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Daily build note · May 15, 2026

Local AI Visibility Radar

A lightweight audit, monitoring, and action-plan tool/service that shows local businesses whether AI assistants recommend them, where the answers come from, and what to fix first.

Mixed Wildcard - Combine Or Alternate Between The Strongest Themes Above Build note published Public demo coming soon

What shipped

Built a runnable Python 3.11+ local audit kit for Local AI Visibility Radar. The CLI can create a demo audit, generate query packs and manual capture templates, ingest captured result JSON, score visibility and source quality, and render client-ready Markdown reports.

Main commands:

``bash PYTHONPATH=src python -m local_ai_visibility_radar demo --out audits/nashville-demo PYTHONPATH=src python -m local_ai_visibility_radar build audits/nashville-demo bash scripts/smoke.sh ``

Architecture

  • Standard-library Python CLI via argparse to keep setup simple.
  • JSON files for business profiles and captured results so an operator can inspect and edit data by hand.
  • Deterministic query generation, scoring, action prioritization, and Markdown rendering with no required API keys.
  • Separated modules for models, query generation, scoring, reports, and demo fixtures.
  • Reports are rendered as plain Markdown for easy client delivery, editing, or later conversion to PDF.

Trimmed scope

  • No SaaS app, accounts, database, billing, or hosted UI.
  • No scraping or automated assistant/browser capture.
  • No external LLM summarization, even optionally, in this build.
  • No PDF export; Markdown is the delivery artifact for the MVP.
  • No multi-location batch workflow.

Limitations

  • Manual result capture is required for real audits.
  • Fact matching is intentionally simple and transparent; abbreviations such as Ave versus Avenue may be flagged for operator review.
  • Scoring is directional and should be calibrated with more paid audit examples.
  • Surface volatility, personalization, and geography can change results between captures.
  • Demo data is fixture-based and must not be presented as live market evidence.

Suggested next steps

  • Add a small Streamlit review UI for filling raw_results.json.
  • Add optional PDF export from the Markdown client report.
  • Add a calibration file for category-specific scoring weights.
  • Add screenshot attachment indexing.
  • Add an optional OpenAI-assisted summary mode guarded by OPENAI_API_KEY, while keeping deterministic mode as the default.
  • Add monthly monitoring comparisons between two audit workspaces.