Recs Studio

Recommendations. Managed.

From data to personalised offer. At any scale.

Recs Studio gives you state-of-the-art recommender systems without the infrastructure overhead. You own the business domain — we handle the platform, education, and consulting.

Benefits

Personalised recommendations across any channel Deliver tailored product and content recommendations across your website, mobile app, email campaigns, CRM, and marketing channels — all from a single platform.

Recs Studio trains models on your unique customer and product data so every touchpoint receives relevant, personalised offers. Whether you serve recommendations on a product page, in a newsletter, or through a sales team dashboard — the same engine powers them all.

Recs Studio manages the entire infrastructure — data ingestion, model training, and serving endpoints — so you can go from connecting your first data source to receiving live predictions without provisioning servers, configuring pipelines, or managing ML operations. Bring your data, configure your first model through the UI, and deploy.

Top-tier accuracy Achieve best-in-class recommendation accuracy with the same model architectures used by the world's leading technology companies.

Recs Studio uses TensorFlow Recommenders with two-tower neural networks — retrieval, ranking, and multitask models — combined with advanced feature engineering and flexible experimentation. Run hundreds of experiments, compare results side by side, and continuously retrain to keep accuracy at its peak as your data evolves.

Recommendations in milliseconds Serve personalised recommendations to your customers in real time with millisecond-level latency at any scale.

Recs Studio deploys trained models to production-grade serving endpoints that auto-scale from zero to millions of requests. Whether you have hundreds or millions of customers, each request returns personalised results fast enough for real-time website rendering, in-app experiences, and API-driven integrations.

Recommendation Types Three model types — retrieval, ranking, and multitask — covering every recommendation scenario from candidate discovery to precise scoring.

Retrieval models quickly scan your entire product catalogue to find the most relevant candidates for each customer — powering scenarios like "next best offer" and "customers who bought this also bought". Ranking models take those candidates and score them by predicted purchase probability, enabling personalised product ordering, promotion targeting, and dynamic pricing displays. Multitask models combine both objectives in a single architecture, simultaneously optimising for relevance and predicted revenue — ideal for complex scenarios where you need to balance discovery with conversion, such as ranking promotional products by individual likelihood to buy.

Data Your data stays where it is — we connect to it. Relational databases, data warehouses, NoSQL, cloud storage, in-memory stores, and big data pipelines — all supported out of the box.

Recs Studio integrates with the full spectrum of modern data technologies. Connect PostgreSQL, MySQL, Oracle, SQL Server, or any relational database. Pull from BigQuery, Snowflake, or Redshift as your data warehouse. Tap into Firestore, MongoDB, DynamoDB, or Redis for real-time and transactional data. Ingest files from GCS, S3, or Azure Blob Storage. For large-scale processing, Apache Beam on Dataflow handles millions of rows automatically. All data flows happen in the background — no infrastructure setup, no pipeline management, no IT involvement required.

Feature Engineering & Model Design Advanced feature processing — embeddings, cross features, cyclical encoding, recency signals — paired with deep two-tower neural network architectures for retrieval, ranking, and multitask objectives.

Every recommendation is shaped by context. TFRS goes beyond traditional collaborative filtering by freely incorporating user, item, and context information into the model. Recs Studio supports the full range of modern feature engineering: taste vectors and purchase history as dense embeddings, product attributes like category, brand, and price tier, temporal signals with cyclical encoding for seasonality and day-of-week patterns, and cross features that capture interactions between any combination of signals. Customer segments, demographics, geography, sales channel — any signal in your data becomes part of the context that drives personalisation.

Models are built on the TFRS two-tower architecture — a Query tower representing the customer and a Candidate tower representing the product. Each tower is a deep neural network with fully configurable architecture — layer types, depth, width, activation functions, and regularisation all adapt to match the complexity of your data. ScaNN approximate nearest-neighbour indexing handles catalogues of millions of products, making real-time retrieval practical at any scale.

Experiments A closed-loop optimization environment — run experiments, understand what works and why, and systematically push accuracy higher with every cycle.

Every experiment you run makes the system smarter. It doesn't just store results — it analyses them across your full history using TPE-based hyperparameter analysis to find what actually matters. Which model configurations, feature combinations, datasets, and training parameters separate your best runs from the rest. The experiments dashboard tracks metrics trends, ranks top configurations, and visualises training dynamics — independently for each model type: retrieval, ranking, and multitask.

Training Full-scale Vertex AI pipelines with multi-GPU training, model registry with native versioning, and automated retraining — no infrastructure to manage.

Promote your best experiment to a full training run — the platform handles everything: compiles TFX pipelines, provisions GPUs, trains on Vertex AI, evaluates quality, and registers the model automatically. Every trained model is versioned in Vertex AI Model Registry with full lineage back to its source configuration. Set up automated retraining — hourly to monthly — and the system keeps your models current as your data evolves.

Inference Deploy with one click, scale from zero to any load, serve recommendations in milliseconds — with full performance monitoring out of the box.

Models ship with all preprocessing logic built in — vocabulary lookups, embedding tables, normalization, feature crosses — so clients send raw data and get recommendations back. Without this, every retraining cycle means updating and redeploying vocabulary mappings and embedding tables across your serving infrastructure — a complex, error-prone process that most teams get wrong. No preprocessing code to write, no feature pipelines to maintain, no training-serving skew.

Trained models deploy to production endpoints with a single click, and as automated retraining produces new versions, they deploy to existing endpoints automatically — no manual intervention required. Endpoints scale from zero when idle to any load on demand, keeping costs at zero between requests while handling traffic spikes automatically. A real-time serving dashboard monitors every endpoint: request volumes, latency, error rates, container instances, and resource utilisation — so you always know how your models perform in production.

Infrastructure & Security Complete infrastructure isolation per client — dedicated resources across the entire stack, from data storage to model serving. Scale without limits, deploy in any region, pay only for what you use.

Your data never touches another client's infrastructure. Every client gets a fully isolated environment — dedicated databases, data warehouses, ML pipelines, and serving endpoints. No shared compute, no shared storage, no resource contention. The platform supports hybrid installations — cloud, on-premise, or a combination — so your data stays exactly where your security policies require. There are no artificial limits on data volumes, processing capacity, GPUs, or inference throughput, and a real-time billing dashboard gives you full visibility into costs.

Education, Support & Consulting Hands-on onboarding, expert guidance on model strategy, and dedicated support from the team that built the platform — included with every engagement, not sold as an add-on.

Every pilot starts with hands-on onboarding — we train your team on the platform, help configure your first data pipelines and models, and work alongside you until you're fully autonomous. Beyond onboarding, we provide ongoing consulting on recommendation strategy: model type selection, feature engineering, data quality, and accuracy optimisation. Support comes directly from the engineering team that builds the platform — no ticket queues, no outsourced help desks, no layers between you and the people who can actually fix things.

Recommendations. Managed.

Benefits

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