Search Intent Mapping for ChatGPT, Gemini, and Perplexity Search: How to Optimize Content for AI Discovery

Search intent mapping is becoming a central SEO process as users move between Google, ChatGPT, Gemini, Perplexity and other AI search interfaces. The question is no longer only whether your page can rank for a keyword. You also need to understand which answer the user wants, how an AI system may interpret that need, and whether your website offers the clearest source to cite or recommend.

This matters especially when several pages target similar terms. Poor intent mapping can create keyword cannibalisation, where your own URLs compete for overlapping queries and send inconsistent signals to search engines and AI systems. One page may explain a concept, another may promote a product, and a third may answer a comparison question, yet all three might use almost identical language.

The practical outcome is usually predictable:

Rankings fluctuate between related pages.
AI systems cite weaker or less commercially useful URLs.
Internal links become unfocused.
Content teams publish more pages without improving topical coverage.
Important conversion pages remain invisible during discovery.

A structured intent map gives you a way to organise the problem. You can assign each keyword, topic and URL a clear role, then build content that answers the underlying need rather than simply repeating the query.

What Is Search Intent Mapping?

Search intent mapping is the process of connecting a search query to:

The user’s likely objective.
The stage of the buying or research journey.
The best content format.
The most suitable website URL.
The evidence and entities needed for visibility.
The action you want the reader to take next.

Traditional SEO often classifies intent into four broad categories:

Intent category	Typical user goal	Common query examples	Suitable content
Informational	Understand a topic or solve a problem	“what is search intent mapping”	Guide, glossary, educational article
Commercial investigation	Compare solutions before choosing	“best AI writing tool for SEO”	Comparison, review, alternatives page
Transactional	Take an action or purchase	“SEO content automation software”	Product page, demo page, sign-up page
Navigational	Find a specific brand, tool or resource	“SEO Letters app”	Brand page, login page, help centre

That framework remains useful, but AI search introduces extra layers. ChatGPT, Gemini and Perplexity may infer a more detailed need from the whole conversation, not just one keyword. A query such as “How can I stop two blog posts competing for the same term?” could imply a need for diagnosis, a content audit, a redirect recommendation or a new editorial plan.

The phrase itself does not give you the complete answer. Context does.

Search Intent Is Now Conversational and Progressive

A user may begin with:

“What is keyword cannibalisation?”

Then continue with:

“How do I find it in Semrush?”

And later ask:

“Should I merge these two pages or redirect one?”

Each prompt represents a different intent state. The first is educational. The second is operational. The third is diagnostic and potentially transactional if the user starts evaluating an SEO platform or content workflow.

Your content architecture should account for this progression. If every page tries to answer every stage, the result can be broad but unfocused. If each page covers only a tiny fragment, the site may lack enough context for AI systems to understand its expertise.

Why ChatGPT, Gemini and Perplexity Change Content Discovery

AI discovery does not replace conventional SEO. It adds another interpretation layer between the user and your website.

A traditional search engine may display ten blue links, adverts, featured snippets and related questions. An AI search interface can summarise a topic, combine information from multiple pages and attach citations to sources it considers useful. The selection process appears to depend on more than exact keyword matching.

Signals may include:

Clear topical relevance.
Direct answers to specific questions.
Consistent terminology and entity relationships.
Evidence of first-hand experience.
Author and organisation credibility.
Freshness where the subject changes quickly.
Readable page structure.
Strong source alignment with the user’s question.
Technical accessibility and crawlability.
Agreement between the page title, headings and body content.

The precise weighting used by each system is not fully public and may change. Be cautious about claims that one formatting trick guarantees inclusion in an AI answer. It does not.

What you can control is the quality and clarity of the information you publish.

ChatGPT Search and Conversational Source Selection

ChatGPT users often ask layered questions. They may want an explanation, a recommendation and a practical next step in the same session. This makes content depth important, but depth alone is not enough. A page needs to expose its main answer clearly before moving into supporting detail.

For example, an article about keyword cannibalisation should not bury its definition beneath 800 words of general SEO theory. A concise explanation near the opening helps both the reader and automated systems identify the page’s purpose.

Useful elements include:

A direct definition.
A diagnosis framework.
Examples showing the difference between overlap and genuine cannibalisation.
A process for choosing the primary URL.
Clear internal links to related resources.
Author or brand information.
Original observations from audits or campaigns.

