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Getting Started

Welcome! Follow the instructions below to learn how to capture data, get structured insights, and answer business questions with Sticklight. // Fancy platform screenshot here
1

Generate a Sticklight API key

  1. Login to the Sticklight platform.
  2. Navigate to API Keys settings and click “Generate New Key”. Give the key a descriptive name, and store it securely.
2

Use our SDKs to capture runtime data

Choose the SDK that matches your tech stack:
Install and use our Python SDK to capture events from Python applications. See the Python SDK guide.
Install and use our TypeScript SDK to capture events from JavaScript/TypeScript applications. See the TypeScript SDK guide.
3

Interact with your data in Views

Views are vantage points over your captured data. They are specialized tables with exploration tools for resolving key product questions.
  1. In the Sticklight Platform, navigate to Views
  2. Extract structured insights from your data by creating Properties
4

Create your first Property

Properties enrich your data for deeper understanding. Sticklight offers two types of Properties:
Calculated Properties process and aggregate any aspect of your data, including other Properties you have created.
  1. From your View, click “New Column” and select “Calculated”
  2. Name your property (e.g., “first_conversation_message”)
  3. Select the Source Column you wish to analyze (e.g., “message_content”)
  4. Select an Aggregation Function to apply to the Source Column (e.g., “First Value”)
  5. Optionally, define a filter to focus the calculation on a part of your data. In our example, we’ll filter to only include messages in conversations that have more than 4 messages.
  6. Click “Create” to save your Property
A new “first_conversation_message” column will be added to your View. It will be available for use throughout the platform.
Magic Properties leverage natural language and LLMs to create insightful new Properties.
  1. From your View, click “New Column” and select “Magic”
  2. Describe what you want to discover using natural language with column references: 
    What is the sentiment of @first_conversation_message in a scale of 1-5?
  3. Choose the output type (“Number”, in this case)
  4. Name your property (e.g., “first_message_sentiment”) and click “Save”
Magic Properties are powerful for extracting subjective, complex insights without having to think in SQL.
5

Create your first Chart

// The text below is LLM generatedCharts transform your View data into visual representations that reveal patterns and trends:
  1. Navigate to the Charts section and click “Create Chart”
  2. Select the View you want to visualize
  3. Choose a chart type (line, bar, etc.) based on what you want to analyze
  4. Configure the axes and series:
    • X-axis: Time or a categorical property
    • Y-axis: A numeric property or count
    • Series: Optional grouping by a categorical property
  5. Add filters to focus on specific data segments if needed
  6. Name your chart and click “Create”
For example, you might create a line chart showing “Average Response Sentiment” over time, grouped by “Model Type” to compare different LLMs.Charts help you identify trends that might not be obvious from tabular data and make it easier to communicate insights with teammates.