The Answer Loop: monitor, plan, produce, publish, measure

AI assistants answer buyer questions by naming companies and citing sources. The Answer Loop is a weekly cycle that checks what those answers say, decides which gaps are worth closing, produces evidence-backed pieces, publishes them where answers look, and measures what changed on the same questions. It does not promise a ranking or a mention. AI answers vary by engine, account and day.

A fixed set of buyer questions is run on a schedule in ChatGPT, Perplexity, Gemini, Grok, Microsoft Copilot and Google AI Mode. Each run records which companies the answer names, how it names them and which sources it cites, with the date and engine. The same questions are re-run so change is measured against the same baseline.

Checked or done weekly

• Re-run the fixed question set and log named companies and cited sources
• Check which pages and which LinkedIn and Reddit threads are being cited
• Check that AI referrals are still being tagged on the site

The log shows which questions name a competitor and not you, and which sources the answer relies on. The plan picks the questions where a specific page, post or thread could realistically be cited, starting with narrow questions because the November 2026 data shows narrow questions name more specialists than broad ones.

Checked or done weekly

• Rank gaps by buyer intent and by how specific the question is
• Choose the page, post, thread or video each gap needs
• Write down the claim each piece must support and the source for it

Pieces are written from a written source of truth and original data, with every claim checked against a source before it is approved. Output is buyer-question pages, founder LinkedIn posts, Reddit answers, videos and research pieces.

Checked or done weekly

• Draft from the source of truth and the dated data
• Run the claim check on each draft
• Founder approves before anything is published

Each piece goes where the answers look: the company site with schema and a markdown twin for agents, LinkedIn, Reddit and, where relevant, directories and registries. Pages carry dates, methodology and sources so they stay citable.

Checked or done weekly

• Publish approved pieces with schema, sitemap and llms.txt entries
• Post to LinkedIn and Reddit in the founder voice
• Submit listings that are due

Results are read in three places: whether the company is named in the monitored answers, whether the cited sources include its pages, and whether tagged AI referrals and inbound leads arrive. AI answers vary between runs and accounts, so a change is reported as what was observed on a date, not as a guarantee.

Checked or done weekly

• Score each monitored answer from 0 to 3 and total it out of 100 with the AI Answer Audit rubric
• Compare with the previous run on the same questions
• Record referrals and leads by source in the CRM

• Monitor and measure: the free AI Answer Audit, the dated monthly index and the AI answer reports.
• Plan, produce and publish: services, including AEO and GEO engineering and LinkedIn ghostwriting.
• Proof of what has been built: the build log.

The loop reports observations with dates. It does not publish results for any client on this page, and a single run of an AI engine is a sample, not a measurement of everything buyers see.