AI Assisted No Code Transformations is now available in Public Beta. This enhancement lets business users describe data clean-up or enrichment needs in plain English, with the system automatically generating the data transformation logic. It reduces complexity, speeds up experimentation, and ensures cleaner, better-structured data for improved search and discovery experiences. Click here to learn more.

Collection limits have been significantly increased to support larger-scale operations. Premium plans now include 500 collections per index, Elevate plans support 1,000 collections per index, with each collection supporting up to 10,000 manual records and 50 conditions. This reduces trade-offs between campaigns and enables more targeted seasonal collections for improved user engagement.

Shopify merchants, now benefit from improved indexing performance with a new engine designed for scalability and reliability. Indexing is more consistent during high-traffic periods, ensuring fresher product data is available sooner. The update also unlocks access to data transformations and fetch for greater customization, along with a new Connection Debugger that provides visibility into indexing operations and helps merchants troubleshoot issues directly. Now automatically available to all merchants not leveraging Shopify's Markets feature.

Interpreting A/B experiment results can often be complex, especially when assessing the significance of observed differences. To provide greater clarity and statistical context, confidence intervals have been introduced. These intervals help indicate the range within which the true effect is likely to fall, making it easier to evaluate the reliability of experiment outcomes. Confidence intervals are visualized on a time series chart, positioned directly below the standard A/B test results.

Algolia's Visual Editor now supports multi-item drag and drop, making bulk pinning changes more convenient and faster. Simply select which items to move and drag them to the desired position before or after other items in your results grid. 

Collections no longer require calling the Push Task to stay up to date. You can now apply transformations using supported API clients (Go, Java, JavaScript, PHP, Python), which handle ingestion automatically. This reduces setup friction and helps business users get started faster. Click here to learn more.

Business users can now generate product collections using natural language prompts like “vintage dresses with a floral print.” Powered by NeuralSearch, this enhancement automatically curates relevant items based on semantic relevance. It reduces manual work, speeds up collection creation, and helps teams respond quickly to seasonal trends and merchandising needs. Click here to learn more. 

We've enhanced Smart Group workflows in Merchandising Studio’s Visual Editor, enabling streamlined curation across Search, Categories, and Collections. Combine index-level optimization with strategic Smart Group placement in your Compositions - all in connected, intuitive flows and through one visual interface. What's more, you can easily curate individual Groups and gain new explainability insights - for example, into the ranking factors behind them.

A/B testing now supports multi-variant experiments, which saves time and helps customers find their optimal settings, faster. Customers can now test up to five variants (one control and four variants). In addition, A/B testing is introducing an improved UX to help better communicate A/B testing results and make way for the exciting developments we have planned. Customers now start on an overview screen that lists their recent experiments. Clicking into a test gives a full page of results with a detailed breakdown of metrics.

Create, edit, and apply data transformations directly in the Algolia DataHub - no coding required. Easily add or remove fields, filter records, or compute new values using a guided form. This update reduces setup time, improves data quality, and makes it easier to optimize search experiences without technical support. Click here to learn more.