Gemini and Entity-Rich Search Experiences

Gemini may be used alongside Google services and broader information-seeking workflows. This makes entity clarity particularly important. Your content should make it obvious what each concept is, how it relates to adjacent concepts and which claims require supporting evidence.

For a page on search intent mapping, relevant entities may include:

Keyword research.
Query classification.
SERP analysis.
Keyword cannibalisation.
Topic clusters.
Internal linking.
Content briefs.
Search engine results pages.
Large language models.
Retrieval-augmented generation.
AI Overviews and conversational search.

Do not stuff these terms into every paragraph. That usually makes the writing worse. Instead, explain the relationships naturally, such as how intent mapping can identify two URLs that address the same problem from slightly different angles.

Perplexity and Citation-Oriented Research

Perplexity users often expect sourced answers. They may be conducting market research, validating a technical claim or looking for recent information. Pages that contain specific, attributable and well-structured information may be more useful in that environment.

Consider adding:

Publication and update dates.
Named authors or reviewers.
References to recognised documentation.
Original data where available.
Transparent methodology.
Practical examples with stated assumptions.
Clear explanations of what is known, inferred or still uncertain.

A citation is not an award. It is a sign that a page was useful for a particular answer at a particular time. Keep improving the page after publication.

The Relationship Between Search Intent and Keyword Cannibalisation

Keyword cannibalisation happens when multiple pages on the same website compete for substantially overlapping search needs. The problem is often misunderstood. Two pages can mention the same keyword without causing a serious issue. Overlap becomes risky when the pages have similar purposes, similar formats and similar authority, while neither URL has a clearly defined role.

A site might have these three pages:

“What Is Keyword Cannibalisation?”
“How to Fix Keyword Cannibalisation”
“Keyword Cannibalisation Audit Guide”

Some overlap is sensible. The pages become problematic when each one repeats the same definition, the same audit process and the same recommendations without a distinct audience or outcome.

Intent Overlap Versus Keyword Overlap

Keyword overlap is a measurable observation. Intent overlap is a strategic judgement.

Situation	Keyword overlap	Intent overlap	Likely action
Two pages mention the same phrase but serve different audiences	High	Low	Keep both and differentiate
Two guides answer the same question with similar depth	High	High	Consolidate or reposition
A blog article and a product page target the same broad term	Medium	Medium	Clarify funnel roles
One page ranks for an unexpected variation	Low initially	Unclear	Review performance before changing
Several weak pages cover one broad topic	Medium	High	Build a primary hub and supporting pages

The mistake is to merge pages solely because a keyword appears in both. That can remove valuable coverage and weaken the site’s ability to answer related questions. Start with the searcher’s job.

A Practical Cannibalisation Test

Use this five-question test for every pair of potentially competing URLs:

Would the same person click both pages from the same search result?
Would the user expect the same answer from both pages?
Do the pages target the same funnel stage?
Do they have similar titles, headings and calls to action?
Does Google or another search engine alternate between the URLs for similar queries?

If you answer yes to most of these questions, the pages probably need intervention. That could mean consolidation, canonicalisation, a clearer internal linking structure or a complete change of intent.

A Search Intent Mapping Framework for AI Discovery

The following process gives you a repeatable way to map keywords and content for ChatGPT, Gemini, Perplexity and conventional search engines.

Step 1: Collect Keywords by Problem, Not Just by Phrase

Start with a broad keyword set, but group queries around the underlying problem. Include:

Core terms.
Long-tail variations.
Question keywords.
Comparison phrases.
Brand and product queries.
Problem statements.
Action-oriented searches.
Queries that appear in customer support conversations.
Prompts used in sales calls and community discussions.

For a keyword cannibalisation project, your dataset might include:

Keyword cannibalisation.
What is keyword cannibalisation?
How to find keyword cannibalisation.
How to fix competing pages.
SEO pages competing with each other.
Should I merge similar blog posts?
Google ranking different page for same keyword.
Content consolidation strategy.

AI search often receives natural language questions that do not resemble traditional keyword lists. Customer language is valuable here because it captures the way people describe a problem when they are not thinking like SEOs.

Step 2: Identify the Dominant User Job

Ask what the user is trying to accomplish. Use an intent label that is more precise than informational or transactional.

