Product Recommenders & Personalized Recommendations

Deliver personalized product recommendations with scalable models, low-latency APIs and continuous A/B testing to improve conversion and retention.

Models
Deployed

Active
Clients

Years in
Recommendations

Services

Product Recommender Services to Increase Engagement

Design, build and deploy personalized recommendation systems — from feature engineering and models to low-latency APIs and continuous experimentation.

Recommender System
Design

Define objectives, signals and evaluation metrics, and design model architectures tailored to your product and users.

Real-time Recommendations & APIs

Serve low-latency personalized suggestions via scalable APIs and streaming scoring for live user experiences.

Personalization Engine

Develop feature stores, embeddings, and personalization layers to customize user experiences consistently across diverse touchpoints.

A/B Testing & Experimentation

Run controlled experiments to validate recommendation strategies and optimize for real business metrics.

Hybrid Algorithms & Modeling

Combine collaborative filtering, content-based and context-aware models to improve accuracy and coverage.

MLOps for Recommenders

Deploy, monitor and retrain models with pipelines, feature freshness checks and scalable serving infrastructure.

Features

Product Recommender Features

Capabilities for building production-grade recommenders: feature store, models, real-time scoring, experimentation, explainability and UI embedding.

Feature Store &
Data Pipelines

Centralize user and item features, ensure freshness and reproducibility for model training and serving.

Collaborative Filtering & Matrix Factorization

Personalize at scale using user-item interactions and embedding-based recommendation models.

Content-based Models & NLP

Enrich recommendations with item metadata and natural language signals for cold-start and relevance.

Real-time Scoring &
Low Latency

Serve recommendations in milliseconds with caching, streaming feature updates and edge-ready APIs.

A/B Testing & Experimentation

Measure impact with experiments, tune ranking policies and optimize for long-term metrics like retention.

Explainability &
Fairness

Provide interpretable recommendations, fairness checks and controls to meet policy and regulatory needs.

Scalable Machine Learning Operations

Automate training, validation and deployment pipelines to keep models fresh and performant at scale.

Recommendations UI & Embedding

Integrate recommendation widgets, personalized sections and embedded analytics into your product UI.

Product Recommenders & Personalized Recommendations

Product Recommender Services to Increase Engagement

Recommender System
Design

Real-time Recommendations & APIs

Personalization Engine

A/B Testing & Experimentation

Hybrid Algorithms & Modeling

MLOps for Recommenders

Product Recommender Features

Feature Store &
Data Pipelines

Collaborative Filtering & Matrix Factorization

Content-based Models & NLP

Real-time Scoring &
Low Latency

A/B Testing & Experimentation

Explainability &
Fairness

Scalable Machine Learning Operations

Recommendations UI & Embedding

What recommender and personalization services do you provide?

What data sources do you use for recommendations?

How do you evaluate and validate recommendation quality?

Can you integrate recommenders with our product and backend services?

What pricing and engagement models do you offer?

Do You Have Project in Mind?

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Address

Contact

Product Recommenders & Personalized Recommendations

Product Recommender Services to Increase Engagement

Recommender System Design

Real-time Recommendations & APIs

Personalization Engine

A/B Testing & Experimentation

Hybrid Algorithms & Modeling

MLOps for Recommenders

Product Recommender Features

Feature Store & Data Pipelines

Collaborative Filtering & Matrix Factorization

Content-based Models & NLP

Real-time Scoring & Low Latency

A/B Testing & Experimentation

Explainability & Fairness

Scalable Machine Learning Operations

Recommendations UI & Embedding

What recommender and personalization services do you provide?

What data sources do you use for recommendations?

How do you evaluate and validate recommendation quality?

Can you integrate recommenders with our product and backend services?

What pricing and engagement models do you offer?

Do You Have Project in Mind?

Contact

Recommender System
Design

Feature Store &
Data Pipelines

Real-time Scoring &
Low Latency

Explainability &
Fairness