Snowplow, a leading name in customer data infrastructure, has unveiled Snowplow Signals — a real-time customer intelligence system designed to accelerate AI-powered digital experiences. With Signals, organizations can overcome the common challenge of connecting AI models to meaningful, real-time customer context. This launch marks a strategic leap for Snowplow, expanding its value beyond data teams to include product, engineering, and AI application developers.

Why Signals? Addressing the AI Context Gap

• The Challenge:
  Companies embedding AI into products often lack:

  • Real-time identity resolution

  • Current behavior tracking

  • Predictive personalization capabilities

• The Gap:

  • Traditional data systems force trade-offs:

    • Real-time speed vs deep trustworthy data

  • Snowplow Signals bridges this by combining low-latency and deep historical context into one seamless infrastructure.

Core Capabilities of Snowplow Signals

1. Profiles Store

   • Delivers app and AI agent access to real-time and historical user attributes.

   • Powered by two processing engines:

     • Streaming Engine: Real-time behavioral data (e.g. clicks, views)

     • Batch Engine: Historical data (e.g. ML scores, customer lifecycle)

2. Interventions Engine

   • Triggers actions like:

     • In-app nudges

     • Dynamic pricing or offers

     • Proactive AI agent assistance

   • Based on live behavioral inputs

3. Fast-Start Tooling

   • Developer tools and SDKs in Python and TypeScript

   • QuickStart guides, notebooks, and accelerators to reduce time-to-value

Built for AI-Driven Product Development

• Target Users:
  Product, engineering, and data science teams building:

  • Personalization engines

  • Adaptive user interfaces

  • AI copilots and chatbots

• Strategic Edge:

  • Hyper-personalized experiences based on both immediate behavior and long-term data

  • Empower AI agents to avoid cold starts and deliver relevance instantly

What Sets Signals Apart

• Real-Time Personalization Without Compromise

  • No trade-off between speed and data depth

• Declarative Intelligence Architecture

  • Define user attributes using Git; deploy via SDKs

• Seamless Training-to-Deployment Flow

  • Train on warehouse data and deploy to live streams without reengineering

• Model-Agnostic Framework

  • Plug in any ML or LLM model—no lock-in or vendor limitations

• Full Transparency and Governance

  • Deployed in customer clouds (AWS, Azure, GCP)

  • Compatible with Snowflake, BigQuery, and Databricks

  • Ensures data security, governance, and ownership

Real-World Impact: Case Example

“Snowplow Signals transforms FindMyPast into a dynamic genealogy journey," says Anup Purewal, Chief Data Officer at DC Thomson.
Using Signals, they now:

• Guide users through archives with real-time suggestions

• Provide proactive, adaptive UI experiences

• Elevate engagement by personalizing every touchpoint

Snowplow Signals signifies a pivotal evolution in the customer data space — delivering a real-time, governed, and extensible platform that bridges AI logic with live user context. For companies aiming to productize AI with reliability and precision, Signals serves as foundational infrastructure. Whether it's enhancing personalization, creating adaptive applications, or empowering AI copilots, Snowplow Signals makes intelligent, responsive digital experiences scalable and achievable.

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