User job	Example query	Page objective
Define	“What does keyword cannibalisation mean?”	Explain the concept
Diagnose	“Why are my pages competing?”	Help identify the cause
Evaluate	“Best tool for content gap analysis”	Compare approaches or products
Execute	“How do I merge two SEO pages?”	Provide instructions
Validate	“Is keyword overlap always bad?”	Correct a misconception
Decide	“Should I redirect or canonicalise?”	Support a technical decision
Monitor	“How do I track content cannibalisation?”	Explain measurement and reporting

This level of detail is useful for AI discovery because each page has a specific answer role. It also prevents a common editorial problem where every article begins as a guide and ends as a product pitch.

Step 3: Analyse Search Results and AI Responses

Review the current search landscape for each intent group. Look beyond the first result.

Record:

Ranking page types.
Common title patterns.
Frequently answered questions.
Missing subtopics.
Dominant content depth.
Use of templates, screenshots or examples.
Commercial elements.
Freshness signals.
Repeated claims that lack evidence.
Whether the results favour tools, agencies or educational resources.

You can also test representative prompts in ChatGPT, Gemini and Perplexity. Do not treat every response as a definitive ranking report. AI outputs can vary between sessions, regions, accounts and dates.

Instead, look for patterns:

Which sources are repeatedly mentioned?
What information does the response fail to answer?
Are product recommendations supported by clear evidence?
Which terms and entities appear in the explanation?
Does the system distinguish between similar concepts?
Are citations attached to specific claims or broad summaries?

This is a research method, not a guaranteed visibility predictor.

Step 4: Assign One Primary URL to Each Intent

Every major intent cluster should have a preferred destination. This does not mean only one page can mention the topic. It means one URL should carry the main responsibility.

Create an intent map such as the following:

Intent cluster	Primary URL	Supporting content	Conversion path
Define search intent mapping	Educational guide	Glossary and examples	Explore SEO Letters
Map intent for AI search	Strategic guide	AI discovery checklist	Start with the app
Diagnose cannibalisation	Audit guide	Technical troubleshooting article	Run a content audit
Create an intent-led content plan	Workflow guide	Topic cluster template	Build a campaign
Automate publishing	Product page	Feature articles and case studies	Create an account

This structure gives internal links a purpose. The supporting pages explain narrower issues, while the primary URL becomes the strongest reference for the main topic.

Step 5: Build an Entity and Evidence Brief

Before writing, document the concepts the page needs to explain and the proof it can provide.

A useful content brief might include:

Primary topic.
Search intent.
Audience.
Funnel stage.
Main question.
Secondary questions.
Required entities.
Claims requiring sources.
First-hand examples.
Recommended internal links.
Suggested conversion action.
Content freshness requirement.

For a technical SEO page, evidence might include Search Console screenshots, anonymised audit findings or a documented process. If you do not have original data, say so. Honest boundaries strengthen trust.

Step 6: Create a Distinct Content Contract

A content contract is a short statement describing what a page will do and what it will not do.

Example:

This page explains how to map search intent across conventional and AI search, with specific attention to keyword cannibalisation. It will provide a diagnostic workflow and content architecture examples. It will not serve as a full technical guide to redirects or a general introduction to keyword research.

That final sentence is useful. It protects the page from expanding into a vague all-purpose article and helps your team decide whether a new topic deserves a separate URL.

How to Structure Content for AI Discovery

AI systems may extract passages from the middle of a page, so every important section should work independently while still contributing to the wider argument. This does not mean reducing everything to disconnected answer blocks. Readers still need a coherent explanation.

Use a layered structure:

State the problem.
Define the main concept.
Explain the decision framework.
Provide examples.
Address exceptions.
Show implementation steps.
Add measurement guidance.
Present the next action.

Write Answer-First Introductions

The opening should establish the subject quickly. A strong introduction usually tells the reader:

What the topic means.
Why it matters now.
Who needs to act.
What the article will help them do.

For example:

Search intent mapping connects a query with the page, format and action most likely to satisfy the user. For AI discovery, the map must also account for conversational follow-up questions, citation needs and entity clarity. The process below shows how to identify overlapping pages, assign a primary URL and build a content system that works across ChatGPT, Gemini, Perplexity and traditional search.

That is clear. It does not require a dramatic claim.

