PENMAN
PENMAN
AI Visibility (AEO / GEO)

The system that tells you: are we selected as a source?

AI Visibility is the diagnostic layer inside PENMAN. It runs a repeatable check across LLMs (GPT, Claude, Perplexity, Google AI Overviews), extracts the sources AI prefers, classifies relevance into consistent tiers and surfaces “included vs ignored” gaps — so the next step is obvious: compare against the source set, generate recommendations, then execute safely.

What you get

Sources + tiers + visibility signals.

Why it’s usable

Normalized outputs, not messy AI text.

Where it goes next

Feeds AI Competition & Recommendations.

app.penmanpro.com/workspace/brand-ai
AI Visibility dashboard
How it ships

AI Visibility helps you measure gaps and decide what to improve next. Draft-first by default — you approve before anything goes live.

What you get

From audit to action plan, in one screen.

PENMAN runs your brand and category prompts against multiple LLMs — and turns the gaps into concrete article briefs.

Multi-model coverage

Track citations across GPT, Claude, Perplexity and Google’s AI Overviews. One score, one trend line.

Prompt-level diagnostics

See the exact prompts where you’re missing, the sources LLMs cite instead and the entities you need to cover.

From gaps to briefs

Each missing prompt becomes a concrete article brief in Content Creator — outline, target audience, talking points.

Step-by-step — how AI Visibility runs

A controlled pipeline, not a one-off audit.

Prepare context, run visibility, normalize results, then hand off to recommendations and safe execution.

1

Set the Workspace context

Choose the site/workspace and the page(s) you want to evaluate. The goal is to run visibility on the content you actually want AI to cite or reference.

2

Pass the prerequisite gate (structural / SEO analysis)

AI Visibility depends on a foundation layer. PENMAN runs/uses the structural readiness analysis first, so visibility signals map cleanly into actions later.

3

Run the AI Visibility check

PENMAN queries the selected AI contexts, observes the answer behavior and extracts the source set that AI is using for your topic/intent.

source extraction visibility signals repeatable runs
4

Normalize results into tiers and gaps

AI outputs are messy. PENMAN converts them into a consistent schema so teams can compare runs and decide quickly.

Tiering
High / Med / Low
Signal
Included vs Ignored
Gap
What blocks selection
5

Store run history

Visibility only matters if you can re-run and compare. PENMAN keeps the results so you can track movement and decide on the next batch.

6

Hand off to AI Competition & Recommendations

The extracted source set becomes your “competition.” PENMAN uses it to explain gaps and generate a backlog of recommendations and draft-safe tasks.

Best practice

Make AI visibility a cadence.

Run a weekly loop on priority topics and pages. Decisions get faster every cycle because the data structure stays the same.

Dependency

The module requires Workspace context and a completed structural / SEO analysis (prerequisite gate).

From visibility to publishing

Visibility results immediately drive action — not a dead-end report. Results flow into Recommendations, then into Safe Publishing with diffs and rollback.

When to run AI Visibility

“What’s blocking selection — and what should we ship next?”

Before a content refresh

Run visibility, then prioritize updates that match what AI already prefers.

After shipping a batch

Re-run to compare and decide whether to iterate or move to the next topic.

For stakeholder alignment

Use the source set and tiers to align content, SEO and editorial quickly.

Next step

Extract the source set — then compare and execute safely.

Run AI Visibility, then proceed to AI Competition and draft-first execution with diffs and rollback. Publishing is always explicit.