendations, and hybrid systems that drive engagement and revenue.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
Semantic Search Implementation Replace keyword matching with meaning-based search using embedding models (Gemini, OpenAI, Cohere). Users find what they mean, not just what they type. We implement semantic search over product catalogues, knowledge bases, and content libraries with sub-100ms latency. Recommendation Engine Development Build recommendation systems using collaborative filtering (users who liked X also liked Y), content-based filtering (items similar to what you've viewed), and hybrid approaches. We handle cold-start problems, real-time personalization, and A/B testing frameworks to continuously improve recommendation quality. Hybrid Search (Vector + Keyword) Combine dense vector search with sparse keyword search (BM25) for the best of both worlds. Hybrid search captures semantic meaning while preserving exact-match precision for product SKUs, names, and technical terms. Search Analytics & Optimization Track search KPIs — click-through rate, zero-result rate, search-to-conversion, and query abandonment. We build feedback loops that use click and purchase data to improve ranking quality over time, and implement search analytics dashboards for your product team. Personalization Engine Build user models from browsing behavior, purchase history, and explicit preferences. Personalize search results, homepage content, email recommendations, and push notifications based on individual user profiles that update in real-time.
AI-Native Search Expertise We build search systems using the latest AI techniques — embedding models, re-rankers, learning-to-rank, and LLM-powered query understanding. Not just Elasticsearch with synonyms, but genuinely intelligent search that improves with usage. Measurable Impact Every search and recommendation implementation includes success metrics — CTR, conversion rate, NDCG, and revenue attribution. We set up A/B testing from day one so you can measure the impact of every improvement. Bengaluru Engineering Team Direct collaboration with search and ML engineers in Bengaluru. Fast iteration on ranking models, embedding strategies, and personalization logic with same-timezone coordination.
E-Commerce Product Search & Discovery Customers search for 'blue running shoes under 5000' and get exactly what they want — with semantic understanding of intent, price filtering, and personalized ranking based on their preferences and browsing history. Content Platform Recommendations Recommend articles, videos, courses, or podcasts based on user engagement patterns and content similarity. We build recommendation feeds that increase time-on-platform, reduce churn, and surface long-tail content that users wouldn't discover otherwise. Marketplace Matching Match buyers with sellers, job seekers with employers, or patients with doctors using semantic similarity, preference matching, and availability filtering. Two-sided marketplace matching that optimizes for satisfaction on both sides.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
Semantic Search Implementation Replace keyword matching with meaning-based search using embedding models (Gemini, OpenAI, Cohere). Users find what they mean, not just what they type. We implement semantic search over product catalogues, knowledge bases, and content libraries with sub-100ms latency. Recommendation Engine Development Build recommendation systems using collaborative filtering (users who liked X also liked Y), content-based filtering (items similar to what you've viewed), and hybrid approaches. We handle cold-start problems, real-time personalization, and A/B testing frameworks to continuously improve recommendation quality. Hybrid Search (Vector + Keyword) Combine dense vector search with sparse keyword search (BM25) for the best of both worlds. Hybrid search captures semantic meaning while preserving exact-match precision for product SKUs, names, and technical terms. Search Analytics & Optimization Track search KPIs — click-through rate, zero-result rate, search-to-conversion, and query abandonment. We build feedback loops that use click and purchase data to improve ranking quality over time, and implement search analytics dashboards for your product team. Personalization Engine Build user models from browsing behavior, purchase history, and explicit preferences. Personalize search results, homepage content, email recommendations, and push notifications based on individual user profiles that update in real-time.
AI-Native Search Expertise We build search systems using the latest AI techniques — embedding models, re-rankers, learning-to-rank, and LLM-powered query understanding. Not just Elasticsearch with synonyms, but genuinely intelligent search that improves with usage. Measurable Impact Every search and recommendation implementation includes success metrics — CTR, conversion rate, NDCG, and revenue attribution. We set up A/B testing from day one so you can measure the impact of every improvement. Bengaluru Engineering Team Direct collaboration with search and ML engineers in Bengaluru. Fast iteration on ranking models, embedding strategies, and personalization logic with same-timezone coordination.
E-Commerce Product Search & Discovery Customers search for 'blue running shoes under 5000' and get exactly what they want — with semantic understanding of intent, price filtering, and personalized ranking based on their preferences and browsing history. Content Platform Recommendations Recommend articles, videos, courses, or podcasts based on user engagement patterns and content similarity. We build recommendation feeds that increase time-on-platform, reduce churn, and surface long-tail content that users wouldn't discover otherwise. Marketplace Matching Match buyers with sellers, job seekers with employers, or patients with doctors using semantic similarity, preference matching, and availability filtering. Two-sided marketplace matching that optimizes for satisfaction on both sides.
FAQ
Ready to upgrade your search and recommendations with AI? Talk to our Bengaluru team about your product.