Use Headings That Express a Question or Outcome

Weak heading:

AI Search

Stronger heading:

How AI Search Interprets Intent Beyond the Exact Keyword

Weak heading:

Cannibalisation Fixes

Stronger heading:

How to Decide Whether Competing Pages Should Be Merged or Repositioned

Outcome-led headings help readers scan the page and create more explicit topical signals. Keep them accurate. A heading should not promise a case study if the section contains only general advice.

Make Important Sections Self-Contained

A page may be cited for one specific point. Each major section should include enough context for that point to make sense.

For example, a section about redirects should clarify:

What a redirect does.
When it may be appropriate.
What evidence should support the decision.
What should happen to internal links.
How to monitor the result.

Avoid placing a key recommendation in a sentence that depends on five earlier paragraphs. That structure is difficult for readers and extraction systems alike.

Where SEO Letters Fits Into Intent Mapping

Search intent mapping requires research, editorial judgement and consistent production. The operational workload can become substantial when you are managing keyword clusters, content briefs, internal links, schema, images, publishing schedules and content refreshes across several websites.

SEO Letters is built for this wider workflow. It helps you move from a keyword or topic to a structured article, with research, headings, internal links, schema and images included in the publishing process.

The platform is particularly relevant when you need to:

Organise keyword research with difficulty ratings.
Build topical authority clusters.
Identify content gaps against competitors.
Create product-aware articles for affiliate or ecommerce publishing.
Generate content in 21 languages.
Publish directly to WordPress, Shopify or webhooks.
Route different stages to Gemini, OpenAI or Claude using your own keys.
Schedule autonomous campaigns.
Refresh existing content instead of publishing new articles endlessly.
Track how published content performs.

This is where intent mapping becomes a publishing system rather than a spreadsheet exercise. You define the strategic direction, then the workflow handles much of what sits between the brief and the live page.

A Worked Example: Mapping Keyword Cannibalisation Content

Imagine a SaaS company has four URLs:

/keyword-cannibalisation-guide/
/how-to-fix-keyword-cannibalisation/
/seo-content-audit/
/content-consolidation/

All four pages mention competing pages. The team suspects cannibalisation, but the URL data is unclear.

Initial Intent Classification

URL	Current apparent intent	Potential issue
Keyword cannibalisation guide	Definition and broad education	May overlap with the fix article
How to fix keyword cannibalisation	Execution	Could repeat the full definition and audit process
SEO content audit	Broad audit methodology	May target a wider audience
Content consolidation	Decision-making and implementation	Could overlap with merging guidance

The next step is not immediately deleting pages. Review performance and content roles.

Proposed Repositioning

Keyword cannibalisation guide: Define the issue, explain symptoms and provide a high-level diagnosis model.
How to fix keyword cannibalisation: Focus on consolidation, redirects, canonical tags, internal links and monitoring.
SEO content audit: Cover the complete audit process, including technical, content and performance checks.
Content consolidation: Explain when to merge, redirect, rewrite or retain pages, with a decision matrix.

The pages still share a topic. They no longer promise the same answer.

A Decision Matrix for Competing Pages

Use a simple scoring rubric before changing URLs. Scores should guide judgement, not replace it.

Criterion	Score 1	Score 3	Score 5
Intent similarity	Different user jobs	Partly related	Almost identical
Topic depth	Unique coverage	Some repetition	Mostly duplicated
Organic performance	One page clearly leads	Both inconsistent	Both weak
Backlink profile	Valuable links to both	Links unevenly distributed	Little authority
Conversion role	Different actions	Some overlap	Same action
Freshness and quality	One clearly stronger	Comparable	Both need work

Add the scores together:

6 to 12: Keep both pages, but improve internal linking and differentiation.
13 to 20: Consider substantial repositioning or selective consolidation.
21 to 30: Investigate merging, redirecting or creating one stronger primary URL.

Do not redirect a page merely because it has low traffic. A low-traffic page can hold useful backlinks, rank for valuable long-tail queries or serve a different audience. Check the complete evidence set first.

Optimising Content for AI Discovery Without Writing for Machines

The phrase “optimise for AI” can encourage unhelpful tactics. Repeating entities, forcing question headings and adding robotic summaries does not create expertise. AI systems are designed to interpret language, and readers quickly notice when a page has been written around a checklist rather than a real need.

Focus on five practical principles.

