About Recs Studio

Recs Studio helps mid-to-large retailers and B2B marketplaces deliver state-of-the-art personalised recommendations without building an internal ML platform from scratch. We combine production-grade recommender systems, modern data engineering, and Google Cloud infrastructure into a managed service that our customers can deploy in hours.

Our Mission

Every retailer sits on rich customer and product data, but most never turn that data into real-time, personalised experiences because the path from raw data to a production recommender is long, expensive, and error-prone. Our mission is to shorten that path to a single managed platform: connect your data, configure your model, deploy your endpoints, and continuously improve accuracy.

What We Built

Recs Studio is a complete recommendation platform built on TensorFlow Recommenders (TFRS), TFX production pipelines, and Vertex AI. It covers the full lifecycle:

• Data ingestion: ETL from SQL, NoSQL, data warehouses, and cloud storage into BigQuery.
• Feature engineering: Visual schema builder, two-tower feature assignment, embeddings, normalisation, cyclical encoding, and cross features.
• Model architecture: Configurable retrieval, ranking, and multitask models with dense, dropout, batch-normalisation, and regularisation layers.
• Experiments: Quick-test Vertex AI pipelines to validate configurations before full training.
• Training: GPU-powered production training, model registry, automated evaluation, and scheduled retraining.
• Serving: Auto-scaling Cloud Run endpoints with TF Serving and ScaNN, real-time monitoring, and client SDK examples.

Founding Team

Recs Studio was founded by a team of engineers with hands-on production experience in large-scale recommender systems, cloud ML infrastructure, and retail data platforms.

Customer Traction

Recs Studio is already in production with two retail customers in Ukraine. Both systems were delivered as tailored implementations, are under active support, and are on a migration path to the managed Google Cloud Marketplace SaaS model.

• Retail — Europe: live recommendation system deployed across digital commerce channels.
• Food delivery — Eastern Europe: personalised recommendations for an online grocery marketplace.

We are also in discussion with additional EU and US retailers for pilots through the Google Cloud Marketplace.

Get in Touch

For partnerships, press, or general inquiries, email us at support@recs.studio. To see the platform in action, click the Request a Demo button.