SEO vs GEO: A Step-by-Step Guide to Running Both in Parallel

Most marketing teams treat SEO and GEO as competing priorities. That framing is wrong, and it costs them traffic. The smarter move is to run them in parallel, using the same research, the same content, and a systematic workflow that satisfies both traditional search crawlers and AI answer engines simultaneously.

This guide walks you through exactly how to do that, step by step.

What You Are Actually Optimizing For

Before you start, be precise about the difference. SEO (search engine optimization) targets ranked links on Google and Bing. GEO (generative engine optimization) targets citations inside AI-generated answers from ChatGPT, Perplexity, Claude, and Gemini.

The ranking signals are meaningfully different:

SEO rewards backlinks, crawlability, Core Web Vitals, and keyword density in the right places.
GEO rewards factual authority, clean entity structure, structured data, and whether AI models consider your content a reliable source worth quoting.

Understanding the difference between GEO and SEO helps you avoid a common mistake: applying SEO tactics to GEO problems and wondering why nothing changes in AI answers.

The goal of this workflow is a content process where each piece you publish is simultaneously optimized for both.

Step 1: Audit Your Existing Visibility Baseline

You cannot improve what you have not measured. Start here before writing a single new word.

For SEO:

Run a crawl using Screaming Frog or Sitebulb. Flag pages with thin content, missing title tags, duplicate meta descriptions, and broken internal links.
Pull your Google Search Console impressions and click-through rates. Note your top 20 pages by impressions.

For GEO:

Manually prompt ChatGPT, Perplexity, and Claude with 5-10 of your target keywords. Write down whether your domain is mentioned, how it is described, and which competitors appear instead.
Use a monitoring tool like Bingly to automate this across models and get a baseline visibility score you can track over time.

Checkpoint: You have a spreadsheet listing your top 20 pages, their Google CTR, and whether each one is currently cited in AI answers for its target keyword.

Step 2: Prioritize Pages That Can Win in Both Channels

Not every page has equal leverage. Prioritize pages that already have some traditional SEO equity (backlinks, indexed status, decent impressions) but are absent from AI-generated answers. These are your highest-ROI targets, a relatively small content upgrade can unlock citations across multiple AI platforms.

Filter your baseline spreadsheet to surface pages that meet all three criteria:

Ranking on page 1-2 for a target keyword (meaning Google already considers them somewhat authoritative)
Zero or weak AI citations when you prompt models with that keyword
Topic is factual, answer-worthy, and not purely navigational

These pages represent the core SEO vs GEO gap in your current content strategy, well-indexed by traditional search but invisible to AI answer layers.

Checkpoint: You have a shortlist of 5-10 pages earmarked for the GEO upgrade sprint.

Step 3: Upgrade Content Structure for AI Readability

AI models do not crawl. They retrieve. The models that cite sources tend to pull from content that is structured like a reliable reference, clear entity definitions, factual claims with supporting context, direct answers near the top of the page.

For each shortlisted page, make these specific edits:

Add a direct answer in the first 100 words. If someone asks "what is [your topic]," your page should answer it immediately, without preamble. This is the single highest-impact GEO change.
Use H2 and H3 subheadings that mirror natural language questions. "How does X work?" and "What are the benefits of Y?" are more AI-readable than creative headline copywriting.
Define key terms explicitly. AI models look for authoritative definitions. If your page is about a concept, define it in a sentence that stands alone clearly.
Add a structured FAQ section. Mark it up with FAQ schema. This helps both featured snippets (SEO) and AI extraction (GEO). See the schema markup guide for AI search for implementation details.
Remove vague superlatives. Phrases like "industry-leading" and "best-in-class" are SEO-neutral and actively hurt GEO, AI models distrust promotional phrasing when selecting sources to cite.

Checkpoint: Each page has a direct answer paragraph at the top, properly nested headings, at least one term definition, and FAQ schema implemented.

Step 4: Build the Technical Foundation for GEO

Content quality matters, but AI models also rely on signals that tell them your site is a trustworthy reference point. These are technical and structural, and most SEO-focused teams have not addressed them.

Implement an llms.txt file. This is a plain-text file at yourdomain.com/llms.txt that tells AI crawlers which pages are canonical, authoritative resources. It is roughly analogous to robots.txt for AI systems. Follow the llms.txt implementation guide to set one up in under an hour.
Check your robots.txt does not block AI crawlers. GPTBot, PerplexityBot, and ClaudeBot all need to access your content. Many sites block them by default.
Verify your structured data is valid. Use Google's Rich Results Test and Schema.org validators. Broken schema hurts both channels.
Ensure fast page load times. AI crawlers time out just like Googlebot. Core Web Vitals work in your favor here.

Checkpoint: llms.txt is live, AI crawlers are not blocked, all schema validates cleanly.

Step 5: Monitor, Measure, and Iterate

The SEO vs GEO comparison only becomes useful when you are tracking both channels over time, not just in a one-off audit. Set up a recurring measurement cadence.

Weekly:

Check AI citation status for your top 10 target keywords across ChatGPT, Perplexity, Claude, and Gemini using Bingly. Log whether your domain appears, what competitors appear, and how the model characterizes your content.

Monthly:

Pull Search Console data for the same keyword set. Compare organic CTR trends against AI citation trends. You are looking for correlation, pages gaining GEO traction often see organic CTR improvement because AI-mentioned brands get more direct navigational searches.
Review any pages that gained or lost AI citations and identify what changed. This is your primary feedback loop.

Quarterly:

Run a full technical audit (crawl, schema validation, llms.txt review).
Expand your keyword list. GEO creates new citation opportunities that do not map directly to traditional keyword research.

For tracking AI citations at scale, see the AI citation tracking guide, it covers how to structure monitoring across multiple models without managing it manually.

Checkpoint: You have a Bingly dashboard or equivalent tracking AI citations weekly, and a Search Console report pulling alongside it monthly.

What a Mature SEO + GEO Program Looks Like

Once this workflow is running, the gap between your SEO and GEO performance should narrow. Pages optimized for AI readability tend to rank better in traditional search too, because the same signals that make content AI-citable (clarity, authority, structure, directness) overlap heavily with what Google rewards in its Helpful Content framework.

The teams that will compound the most over the next two years are the ones that stop treating seo vs geo as a resource allocation debate and start treating them as two measurement frameworks pointing at the same underlying content quality.

Start tracking your AI visibility today at Bingly, see which keywords your brand is winning in AI answers and where the citation gaps are before your competitors close them.