aeo-audit-lite
Does AI actually recommend your product? Run a buyer prompt against an AI engine several times and measure how often your brand shows up versus named competitors — your share of model — with a confidence interval.
A single ChatGPT screenshot proves nothing: AI answers are non-deterministic. This measures properly, the same way every time.
- Zero dependencies — Python standard library only.
- Mock mode by default — runs offline with illustrative numbers.
- Bring your own key — set
PERPLEXITY_API_KEYorOPENAI_API_KEYfor real data.
Usage
Offline demo (no key needed):
python aeo_audit_lite.py --brand "Acme" --competitors "Datadog,Grafana,New Relic" \
--prompt "best observability tool for a Series A startup" --runs 12 --mock
Real measurement:
export PERPLEXITY_API_KEY=pplx-...
python aeo_audit_lite.py --brand "Acme" --competitors "Datadog,Grafana" \
--prompt "best APM for kubernetes" --runs 10 --engine perplexity
Example output
brand rate 95% CI share
------------------------------------------------------------
Datadog 10/12 [55.2–95.3%] 31.2%
Grafana 10/12 [55.2–95.3%] 31.2%
New Relic 8/12 [39.1–86.2%] 25.0%
Acme 4/12 [13.8–60.9%] 12.5% <- you
Your share of model: 12.5% (mentioned in 4 of 12 answers).
How it works
- Sends your prompt to the engine
--runstimes. - Checks each answer for whole-word mentions of your brand and each competitor.
- Reports mention rate, a 95% Wilson confidence interval, and share of model (your mentions ÷ all brand mentions).
Why repeat the prompt?
LLMs sample their output — ask twice, get two answers. One run is a coin flip. Repeating and reporting the median/interval is the difference between data and vibes.
Options
| Flag | Meaning |
|---|---|
--brand |
your product name (required) |
--competitors |
comma-separated competitor names |
--prompt |
the buyer question (required) |
--runs |
number of repetitions (default 10) |
--engine |
perplexity (default) or openai |
--mock |
force offline mock mode |
License
MIT © Clear Cited
This is the lite version. The full Clear Cited service measures dozens of prompts across every major engine (ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews), with human QC and a fix roadmap. Get a free teardown →
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