Sift Lab is an AI-native platform - an agentic customer intelligence layer specifically designed to unlock insights and activation from all your first-party data. We did not just set out to build another analytics tool; we built a AI-native platform that combines a highly efficient database design, cutting-edge machine learning models in cooperation with world-leading researchers together with the latest breakthroughs in language models.
At the core is a proprietary distributed columnar database built on DuckDB, optimized for extreme data density and proprietary snapshot optimization. This enables, complex queries in near real time at a fraction of the cost of traditional cloud warehouse–centric setups.
The platform sits directly on top of your existing data infrastructure (e.g. Snowflake, BigQuery, Databricks) and unifies customer, product, and channel data into a single semantic layer. Our dynamic ingestion and snapshot architecture lets new data stream in continuously and become queryable in milliseconds, ensuring that predictions, segments, and dashboards are always built on fresh data.
On top of this data core, an agentic layer (Sift Sense) can reason across your full data model, answer complex ad hoc questions, and automate the entire flow from insight to activation. The platform applies advanced time-series modeling and a flexible recommendation engine (transformer/LLM-based, multimodal embeddings, GPU-accelerated) to predict churn, next-best-action, CLV, and product preferences with high precision. This powers predictive audiences, optimized recommendations, and automated journeys (e.g. churn, second purchase, replenishment) that continuously adapt as behavior changes.
Sift Lab ships with a large library of out-of-the-box use cases across CRM, ecommerce, retail media, and merchandising—while everything remains fully customizable through our semantic layer and SQL.
Users can build their own dashboards, reports, segments, and optimization logic in an intuitive UI, while the engine scales horizontally for thousands of concurrent users and embedded workloads.