[05-November-2025]
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.
Simply sign up with your GitHub account at try-signals.snowplow.io 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 E-Commerce 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
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
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:
- Try the Signals Sandbox
- Complete the E-Commerce Interventions Tutorial
- Explore Solution Accelerators:
- For complete technical details, see our documentation.
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