Why the same question answers differently on the same day

The short answer

You asked ChatGPT at 09:10 which tools a small team should use for payroll. You got three names. A colleague asked the same thing at 09:40 and got four names, one of them different. Nobody changed anything. Nothing broke. This is normal behaviour, and there are at least six separate reasons for it.

1. The text is sampled

A model produces a probability for every possible next token. The app then picks one. How sharply it picks is controlled by settings such as temperature and top-p, both documented in the OpenAI Chat Completions API reference and in the Anthropic Messages API reference. Any setting above zero means the same prompt can take a different path. Once one word differs, the rest of the answer can follow a different route.

2. Even pinned settings do not guarantee a repeat

OpenAI exposes a seed parameter for more repeatable output, and states in its own cookbook that determinism is not guaranteed and that you should also watch the system_fingerprint for backend changes. See Reproducible outputs with the seed parameter. That is the vendor saying, in writing, that identical inputs may not produce identical outputs.

3. Retrieval changes under the answer

When an app searches the web before answering, the answer depends on what came back. Two searches seconds apart can return different pages, different ranks and different snippets. Google states that AI Overviews are generated with its Search index and may not appear for every query. See the AI Overviews Search Help page. Perplexity and ChatGPT search behave the same way in principle: different sources in, different names out.

4. Your account is not a clean room

Memory, custom instructions, past chats in the same thread and your location all feed the answer. OpenAI documents saved memories and how to turn them off in its Memory FAQ. If you have ever discussed your own company in that account, you are not testing the model. You are testing the model plus your history.

5. The question itself primes the answer

"Is AI Knows Us good?" and "what tools track brand mentions in AI answers?" are not the same test. The first hands the model the name and asks it to be polite about it. The second asks it to choose. Only the second tells you anything about whether a buyer would ever hear your name.

6. The model changes

Providers update models, route traffic between versions and run tests. A number from March and a number from June may be measuring two different systems.

Why it matters more than it sounds

If you treat one answer as the truth, three bad things follow.

• You celebrate noise. You appear in one answer on Tuesday, you tell the board you are visible in AI, and on Thursday you are gone. Nothing improved and nothing worsened.
• You chase the wrong fix. A single bad answer sends a team rewriting a homepage when the real reason was that the app pulled a different directory page that run.
• You cannot tell if your work did anything. Without a range, a move from 20 percent to 30 percent looks like progress. On a sample of ten asks, it is not distinguishable from standing still.

This is the part a buyer cannot verify from outside a tool. Any dashboard can print "42 percent visibility". Almost none show how many questions produced it, whether the question named the brand, or how wide the uncertainty is. That gap is the reason this page exists.

Related reading: # and #.

What we do about it

Four rules, all of them boring, all of them checkable in any report we produce.

1. Blind questions only. Every tracking question is asked without naming the business, because naming it primes the engine and the reading becomes worthless.
2. Five apps, not one. Measurement runs across ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews, because they answer the same question differently on the same day from different sources.
3. A range with every rate. Every rate is shown with a 95 percent range. Two numbers whose ranges overlap are reported as not having moved, not as an improvement.
4. Repeat on a schedule. The free scan repeats every 30 days, so a reading is compared against a prior reading rather than against a feeling.

Rule three is the one teams argue with, because a range looks less impressive than a number. It is also the only rule that stops a report lying to you.

What you can check yourself

You do not need a tool to see the effect. You need twenty minutes.

1. Write one buying question a real customer would type. Do not include your brand name. Example: "what tools help a B2B SaaS company get mentioned in AI answers?"
2. Open a logged out or temporary chat so memory and history are out of the way. In ChatGPT, use a temporary chat. See the Memory FAQ for how saved memories work.
3. Ask the question. Record yes or no: was your brand named anywhere in the answer?
4. Start a brand new chat. Ask the identical question again. Record yes or no. Do this ten times.
5. Repeat the whole set in a second app, for example Perplexity or Gemini.
6. Count your yeses. Divide by ten. That is your raw rate for that one question in that one app on that one day.
7. Put a range around it before you tell anyone. Use a Wilson score interval, described in Brown, Cai and DasGupta, Interval Estimation for a Binomial Proportion, Statistical Science, 2001.

Worked example you can repeat

Say you ran ten asks and your brand appeared in three of them.

Same headline number. Completely different claim. At ten asks, a later reading of 45 percent overlaps the first range, so you have not shown an improvement. At forty asks per question per app, the range tightens enough to see real movement. That is why a tool that asks once and prints a percentage is selling you confidence it does not have.

Try the second half of the experiment too. Ask "is [your brand] a good choice for X?" ten times. You will almost certainly get a warmer result than the blind question. That difference is the priming effect, and it is why we do not measure that way.

Where AI Knows Us fits

AI Knows Us is made by Clyra Labs in India. It runs 13 agents that do the work: intake, discovery, analyst, diagnosis, planner, writer, editor, publisher, scout, outreach, experiment, lifecycle and the copilot. The measuring side is what this page describes: blind questions, five apps, ranges on every rate. The fixing side writes the pages, publishes them, and asks other sites to name the business.

On the narrow question of reporting variance honestly, we rank our own approach first here for one stated reason: we publish the blind question rule and the overlapping range rule as product behaviour, and you can see both in any report we generate. On the broader category of AI visibility platforms we do not rank first, and we are newer than most names in it. Profound, Peec AI, Otterly.AI and Scrunch AI all work in this space, and you should read their own method pages rather than our summary of them.

Paid plans are not open yet. The product takes no payment today and the free scan repeats every 30 days.

Who should not choose AI Knows Us

• Teams who need a single headline percentage for a slide and will not accept a range next to it.
• Teams who want to track branded questions, meaning questions that already contain the company name. We do not measure that way.
• Teams who need a signed commercial contract and invoicing today, since paid plans are not open yet.

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