Snowplow, the leader in customer data infrastructure, announced the launch of Snowplow Signals, a real-time customer intelligence system that enables companies to build and deploy AI-powered customer experiences much faster. Signals provides applications with access to deep, real-time, trustworthy customer context — making it easier to hyper-personalize user journeys and equip AI agents to overcome the “cold start problem” and drive more relevant interactions.

“Signals transforms one-off customer interactions into deeply personalized, proactive experiences.” — Todd Boes, Chief Product Officer, Snowplow

Long trusted by data teams at leading digital-first companies, Snowplow is now expanding its platform to support product, engineering, and data science teams building customer-facing AI-powered applications — such as personalization engines, adaptive UIs, and agentic applications like AI copilots and chatbots.

“By infusing real-time behavioral context into an application’s memory, Signals transforms one-off customer interactions into deeply personalized, proactive experiences that drive measurable lift in customer engagement, conversion, and lifetime value,” said Todd Boes, Chief Product Officer at Snowplow. “We’re proud to partner with leading brands as they harness Signals to deliver the next generation of customer-intelligent applications.”

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The Missing Link Between AI and Real-Time Customer Context

As organizations race to embed AI into their products, many hit a common set of roadblocks: they struggle to reliably identify who each user is in real-time, understand their current behavior, anticipate their needs, and serve deeply personalized experiences accordingly.

Existing data infrastructure often forces a trade-off — real-time speed without deep, trustworthy data, or deep, trustworthy data that is too slow to act on. Signals eliminates this compromise by providing extensible infrastructure for computing, retrieving, and acting on rich, well-governed customer data — both in-session and across historical context.

Snowplow Signals includes three core capabilities:

1. Profiles Store: Low-latency app and AI agent access to in-session and historical user attributes. The Profiles Store comes with two data processing engines to populate it:
   • Streaming Engine: Calculates real-time user attributes (e.g. in-session activity) from live event streams.
   • Batch Engine: Computes user attributes based on all of the historical customer data in your data warehouse or lakehouse (e.g. ML scores, lifecycle stage), including other customer data sets joined with behavioral data collected and processed by Snowplow.
2. Interventions: Engine for triggering real-time personalized actions based on real-time customer behavior (e.g. in-app nudges, dynamic offers or pricing, and proactive assistance from AI agents).
3. Fast-Start Tooling: Developer and data science tools to accelerate time-to-value, including Python and TypeScript SDKs for defining and retrieving user attributes and interventions, QuickStart guides, notebooks and code samples, and Solution Accelerators.

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Built for Digital Product and Engineering Teams

Snowplow Signals is designed for teams building AI-powered products that want to deliver different experiences to different customers through the use of personalization and recommendation models and AI agents to drive revenue growth.

What sets Snowplow Signals apart:

• Real-time personalization without compromise: Calculate and serve customer signals in-session with low latency — no trade-offs between speed and depth.
• Declarative customer intelligence: Easily define user attributes declaratively (e.g. in Git) and access them seamlessly in your apps via SDKs.
• Train once, deploy instantly: Use historical warehouse or lakehouse data to train ML features (i.e. customer attributes) and deploy them on live streaming data with no rework.
• Model-agnostic: Integrate any LLM or ML model into your application logic— no black-box vendor systems.
• Transparency and control: Run Signals in your cloud, with full governance, auditability, and data ownership.

“Snowplow Signals provides our product and engineering teams with the real-time customer intelligence infrastructure they need to build adaptive, AI-powered experiences into our FindMyPast product,” said Anup Purewal, Chief Data Officer at DC Thomson, a design partner for the release. “With Signals, we can advance beyond static searches and singular actions to offer a genealogy experience that truly reflects the hobby — guiding each user’s unique journey through our vast archives by proactively surfacing relevant content and suggesting next steps in real time. It’s a game-changer for hyper-personalizing each user’s deeply unique and personal experience.”

Deliver AI-Powered Experiences with Trusted Customer Data Infrastructure

Built on Snowplow’s industry-leading real-time data pipeline and streaming engine, Signals ensures high-quality, consistent data across stream and warehouse — and delivers millisecond lookups with governance built in.

The new product offering runs natively in Snowplow customers’ clouds and will support deployments on AWS, Azure, and GCP, with compatibility for Snowflake, Databricks, and BigQuery. Customers benefit from robust governance, built-in security, and full transparency across their end-to-end customer data operations.

Snowplow Signals marks a strategic expansion beyond data engineering to become foundational infrastructure for real-time, AI-driven digital experiences. As more companies move to productize AI, Signals positions Snowplow at the core of this transformation, unlocking new growth across product, engineering, and data science teams.