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Your First Discovery

Time: 15 minutes Prerequisites: DecisionBox running (Quick Start), a data warehouse with data, an LLM API key

This guide walks through creating a project, configuring it, running a discovery, and understanding the results.

Step 1: Create a Project​

Open http://localhost:3000 and click New Project.

Basics​

  • Name: A descriptive name (e.g., "Puzzle Quest Analytics")
  • Domain: Select your industry. Currently available: Gaming
  • Category: Select your sub-type. For gaming: Match-3

The domain and category determine which analysis areas and prompts are used. A Match-3 game gets churn, engagement, monetization analysis (shared across all games) plus level difficulty and booster usage analysis (specific to match-3).

Data Warehouse​

Select your warehouse provider and enter connection details:

BigQuery:

  • Project ID: Your GCP project ID (e.g., my-gcp-project)
  • Location: Dataset location (e.g., us-central1, US, us-east5)
  • Datasets: Comma-separated dataset names (e.g., analytics, features)

Amazon Redshift (Serverless):

  • Workgroup Name: Your Redshift Serverless workgroup (e.g., default-workgroup)
  • Database: Database name (e.g., dev)
  • Region: AWS region (e.g., us-east-1)

Filter (optional): If your warehouse has data from multiple apps/tenants, set a filter:

  • Filter Field: Column name (e.g., app_id)
  • Filter Value: Your app's ID (e.g., my-app-123)

The agent will add WHERE app_id = 'my-app-123' to all queries.

AI Provider​

Select your LLM provider:

ProviderModel ExampleAuth
Claude (Anthropic)claude-sonnet-4-20250514API key
OpenAIgpt-4oAPI key
Ollamallama3.1:70bNone (local)
Vertex AIclaude-sonnet-4-20250514GCP ADC
AWS Bedrockus.anthropic.claude-sonnet-4-20250514-v1:0AWS credentials

Type the model name as free text — model lists change frequently, so we don't restrict to a dropdown.

For provider-specific configuration (Vertex AI project ID, Bedrock region), additional fields appear when you select the provider.

Schedule​

Leave disabled for now. You'll trigger runs manually. Scheduling can be enabled later in project settings.

Click Create Project.

Step 2: Add Your API Key​

After creating the project, go to Settings → Secrets tab.

  1. Select LLM API Key from the dropdown
  2. Enter your API key
  3. Click Save Secret

If your warehouse requires credentials (e.g., BigQuery from outside GCP):

  1. Select Warehouse Credentials (SA Key JSON)
  2. Paste your service account JSON key
  3. Click Save Secret

Go to Settings → Profile tab.

The profile tells the AI about your product — its genre, mechanics, target audience, monetization model. This context dramatically improves insight quality.

For a match-3 game, you'd fill in:

  • Basic Info: Genre, platforms, target audience
  • Gameplay: Core mechanic (match3), session type, difficulty curve
  • Monetization: Model (freemium/ad-supported), has IAP, has ads
  • Boosters: Name, description, consumable/permanent, purchasable
  • IAP Packages: Name, SKU, price, contents
  • KPIs: Target retention rates, ARPU, DAU

The form is generated from the domain pack's JSON Schema — different domains have different fields.

Step 4: Run Discovery​

Click Run discovery in the top bar.

The dropdown lets you configure:

  • Exploration steps: More steps = more comprehensive (default: 100). Start with 50-100 for your first run.
  • Estimate cost: Check this to see estimated LLM and warehouse costs before running.
  • Run All Areas: Runs all analysis areas (churn, engagement, monetization, etc.)
  • Select areas: Run only specific analysis areas.

Click Run All Areas to start.

What Happens During a Run​

The live progress panel shows each step:

  1. Schema Discovery — The agent lists your warehouse tables and reads their schemas
  2. Exploration — The AI writes SQL queries, executes them, analyzes results, and writes more queries based on what it finds. Each step shows:
    • What the AI is thinking
    • The SQL query it wrote
    • Row count and execution time
    • Whether the query was auto-fixed (SQL errors are retried with corrections)
  3. Analysis — For each analysis area (churn, engagement, etc.), the AI reviews all exploration results and generates insights
  4. Validation — Each insight's affected count is verified against the warehouse
  5. Recommendations — Based on all validated insights, the AI generates specific action steps

Step 5: Review Results​

Click the completed discovery card to see the full results.

Insights​

Insights are displayed in a table with:

  • Name: Specific finding (e.g., "Day 0-to-Day 1 Drop: 67% Never Return")
  • Severity: Critical, High, Medium, or Low
  • Area: Which analysis area found this (churn, engagement, etc.)
  • Players Affected: How many users are impacted
  • Confidence: How confident the AI is (based on data quality and validation)

Click an insight name to see:

  • Full description with exact numbers
  • Key indicators (specific metrics)
  • Risk score and confidence
  • Validation results (claimed vs. verified count)
  • The actual SQL queries that discovered this insight

Recommendations​

Each recommendation includes:

  • Title: Specific action (e.g., "Send Extra Lives After 3 Failures on Level 42")
  • Impact estimate: Expected improvement (e.g., "+15-20% retention")
  • Effort: Low, Medium, or High
  • Target segment: Exact criteria for who to target
  • Action steps: Numbered implementation steps
  • Related insights: Which insights this recommendation addresses

Feedback​

Use the thumbs up/down buttons on insights and recommendations:

  • Like: Tells the agent this finding is valuable — it will monitor for changes
  • Dislike: Tells the agent to avoid similar conclusions — add a comment explaining why

Feedback is used in subsequent runs. The agent won't repeat disliked insights and will track liked ones for changes.

Step 6: Run Again​

After reviewing results and providing feedback, run another discovery. The agent will:

  • Not repeat previously found insights (unless data changed)
  • Avoid patterns you disliked
  • Monitor insights you liked for changes
  • Focus on new patterns and unexplored areas

Each run builds on previous context, making discoveries more targeted over time.

Next Steps​