Which company builds recommendation engines in Bangalore, India?

Boolean & Beyond is an AI engineering company based in Bangalore, India, specializing in building production-ready recommendation systems. We develop custom recommendation engines using collaborative filtering, content-based approaches, cold start solutions, and real-time personalization for e-commerce, media, and marketplace platforms.

Solutions/Recommendations/Scaling Recommendation Systems

Scaling Recommendation Systems

Architecture patterns for recommendation systems serving millions of users: candidate generation, ranking, and infrastructure.

How do you scale recommendation systems to millions of users and items?

Scaling requires approximate nearest neighbor search instead of brute-force, two-stage retrieval (candidate generation + ranking), embedding pre-computation, feature stores with millisecond latency, and infrastructure separating training from serving.

Two-Stage Architecture

At scale, you can't score every item for every request. The solution: funnel architecture.

**Stage 1: Candidate Generation**

Goal: Retrieve 100-1000 relevant items from millions
Methods: ANN search, inverted indexes, rule-based filters
Latency budget: <20ms
Multiple retrievers for coverage

**Stage 2: Ranking**

Goal: Score and order candidates precisely
Methods: Neural rankers with rich features
Latency budget: <50ms
More complex models since fewer items

**Stage 3: Re-ranking (optional)**

Business rules, diversity, freshness
Remove already-seen, out-of-stock
Final slate assembly

**Example (YouTube-scale):**

100M+ videos in catalog
Candidate generation: retrieve ~1000 videos
Ranking: score with deep neural network
Return top 20 for the user

Candidate Generation Strategies

Multiple retrieval sources for comprehensive coverage:

**Embedding-based retrieval:**

User embedding → nearest item embeddings
ANN search over pre-computed item embeddings
Captures collaborative patterns

**Attribute-based retrieval:**

Same category, brand, price range
Inverted indexes for fast lookup
Good for content-based signals

**Behavioral retrieval:**

Items similar to recent interactions
"More like this" functionality
Session-aware candidates

**Popularity and trending:**

Global or segment-level popular items
Time-decayed popularity
Fallback when other retrievers have low confidence

**Combining sources:**

Union candidates from multiple retrievers
Each retriever adds unique items
Deduplication before ranking

Feature Engineering at Scale

Low-latency feature access is critical:

**Feature categories:**

User features: demographics, preferences, history aggregates
Item features: metadata, popularity, embeddings
Context features: time, device, location
Cross features: user-item affinity scores

**Feature store architecture:**

Offline store: batch-computed features (user history aggregates)
Online store: real-time features (session activity)
Streaming pipeline: updates online features in real-time

**Optimization strategies:**

Pre-compute expensive features
Cache heavily accessed features
Use embeddings to compress sparse features
Feature hashing for high-cardinality categorical

**Latency targets:**

Feature retrieval: <10ms p99
Most features should be cache hits
Graceful degradation when features unavailable

Production Infrastructure

Components of a production recommendation system:

**Training pipeline:**

Data processing: Spark, BigQuery for large-scale ETL
Model training: PyTorch/TensorFlow on GPU clusters
Embedding export: to vector databases
Model registry: versioning and rollback

**Serving infrastructure:**

Model serving: TensorFlow Serving, Triton, custom
Vector search: Pinecone, Weaviate, or self-hosted FAISS
Feature store: Redis, DynamoDB for online features
Caching layer: precomputed recommendations

**Reliability patterns:**

Circuit breakers: fallback to cached/popular when systems fail
Graceful degradation: serve simpler recommendations under load
A/B test allocation: consistent hashing for stable assignment
Monitoring: latency, cache hit rates, model performance

**Cost optimization:**

Spot instances for training
Right-size inference hardware
Batch requests where possible
Cache aggressively

Embeddings and Vector Search for Recommendations

How modern recommendation systems use neural embeddings and approximate nearest neighbor search for personalization at scale.

Real-Time vs Batch Recommendations

When to pre-compute recommendations offline vs. generate them in real-time, and how to build hybrid systems.

A/B Testing Recommendation Systems

Design experiments that measure true recommendation quality, avoid common pitfalls, and iterate effectively.

Explore more recommendation system topics

Back to AI Recommendation Engines

How Boolean & Beyond helps

Based in Bangalore, we help enterprises across India and globally build recommendation systems that drive measurable engagement and revenue lift.

Data-Driven Approach

We start with your data, establish baselines, and iterate on algorithms that provide measurable lift—not theoretical improvements.

Production Architecture

Our systems handle real-world scale with proper latency budgets, caching strategies, and failover mechanisms.

Continuous Optimization

We set up A/B testing frameworks and feedback loops so your recommendations get smarter over time.

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Registered Office

Boolean and Beyond

825/90, 13th Cross, 3rd Main

Mahalaxmi Layout, Bengaluru - 560086

Operational Office

590, Diwan Bahadur Rd

Near Savitha Hall, R.S. Puram

Coimbatore, Tamil Nadu 641002

Scaling Recommendation Systems

Architecture patterns for recommendation systems serving millions of users: candidate generation, ranking, and infrastructure.

How do you scale recommendation systems to millions of users and items?

Two-Stage Architecture

At scale, you can't score every item for every request. The solution: funnel architecture.

