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Documentation Index

Fetch the complete documentation index at: https://closedloop.sh/docs/llms.txt

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ClosedLoop AI autonomously transforms customer conversations into product intelligence scored by business impact. No manual tagging, no predefined categories — problems surface on their own. Every recommendation traces back to real quotes and deal values.

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How it works

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Import

Data is pulled automatically from your connected sources — call transcripts, chat messages, survey responses, support tickets. Webhook-based integrations deliver data in real-time.

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AI Analysis

Each conversation is analyzed to extract:
  • Product insights — specific feature requests, bugs, pain points, improvements — discovered autonomously, not from a predefined taxonomy
  • Strategic signals — satisfaction, churn indicators, competitor mentions, buying behavior
  • Business context — severity, emotion, deal impact, feature area
A typical 30-minute sales call produces 5-15 product insights and 10-30 strategic signals.

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Enrichment

Each insight is enriched with:
  • CRM context — linked to the customer’s company, deals, and contacts from HubSpot or Salesforce
  • Semantic indexing — enables hybrid search (keyword + meaning-based) so you find results even when exact words don’t match
  • Business outcomes — categorized by impact: retention, new sales, expansion, cost reduction

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Opportunity clustering

Related insights are automatically grouped into opportunities — product problems scored by Reach, Impact, and Confidence (RIC). This surfaces the biggest themes across all your data, including problems you didn’t know to look for.

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Zero-config, zero-maintenance

The entire pipeline runs autonomously. Connect once, and intelligence flows everywhere — your dashboard, your MCP-connected AI dev agents, your team. Insights are typically available shortly after a conversation is imported. Opportunity clusters update daily.