Short answer Track it like search/PR monitoring but adapted to LLM behavior: (1) define the visibility signals you care about, (2) create a repeatable prompt/test-suite and schedule automated queries across each engine (via API where offered, otherwise browser automation), (3) parse and score the responses for brand mentions, prominence, sentiment and factual accuracy, and (4) store and visualize trends + alerts. Also monitor the upstream sources LLMs read (Wikipedia, news, docs) and publish canonical, machine-readable content to influence future model outputs.

Detailed, actionable plan

1. Decide goals & KPIs

• What “visibility” means for you: raw mentions, answer prominence (first sentence / headline), correctness, sentiment, presence in comparison queries, citations/sources shown, or hallucination rate.
• Example KPIs:
  • Mentions per 1,000 queries
  • Share-of-voice vs competitors (brand_mentions / total_brand_mentions)
  • Prominence score (weighted by position in answer)
  • Accuracy/confidence score (human-verified sample)
  • Citation rate (percent of responses that cite a verifiable source)
  • Time-to-fix for factual errors

2. Build a repeatable prompt/test-suite (the “canary queries”)

• Create a matrix of prompts covering:
  • Direct queries: “Who is [BRAND]?”
  • Intent queries: “I need a [product type] — recommend options”
  • Comparison: “Compare [BRAND] vs [COMP1, COMP2]”
  • Question variants / colloquial phrasing / localizations / voice-style prompts
  • Prompts that could trigger hallucinations or brand confusion
• For each prompt store metadata: locale, language, model/engine, temperature/randomness, date/time, context.

Sample starter prompts

• “Who is [BrandName]? Give a short description.”
• “What products does [BrandName] make?”
• “Is [BrandName] a good alternative to [Competitor] for X use case?”
• “List common complaints and praises about [BrandName].”
• “I need a [category] for [need]. Why would I choose [BrandName]?”

3. Data collection: query every engine consistently

• Use official APIs if available (preferred: reliable, rate-limited).
• If no API, use browser automation (Playwright / Selenium) with a set of accounts and consistent UI prompts. Obey Terms of Service.
• Vary parameters (temperature, system prompt) if that matches real user experiences.
• Collect full response text, metadata (timestamps), and any citations or links the engine returns.
• Collect region/locale and user persona context if the engine supports it.

4. Parse responses: extract signals automatically

• Detect brand mentions:
  • exact token match + fuzzy matching for nicknames/misspellings
  • entity recognition (spaCy, transformers, regex)
• Measure prominence:
  • Is brand in opening sentence? In title? Is it included in the recommended list?
  • Assign weights (e.g., 3 points if in first sentence, 2 if in top 3, 1 if mentioned later)
• Sentiment / qualitative attributes:
  • Use a sentiment model or attribute-extraction model to capture praise/complaints.
• Source/citation extraction:
  • If engines include sources, parse them and classify (high authority: official site, Wikipedia, news).
• Factual accuracy/hallucination detection:
  • Compare model claims to authoritative canonical sources stored by you (product pages, press releases, knowledge graph). Flag mismatches for human review.

5. Scoring & dashboards

• Define composite metrics:
  • Visibility score = αmention_count_norm + βprominence_score + γ*citation_quality
  • Brand share = brand_mentions / (brand_mentions + competitor_mentions)
• Build a dashboard (Grafana, Metabase, PowerBI) showing:
  • Time series for mentions & visibility score per engine
  • Example answers (rotating sample) for QA and auditing
  • Alerts for sudden drops/increases or new false claims
• Keep samples for manual QA and to train correction requests.

6. Monitoring upstream sources and “SEO for LLMs”

• LLMs are influenced by the corpus they see and by the sources they cite. Improve your footprint by:
  • Maintaining accurate, canonical product/service pages, FAQs, press pages
  • Adding structured data (schema.org), open graph metadata, and clear Q&A sections
  • Ensuring company profiles (Wikipedia, Crunchbase, Google Knowledge Panel) are accurate
  • Publishing authoritative content on high-domain sites and ensuring press releases are picked up
• For retrieval-augmented systems, provide API-accessible knowledge bases and partner integrations when possible (e.g., plugin/connector programs some vendors have).

7. Handling corrections & PR process

• When an engine produces a factual error:
  • Log and collect evidence from canonical sources.
  • If engine offers a feedback or “report incorrect” mechanism, submit it.
  • Correct the upstream source (site copy, Wikipedia) and document changes (date/time), because many models refresh from public web content.
• Keep a playbook: triage (is it harmful/misleading?), escalate to legal/PR if necessary, correct upstream content.

8. Sampling methodology & statistical validity

• Don’t rely on single queries. Run n variations across times, locales, and random seeds.
• Use stratified sampling: direct brand queries, competitor queries, intent queries, long-tail queries.
• Track confidence intervals in your metrics (so you know if changes are noise or significant).

9. Tech stack (practical suggestions)

• Orchestration: Python scripts, Airflow, or cron jobs to run scheduled queries.
• Automation: Official model APIs or Playwright/Selenium (with careful rate-limiting).
• Parsing & NLP: spaCy, Hugging Face models for NER and sentiment, or off-the-shelf NLU.
• Storage: SQL + object store for raw responses, or a vector DB for semantic dedup and searching.
• Dashboarding: Grafana / Metabase / Looker / PowerBI.
• Alerting: Slack/email/webhooks for anomalies.

10. Legal, ethical & operational cautions

• Respect Terms of Service — some providers prohibit scraping UI.
• Rate limits & cost: API calls to multiple engines can be expensive; monitor usage.
• Privacy: don’t send private or customer data into third-party LLMs without consent.
• Bias & hallucinations: track and flag risky misinformation.

Example metrics & simple formulas

• Mention rate = (number of responses that mention Brand) / (total responses)
• Prominence index = Σ(weight(position) for each mention) / number_of_responses
  • e.g., weight(position) = 3 if in opening sentence, 2 if top list, 1 otherwise
• Share-of-voice = brand_mentions / (brand_mentions + competitorA + competitorB)
• Citation quality = Σ(score(source_authority)) / citation_count

Quick monitoring workflow (practical)

1. Run prompt-suite across models every day (or weekly) and store raw outputs.
2. Run parser to extract mentions, sentiment, citations, and flagged claims.
3. Update dashboard and send alerts for threshold breaches (e.g., >10% drop in visibility or any “critical” hallucination).
4. For flagged errors, collect evidence and submit corrections to the engine (where a feedback path exists) and update source content.

If you want, I can:

• Create a starter prompt-suite tuned to your brand and competitors.
• Draft a simple Python orchestration + parsing script (API + browser fallback) you can run.
• Sketch a dashboard layout with the specific KPIs above.

Which would you prefer me to produce next?