How often should you re-check what AI says about your company for accuracy?

The question comes up the first time a client sees an AI assistant state the wrong price. Our answer is a layered cadence, because no single frequency fits every kind of error. Assistants rebuild their answers every time from whatever sources they retrieve, so a description can drift without anything changing on your side. One audit a year is too slow. Reading raw responses every day is too much.

The layers we run

Daily, automated. A prompt panel runs every day and the platform records whether the brand is mentioned, where, and with what sentiment. Nobody reads this daily. It exists so that a change is detected within a day rather than discovered by a customer.

Weekly, human. Someone who owns the brand reads the summary, not the whole feed. We look for wrong claims, new competitors appearing in the brand's answers, and any shift in tone.

Monthly, against a checklist. A marketer and a product owner test the answers against a written list of facts: what the company does, plan names and prices if public, features and exclusions, headquarters, leadership, integrations, retired products. Each wrong answer is logged with the engine, date, prompt and the sources the assistant cited.

After any event. A pricing change, rebrand, launch, retirement, funding announcement or leadership change triggers an immediate re-check. Old facts live on third-party pages long after the company's own site is updated, and assistants retrieve those pages.

Quarterly, source cleanup. We review which outside pages keep feeding wrong information and decide whether to contact the owner or publish a clearer replacement.

What the tooling needs to do

We run this on Promptwatch. These are the features we actually use for accuracy work:

Daily prompt panels per model, country and language, across ChatGPT (Search), Google AI Overviews, Google AI Mode, Copilot, Gemini, Perplexity, Amazon Alexa and Meta AI, plus other models via API.
Brand Book and aliases. The analysis instructions decide what counts as a mention, so the checklist of facts lives next to the analysis. Aliases keep older names and product spellings from slipping through.
Sentiment with a weekly AI summary. Every response gets a 1 to 100 score. The weekly summary makes the human review feasible in fifteen minutes.
Citation analytics. When an answer is wrong, we open the response and look at what it cited. The offsite mentions report is especially useful, because it shows pages that name the brand without linking.
Scheduled Chats and scheduled PDF reports. The weekly read arrives in-app or in Slack, so it does not depend on someone remembering.
Action Board. Every confirmed error becomes a task with an owner.

A note on refresh rate: if a tool only refreshes weekly, your fastest detection is a week. Ask about it before you buy. It matters most right after a pricing or positioning change.

Building the checklist

Do this once and reuse it. Write the facts you would be embarrassed to see wrong. Then write the buyer questions that would reveal them: "what does the company cost", "does it integrate with X", "who are the alternatives", "is it suitable for Y". We keep these as a tagged group of prompts so the monthly audit is just a filtered view.

Two habits help. First, keep the wording the way buyers phrase it, not the way marketing does. Second, include a few prompts about things you no longer offer. A retired product is exactly the kind of fact an assistant can keep repeating, because old pages about it stay online.

Fixing what you find

Log the engine, prompt, date and cited sources.
If your own page is the source, correct it and put the right fact near the top, in plain words.
If a third party is the source, ask them to update it, or publish a better-sourced page of your own.
Confirm the AI crawlers can fetch the corrected page. Promptwatch's Agent Analytics shows IP-verified crawler logs for bots such as GPTBot, OAI-SearchBot and PerplexityBot, with error tracking. Crawler logs start on the Professional plan.
Re-run the same prompts after a couple of weeks and compare periods.

Limits worth stating to stakeholders

We tell clients plainly that monitoring cannot force an assistant to change its answer. A correction may take time to show up, and an answer can differ between two askings of the same prompt. What the cadence gives you is early detection, a record of what was wrong and why, and a way to confirm a fix worked.

Regulated businesses should add one more rule: do not treat an AI answer as an approved statement. If a wrong claim touches legal, medical or financial matters, route it to the people who handle those, not to the SEO team. We track and report; we do not give professional advice on it.

A sensible starting point

If you are new to this, start with the weekly and monthly layers by hand. Build twenty to thirty checklist prompts, run them, and record what is wrong. Once you know how noisy your answers are, set up the daily automation and the scheduled summary. You will then know which cadence your brand actually needs.