Solutions by need 7 of 8

Content gap analysis for AI search: fix what loses answers

Every answer that recommends a rival instead of you points to a gap: a theme you haven't covered, a source that doesn't name you, or a claim you haven't answered. MentionX finds them and ranks the fixes.

Start free See how it's calculated

Answers checked this scan800

Near-wins25%

Lost to a rival35%

Won30%

Example: Vela CRM is an invented brand, scanned on Nova (200 prompts × 4 engines).

The definition

What is content gap analysis for AI search?

Content gap analysis for AI search compares the answers where competitors are recommended with yours, finds the themes, sources and claims engines use for them, and turns each gap into a ranked action. MentionX ranks every fix by how many stored answers it could change.

Ranked in code: Fixes are ordered by how many stored answers they could change, not by an AI model's opinion.
Four kinds of fix: Near-wins, pages to publish, sources to earn and rival claims to answer.
A proof loop: The next weekly scan shows which answers moved.

Which CRM has the best built-in forecasting?

For forecasting, Pipewise^#1 is the usual pick: its pipeline predictions are built in[1]. Kestrel^#2 offers forecasting as an add-on.

[1]forecasting-guide.example

Recorded for Vela CRMLostGap: no page of yours explains forecasting. Fix: publish one, with examples.

Publish a pageEarn a sourceAnswer a claim

Example answers and .example domains. Pipewise, Kestrel and Vela CRM are invented brands.

Four kinds of answer

Every answer is won, nearly won, lost or open

MentionX sorts each stored answer by what it means for you, so the gaps line up in the order to fix them.

Open

No tracked brand is recommended. Whoever covers it first can take it.

Look for a CRM with forecasting and good mobile apps…

Lost

A rival is recommended and you're absent: publish or earn a source.

1. Pipewise: built-in forecasting…

Near-win

You're named but a rival is recommended: the fastest to flip.

…Vela CRM has basic forecasting, though…

Won

AI recommends you: protect the sources behind it.

2. Vela CRM, whose forecasting module…

Publish or earnlost

Fix firstnear-wins

Protectwon

The calculation

How content gaps are counted

Each square below is one stored answer, sorted by what it means for you. Pick a count to light up those answers.

WonNear-winLostOpen

22%

ChatGPT

26%

Gemini

24%

Google AI Overviews

28%

Google AI Mode

Each block is one engine: 200 prompts, 200 squares.800 answers this scan

Near-wins = answers that name you while recommending a rivalall answers

200 ÷ 800 = 25%800 = 200 prompts × 4 engines

200 near-wins come first: AI already names Vela CRM in them, so they're the fastest answers to flip.

Example data for an invented brand. Counts are calculated in code from stored answers; formulas are published on the methodology page.

Near-winsWon

Week over week

Close gaps, then count the answers

Each fix should move a known set of answers. Weekly re-runs show whether near-wins became wins.

WonWeek 1 to week 8+8 pts

Near-winsWeek 1 to week 8−4 pts

Example data for an invented brand.

The method

How to find and fix content gaps for AI, step by step

Day 0
Run the baseline
Every prompt on every engine in your plan, answers stored before scoring.
Day 1
Sort the answers
Won, near-win, lost or open, for every prompt and engine.
Week 1
Take the near-wins
Update the pages behind prompts where AI already names you.
Weeks 2–4
Publish and earn
Brief and draft pages for lost prompts; pitch the sources engines cite for rivals.
Monthly
Count what moved
Which near-wins became wins, by engine, with the answers that changed.

Reading the results

Five patterns worth acting on

Gaps cluster. These patterns show where one fix can change many answers.

Every chart here uses one colour code

Coral: absent, or a rival aheadBlue: mentioned, not pickedMint: recommendedSilver: first choice, or you

Many near-wins share one theme

One missing page is costing several answers.

First move: Write that page first; it can flip the most answers.

Topics Intelligence

Lost answers cite the same review site

One source is making the rival's case.

First move: Earn coverage there before writing more content.

Citation & Source Intelligence

Won on one engine, lost on another

Your page is cited by one engine only.

First move: Check the other engine's sources for the same prompt.

Cross-Engine Monitoring

Open answers in a new theme

Engines don't have a favourite yet.

First move: Publish early to shape the answer.

GEO Content Agent

A won answer slips to a near-win

A rival published a stronger page.

First move: Read the new answer and refresh your page.

Answers & Evidence

The tools behind it

The MentionX features that do the work

Plans

How much each plan tracks

Answers per scan is prompts × engines, re-run 4 times a month. Spark and above include unlimited seats and competitors; Starter includes 3 of each.

SparkSelf-serve

$149 /mo

100 prompts × 3 engines = 300 answers a scan

Each square is 10 answers1,200 a month

ChatGPT, Gemini and Google AI Overviews

Choose Spark

NovaSelf-serve

$249 /mo

200 prompts × 4 engines = 800 answers a scan

Each square is 10 answers3,200 a month

Adds Google AI Mode

Choose Nova

GalaxySelf-serve

$599 /mo

300 prompts × 5 engines = 1,500 answers a scan

Each square is 10 answers6,000 a month

Adds Perplexity

Choose Galaxy

GEO Content Agent drafts: 5 a month on Spark, 30 on Nova, 50 on Galaxy.

Questions

Content gap analysis, answered

The questions buyers ask before they start. Your first scan answers the rest with your own data.

Read the methodology

How do I find content gaps for AI search?

Compare the answers where competitors are recommended with yours: the themes, sources and claims engines use. MentionX does this from stored answers and ranks the fixes.

What should I fix first?

Near-wins: prompts where AI already names you but recommends someone else.

What is a near-win?

An answer that names your brand while recommending a competitor. It's usually the fastest kind of answer to flip.

Does MentionX write the content?

The GEO Content Agent drafts articles from your actions: 5 a month on Spark, 30 on Nova and 50 on Galaxy.

How are fixes ranked?

By how many stored answers each fix could change, calculated in MentionX's code.

How do I know a fix worked?

The next weekly scan shows whether engines changed their answers on those prompts.

Other needs

Start from a different question

Free forever, no card needed. Free is a plan ($0, no card, ChatGPT): 20 prompts the first week, then 10 a week. There is no 7-day trial

Find the gaps costing you answers

Prefer to talk first?

Talk to the founder· 20 min

Your first scan sorts every answer into won, near-win, lost or open, and ranks what to fix first.