aeo-audit-lite 0.1.0

Measure share of model: run a buyer prompt against an AI engine repeatedly and report how often a brand appears versus competitors, with a confidence interval.

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_KEY or OPENAI_API_KEY for 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

1. Sends your prompt to the engine --runs times.
2. Checks each answer for whole-word mentions of your brand and each competitor.
3. 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

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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 →