AI & Machine Learning

Recommendation Systems — Personalization at Scale

Recommendation engines are proven revenue multipliers — Amazon attributes 35% of revenue to them. We build production recommendation systems that personalize every user interaction at any scale.

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Capabilities

Recommendation Engines — deep expertise

Collaborative Filtering

User-to-user and item-to-item collaborative filtering surfacing items based on the behavior of similar users.

Matrix FactorizationNeural CFALS

Content-Based Filtering

Attribute-based recommendation using product features, NLP embeddings, and user preference profiles — effective for cold-start and long-tail catalog scenarios.

TF-IDFSentence EmbeddingsFeature Engineering

Deep Learning (DLRM)

Deep learning recommendation models combining collaborative and content signals — achieving state-of-the-art accuracy for large-scale catalogs.

DLRMTwo-TowerYouTube DNNTransformers

Real-Time Serving

Sub-millisecond recommendation serving using feature stores and vector databases — delivering personalization at the speed users expect.

FeastRedisPineconeQdrant

Cross-Sell & Upsell Models

Propensity models identifying the optimal next-best-offer for each customer — maximizing revenue per interaction across e-commerce, banking, and retail.

Propensity ScoringUplift ModelsBandit Algorithms

A/B Testing Framework

Experimentation infrastructure for recommendation algorithms — statistically rigorous tests that continuously improve recommendation quality.

A/B TestingBanditsCausal Inference

Recommendation Results

Recommendation Engines Driving Revenue

10B+

Recommendations served/day

22%

Avg revenue uplift

100ms

P99 recommendation latency

40+

Recommendation systems deployed

Our Approach

From Generic Content to Personalized Experiences

Use Case Scoping

Define recommendation contexts — product, content, or next-best-action — and catalog available interaction data.

Algorithm Design

Select the recommendation approach — collaborative filtering, content-based, or hybrid — and design the pipeline.

Build & A/B Test

Train the recommendation model, integrate into your platform, and run A/B tests to measure lift.

Scale & Optimize

Tune for latency and throughput, add diversity controls, and implement feedback loops for continuous improvement.