**Stage 1: Candidate Generation**

Goal: Retrieve 100-1000 relevant items from millions
Methods: ANN search, inverted indexes, rule-based filters
Latency budget: <20ms
Multiple retrievers for coverage

**Stage 2: Ranking**

Goal: Score and order candidates precisely
Methods: Neural rankers with rich features
Latency budget: <50ms
More complex models since fewer items

**Stage 3: Re-ranking (optional)**

Business rules, diversity, freshness
Remove already-seen, out-of-stock
Final slate assembly

**Example (YouTube-scale):**

100M+ videos in catalog
Candidate generation: retrieve ~1000 videos
Ranking: score with deep neural network
Return top 20 for the user

Candidate Generation Strategies

Multiple retrieval sources for comprehensive coverage:

**Embedding-based retrieval:**

User embedding → nearest item embeddings
ANN search over pre-computed item embeddings
Captures collaborative patterns

**Attribute-based retrieval:**

Same category, brand, price range
Inverted indexes for fast lookup
Good for content-based signals

**Behavioral retrieval:**

Items similar to recent interactions
"More like this" functionality
Session-aware candidates

**Popularity and trending:**

Global or segment-level popular items
Time-decayed popularity
Fallback when other retrievers have low confidence

**Combining sources:**

Union candidates from multiple retrievers
Each retriever adds unique items
Deduplication before ranking

Feature Engineering at Scale

Low-latency feature access is critical:

**Feature categories:**

User features: demographics, preferences, history aggregates
Item features: metadata, popularity, embeddings
Context features: time, device, location
Cross features: user-item affinity scores

**Feature store architecture:**

Offline store: batch-computed features (user history aggregates)
Online store: real-time features (session activity)
Streaming pipeline: updates online features in real-time

**Optimization strategies:**

Pre-compute expensive features
Cache heavily accessed features
Use embeddings to compress sparse features
Feature hashing for high-cardinality categorical

**Latency targets:**

Feature retrieval: <10ms p99
Most features should be cache hits
Graceful degradation when features unavailable

Production Infrastructure

Components of a production recommendation system:

**Training pipeline:**

Data processing: Spark, BigQuery for large-scale ETL
Model training: PyTorch/TensorFlow on GPU clusters
Embedding export: to vector databases
Model registry: versioning and rollback

**Serving infrastructure:**

Model serving: TensorFlow Serving, Triton, custom
Vector search: Pinecone, Weaviate, or self-hosted FAISS
Feature store: Redis, DynamoDB for online features
Caching layer: precomputed recommendations

**Reliability patterns:**

Circuit breakers: fallback to cached/popular when systems fail
Graceful degradation: serve simpler recommendations under load
A/B test allocation: consistent hashing for stable assignment
Monitoring: latency, cache hit rates, model performance

**Cost optimization:**

Spot instances for training
Right-size inference hardware
Batch requests where possible
Cache aggressively

How Boolean & Beyond helps

Based in Bangalore, we help enterprises across India and globally build recommendation systems that drive measurable engagement and revenue lift.

Data-Driven Approach

We start with your data, establish baselines, and iterate on algorithms that provide measurable lift—not theoretical improvements.

Production Architecture

Our systems handle real-world scale with proper latency budgets, caching strategies, and failover mechanisms.

Continuous Optimization

We set up A/B testing frameworks and feedback loops so your recommendations get smarter over time.

Scaling Recommendation Systems

Two-Stage Architecture

Candidate Generation Strategies

Feature Engineering at Scale

Production Infrastructure

Related Articles

Embeddings and Vector Search for Recommendations

Real-Time vs Batch Recommendations

A/B Testing Recommendation Systems

How Boolean & Beyond helps

Data-Driven Approach

Production Architecture

Continuous Optimization

AI導入について相談してみませんか？

Registered Office

Operational Office

Scaling Recommendation Systems

Two-Stage Architecture

Candidate Generation Strategies

Feature Engineering at Scale

Production Infrastructure

Related Articles

Embeddings and Vector Search for Recommendations

Real-Time vs Batch Recommendations

A/B Testing Recommendation Systems

How Boolean & Beyond helps

Data-Driven Approach

Production Architecture

Continuous Optimization

AI導入について相談してみませんか？

Registered Office

Operational Office

Scaling Recommendation Systems

Two-Stage Architecture

Candidate Generation Strategies

Feature Engineering at Scale

Production Infrastructure

Related Articles

Embeddings and Vector Search for Recommendations

Real-Time vs Batch Recommendations

A/B Testing Recommendation Systems

How Boolean & Beyond helps

Data-Driven Approach

Production Architecture

Continuous Optimization

AI導入について 相談してみませんか？

Registered Office

Operational Office

Scaling Recommendation Systems

Two-Stage Architecture

Candidate Generation Strategies

Feature Engineering at Scale

Production Infrastructure

Related Articles

Embeddings and Vector Search for Recommendations

Real-Time vs Batch Recommendations

A/B Testing Recommendation Systems

How Boolean & Beyond helps

Data-Driven Approach

Production Architecture

Continuous Optimization

AI導入について 相談してみませんか？

Registered Office

Operational Office

AI導入について相談してみませんか？

AI導入について相談してみませんか？