Introducing Signals Sandbox: start experimenting with new solution accelerators

The Signals Sandbox has been retired

The Sandbox described below is no longer available. To try Signals, start on the Signals free tier.

The Snowplow Signals Sandbox is available today to all users.

This free, ready-to-use environment allows developers to experiment with real-time customer intelligence without setting up any infrastructure.

We’re also releasing two Solution Accelerators that make it easy to explore what’s possible with Snowplow Signals. Each accelerator provides open-source reference code, schema definitions, and a clear architectural pattern for production-grade, agentic applications.

Try Snowplow Signals in Minutes

The Signals Sandbox provides a ready-to-use environment for testing how behavioral data becomes real-time intelligence. No Kafka setup, Flink configuration, or Redis deployment is required.

Sign up with your GitHub account and you get:

• A complete Signals environment with streaming data from a live e-commerce demo
• Real-time feature computation and profile updates you can watch happen
• The ability to write and test Interventions using Python scripts
• Direct integration with OpenAI or AWS Bedrock to experiment with AI agents

New users should start with our ecommerce interventions tutorial. This walks you through the fundamentals of tracking events, computing features, updating user profiles, and defining rule-based interventions.

From there, you can explore the more advanced use cases in our Solution Accelerators.

Solution Accelerators: Design Patterns for Intelligent Apps

1.Real-Time Personalization for Digital Travel Bookings

View accelerator →

This accelerator is a travel platform simulation. It shows how AI agents can query the Profiles API for instant context.

The architecture demonstrates how Signals computes features like recent_destination_views and luxury_interest_score in real time, then feeds them into an OpenAI-powered agent that personalizes responses on the fly:

profile = profiles_api.get(user_id)
prompt = f"""
The user has viewed {profile["recent_destination_views"]} luxury destinations
and has a loyalty_tier of {profile["loyalty_tier"]}.
Suggest an upgrade or high-value recommendation.
"""
ai_response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}]
)

When the agent responds, Signals executes a rules-based Intervention, such as displaying a premium offer or opening a chat prompt, completing the feedback loop.

2. ML-Based Prospect Scoring

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This accelerator shows you how to integrate machine learning models into the Signals workflow for real-time lead scoring.

A predictive model trained on historical data outputs a base_prospect_score. Signals then merges this with streaming features like feature_usage_10min to calculate a live_prospect_score that updates as behavior changes.

When the score crosses a threshold, an Intervention fires automatically:

if (liveProspectScore > 0.8) {
  interventions.trigger({
    id: "notify_sales_team",
    payload: { userId, liveProspectScore },
  });
}

This eliminates the need for a separate feature store or heavy orchestration.

Start building today:

1. Start on the Signals free tier

2. Complete the ecommerce interventions tutorial

3. Explore Solution Accelerators:

   • Real-Time Personalization for Travel
   • ML-Based Prospect Scoring

4. For complete technical details, see our documentation.

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