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Daily build note · May 26, 2026

Lead Leak Recovery Desk

A productized service and lightweight tool that audits a local business's recent calls, forms, texts, inboxes, and booking calendar to find leads that were missed, mishandled, or never...

Local Business Automations Build note published Public demo coming soon

What shipped

  • A dependency-free local Node.js dashboard named lead-leak-recovery-desk.
  • CSV importers for calls, forms, calendar records, estimates, and manual notes.
  • File-backed JSON workspace storage under data/.
  • Deterministic normalization, leak classification, prioritization, and template generation.
  • A browser dashboard with metrics, queue filters, lead detail review, status updates, draft actions, leak-cause reporting, booked revenue ledger, and CSV import controls.
  • Markdown report and CSV queue exports under exports/current/.
  • Sample data for a fake Nashville HVAC business.
  • Service collateral for a $2,500 Lead Leak Diagnostic.
  • A smoke test covering reset, classification, booking update, report generation, and export verification.

Architecture

  • Used Node.js standard-library HTTP instead of Express to avoid install/network dependency risk while preserving the requested API surface.
  • Kept business logic in dedicated modules: importer, normalizer, classifier, templates, reports, and store.
  • Stored all workspace state in data/workspace.json so the demo is inspectable and portable.
  • Used deterministic templates instead of live LLM calls to keep the recovery loop runnable without credentials.
  • Made rule explanations visible on every queue item so owners can audit why a lead was flagged.

Trimmed scope

  • No auth, billing, multi-tenant model, database, or live third-party integrations.
  • No automated SMS or email sending.
  • No PDF export.
  • No LLM-assisted transcript analysis.
  • No real customer data.

Limitations

  • CSV schemas are intentionally narrow and match the committed samples.
  • Duplicate handling is contact-key based and basic.
  • Classification rules are transparent but not tuned on real-world client exports.
  • Compliance guardrails are operational checks only and are not legal advice.
  • The frontend is a local single-page dashboard, not a production SaaS shell.

Suggested next steps

  • Add schema validation and import error reporting per CSV row.
  • Add richer duplicate resolution across phone, email, and names.
  • Add a reviewer note field and durable status history UI.
  • Add adapter modules for CallRail, Twilio, Google Calendar, and common home-service CRMs.
  • Add optional PDF rendering for the owner report.
  • Add a fixture-based test suite for classifier edge cases.