The Intelligence Engine

Turn noise
into signal.

FountainData isn't just a dashboard. It's a continuous learning loop that ingests unstructured feedback, extracts engineering specs, and quantifies revenue impact.

Ingest
2.4M
Signals Processed
Revenue Risk
$4.2M
Identified
AI Agents
Active
24/7 Monitoring
Specs
Auto-Gen
Jira/Linear Ready

How the Engine Works

A fully automated pipeline from raw data to revenue-critical decisions.

Step 01

Ingest & Normalize

Connects to 50+ sources. Normalizes unstructured text into a unified vector space.

Step 02

Cluster & Correlate

AI identifies semantic patterns and correlates them with account value (ARR) and churn risk.

Step 03

Quantify & Act

Outputs prioritized engineering specs with dollar values attached. Pushes to Jira/Linear.

Built for the
Product Operating System.

Financial Impact Core

Revenue-First Intelligence

Context is useful, but revenue is critical. We don't just cluster feedback; we calculate the exact ARR at risk for every bug, feature request, and friction point.

Issue: Checkout TimeoutCRITICAL
Affected ARR:$4.2M
Churn Risk:High (85%)
projected Loss:-$125k / qtr

Financial Attribution

Every bug is tagged with the ARR of the customer reporting it. Prioritize by dollars, not loud voices.

Auto-Spec Generation

We don't just find problems. We write the Jira ticket for you, complete with reproduction steps and user quotes.

Enterprise-Grade Security

SOC 2 Type II compliant. Your data is encrypted at rest and in transit. We offer single-tenant isolation for enterprise customers.

SOC 2 Ready
GDPR
HIPAA Option
Security Badge Placeholder

Built for Developers

Our API-first approach means you can ingest data from custom sources or query our intelligence engine programmatically.

// Example: Querying the Intelligence Engine
const response = await fountain.intelligence.query({
  workspace_id: "ws_123",
  query: "What are the top 3 reasons for churn this month?",
  filters: {
    segment: "enterprise",
    source: ["zendesk", "salesforce"]
  }
});

console.log(response.insights);
// Output: 
// [
//   { issue: "SSO Integration", revenue_risk: 450000, confidence: 0.98 },
//   { issue: "Reporting Latency", revenue_risk: 120000, confidence: 0.92 },
//   ...
// ]