1. Resolve the Main Query Completely

Answer the central question with enough detail to support a decision. If the article covers keyword cannibalisation, explain identification, causes, fixes, exceptions and monitoring rather than stopping at a definition.

2. Show Experience Where You Have It

First-hand information can include:

The types of cannibalisation patterns found in audits.
How ranking URLs change over time.
Common internal linking mistakes.
The difference between seasonal URL movement and genuine competition.
What happened after a consolidation project.

Use anonymised examples if client confidentiality applies. Do not invent campaign results.

3. Make Claims Proportionate

Search algorithms and AI systems change. Use careful language where evidence is incomplete. A useful SEO article can say that a practice may help clarify relevance instead of claiming it will guarantee AI citations.

That is not weakness. It is accurate professional communication.

4. Define Technical Terms in Context

Readers may understand keyword research but not canonicalisation, retrieval or entity salience. Explain the term when it first becomes necessary, then use it consistently.

5. Keep the Page Current

AI discovery often involves questions about changing tools, policies and search features. Add a review date, check external references and update screenshots or workflows when the interface changes.

Measuring Visibility Across Search and AI Channels

AI discovery is difficult to measure with one universal metric. Track a group of indicators instead.

Measurement area	Useful KPI	What it suggests
Organic search	Impressions, clicks, rankings	Conventional visibility and demand
URL stability	Ranking URL consistency	Whether page roles are clear
Query coverage	Number of intent groups ranking	Breadth of topical reach
Engagement	Engaged sessions, scroll depth	Content usefulness
Conversion	Sign-ups, enquiries, assisted conversions	Commercial value
AI visibility	Citation frequency and source mentions	Presence in sampled AI responses
Content operations	Publishing and refresh completion rate	Workflow reliability

For AI monitoring, create a fixed prompt set. Include:

Definition prompts.
Problem-solving prompts.
Comparison prompts.
Brand prompts.
Industry-specific prompts.
Follow-up questions.

Run the same prompts periodically, recording:

Whether your brand appears.
Which URL is mentioned.
What claim is associated with the URL.
Whether the citation is accurate.
Which competitors appear.
What information appears to be missing.

This is directional data, not a perfect share-of-voice calculation. Treat it like a monitoring sample.

Track Cannibalisation Before and After Changes

Use Google Search Console, analytics data and a rank-tracking platform to compare:

Query-to-URL associations.
Impressions by URL.
Click-through rate.
Average position.
Conversion contribution.
Internal link changes.
Crawl and indexation status.

A useful warning sign is repeated URL switching for the same important query, especially when neither page has a stable performance advantage. Still, seasonal changes, algorithm updates and search demand shifts can create similar patterns, so investigate the full timeline.

Using SEO Letters for Repeatable Intent-Led Publishing

If you manage one website, intent mapping can be maintained manually. If you manage several brands, languages or publishing destinations, the process becomes harder to keep consistent. Content teams often lose the original intent decision after the campaign begins, then produce articles that slowly drift towards the same broad topics.

Use SEO Letters to turn a mapped topic into an organised publishing workflow. You can structure campaigns around topical authority, assign a cadence and publish to the destination that fits your operation.

A practical workflow looks like this:

Define the business topic: Identify the product, service or category you want to grow.
Research the keyword landscape: Review difficulty, relevance and existing content gaps.
Group queries by searcher job: Separate definition, diagnosis, comparison, implementation and purchase intents.
Assign primary URLs: Decide whether the content belongs on a blog, landing page, category page or product page.
Generate the article brief: Include headings, related questions, entities, links and conversion requirements.
Create the content: Produce a structured article in the required brand voice.
Review factual and commercial claims: Check evidence, accuracy and compliance.
Publish directly: Send the article to WordPress, Shopify or a webhook.
Measure performance: Track rankings, engagement, conversions and URL stability.
Refresh the asset: Update the page when search behaviour, competitors or product information changes.

The autonomous campaign scheduler is useful when the strategy is already clear. You can set the topic, cadence and publishing destination, then let the system research, write and publish on schedule. For mature sites, content-refresh campaigns can be more valuable than simply increasing publishing volume.

Common Search Intent Mapping Mistakes

Creating One Page for Every Keyword Variation

A separate URL for every small variation can produce thin coverage and internal competition. Google and AI systems usually understand related language, so the better approach may be one comprehensive page with a clear purpose.

Create separate pages when the audience, task, funnel stage or expected answer genuinely changes.

