# Introducing Signals Sandbox: start experimenting with new solution accelerators
> The Snowplow Signals Sandbox is available today to all users.
> Source: https://docs.snowplow.io/release-notes/introducing-signals-sandbox-start-experimenting-with-new-solution-accelerators/
> Documentation index: https://docs.snowplow.io/llms.txt
> Date: 2025-11-05
> Components: Signals

> **Note: The Signals Sandbox has been retired**
>
> The Sandbox described below is no longer available. To try Signals, start on the [Signals free tier](https://signals.snowplow.io/start?utm_source=docs.snowplow.io\&utm_medium=referral).

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](https://snowplow.io/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](/tutorials/signals-interventions/start/). 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 →](/tutorials/signals-personalize-travel/intro/)

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:

```text
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**

[View accelerator →](/tutorials/signals-ml-prospect-scoring/intro/)

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:

```text
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.[](https://example.com)

## **Start building today:**

1. Start on the [Signals free tier](https://signals.snowplow.io/start?utm_source=docs.snowplow.io\&utm_medium=referral)

2. Complete the [ecommerce interventions tutorial](/tutorials/signals-interventions/start/)

3. Explore Solution Accelerators:

   - [Real-Time Personalization for Travel](/tutorials/signals-personalize-travel/intro/)
   - [ML-Based Prospect Scoring](/tutorials/signals-ml-prospect-scoring/intro/)

4. For complete technical details, see our [documentation](/docs/signals/connection/).

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