Treating All Informational Queries as Equivalent

Informational intent is broad. Someone searching for a definition needs a different page from someone implementing a technical fix. A generic label conceals useful differences.

Break informational queries into user jobs and decision points.

Optimising Only for Search Volume

High-volume terms can attract attention but may have weak commercial value or intense competition. A lower-volume query about a specific problem may bring users who are much closer to taking action.

Include these metrics:

Relevance to the offer.
Ranking difficulty.
Conversion potential.
Existing authority.
Content production cost.
Competitive gap.
Likely lifespan of the topic.

Publishing AI-Generated Pages Without Editorial Control

Automation can increase output, but unsupervised publishing can multiply errors. Weak facts, generic examples and incorrect internal links can damage trust across an entire site.

Use human review for:

Technical claims.
Legal, financial or health information.
Product comparisons.
Statistics.
First-hand experience statements.
Brand positioning.
Redirect and canonical recommendations.

SEO Letters supports the workflow. It should not remove accountability from the team publishing the content.

Ignoring Existing Pages

New content is easier to plan than a content audit. That does not make it the right choice. If several old URLs already cover a topic, map them before commissioning another article.

A quarterly content review can identify:

Pages with declining impressions.
URLs targeting the same intent.
Articles with outdated references.
Orphaned pages.
Posts attracting irrelevant queries.
Content that should support a stronger commercial page.

A Content Brief Template for AI Search Intent

Use the following template for each important topic.

Strategic Definition

Primary topic:
Business objective:
Target audience:
Funnel stage:
Main user job:
Primary URL:
Supporting URLs:
Primary conversion action:

Search and AI Research

Core queries:
Conversational prompts:
Related entities:
Common competing formats:
Questions missing from current results:
Sources requiring review:
Freshness requirement:

Editorial Requirements

Working title:
Main answer:
Required sections:
Practical examples:
Evidence available:
Internal links:
External references:
Schema type:
Image requirements:
Reviewer:

Cannibalisation Controls

Existing pages with overlap:
Distinct role of this page:
Terms to avoid targeting as the primary phrase:
Internal link destination:
Consolidation risk:
Review date:

This document takes a little time to create. It can save much more time later, particularly when several writers or automated campaigns are working on the same site.

When to Use SEO Letters for This Workflow

If you’re publishing regularly and the connection between research, writing and deployment is breaking down, SEO Letters is designed to close that gap. It is not simply a text generator. The platform combines keyword research, topical authority planning, competitor gap analysis, structured article creation and direct publishing in one operating workflow.

It can be a strong fit for:

In-house SEO teams managing large editorial calendars.
Agencies producing content for multiple clients.
Affiliate publishers creating product-aware articles.
Ecommerce teams publishing category and buying guides.
International businesses working across 21 languages.
Teams that need scheduled campaigns rather than one-off drafts.
Businesses maintaining old pages through systematic content refreshes.

You can also bring your own AI keys and route stages to Gemini, OpenAI or Claude. That gives your team more control over model selection, costs and workflow design.

Key Takeaways for Search Intent Mapping

Search intent mapping for AI discovery is an exercise in clarity. You are clarifying what the searcher wants, which URL should answer it and how that answer connects to your wider content system.

The main principles are:

Map user jobs rather than relying on broad intent labels.
Assign one primary URL to each important intent cluster.
Treat keyword overlap as a signal to investigate, not proof of cannibalisation.
Structure content so individual sections answer clear questions.
Use entities naturally and explain their relationships.
Support important claims with evidence and transparent experience.
Monitor which URLs appear for priority queries.
Sample ChatGPT, Gemini and Perplexity responses consistently.
Refresh existing content when the topic or search landscape changes.
Use automation to execute a strategy, not to avoid editorial judgement.

The sites most likely to perform well in AI-assisted discovery will not necessarily be those publishing the most pages. They will be the ones with a coherent information architecture, trustworthy explanations and clear ownership of each search intent.

If keyword cannibalisation is already affecting your visibility, begin with an inventory of URLs, queries and user jobs. Then use the resulting map to consolidate weak overlaps, strengthen primary pages and build a publishing schedule around genuine topic gaps. For a faster path from that strategy to structured, published content, start building your workflow in SEO Letters. You can also use the rightbar as the contact path when you need help assessing the best setup for your site.