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Piyush Kumar Soni
Piyush Kumar Soni

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How LLMs Can Automate Technical SEO Workflows

#seo #aiseo #automation #technicalseo

Technical SEO has always involved a strange mix of strategy and repetition.

The strategic side is interesting: understanding how a website is structured, identifying why important pages are not performing, deciding what deserves to be indexed, improving internal linking, and prioritizing fixes that actually affect organic growth.

The repetitive side is less exciting.

SEO teams repeatedly review title tags, meta descriptions, canonical URLs, heading structures, status codes, internal links, schema markup, indexability signals, and large spreadsheets of crawl data.

This is where Large Language Models can become genuinely useful.

I am Piyush Kumar Soni, an SEO Expert & AI/LLM Specialist, and much of my current work focuses on combining Technical SEO, Generative Engine Optimization (GEO), AI Search, LLM integration, RAG, Entity SEO, Schema Markup, and automation.

I do not see LLMs as a replacement for technical SEO expertise.

I see them as a layer that can sit on top of deterministic SEO data and help teams interpret, prioritize, automate, and communicate findings faster.

This article explains where that approach works well, where it does not, and how I think about building LLM-powered SEO workflows in practice.

The Right Way to Think About LLMs in Technical SEO

A common mistake is asking an LLM to "audit a website" without giving it structured data.

That usually results in generic recommendations such as:

  • improve page speed
  • optimize title tags
  • add schema markup
  • improve internal linking
  • make content helpful

None of those suggestions are necessarily wrong.

But they are not an audit.

A real technical SEO workflow should start with actual evidence.

For example:

URL: https://example.com/product-a

Status: 200
Canonical: https://example.com/product-a
Robots: index, follow
Title: Product A | Example
Meta description: "Missing"
H1: Product A
Internal links pointing to page: 2
Structured data: Product
Word count: 430
Crawl depth: 5
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Now an LLM has something useful to work with.

The architecture should look more like this:

Website
   ↓
Crawler / SEO API
   ↓
Structured SEO Data
   ↓
Rules & Validation
   ↓
LLM
   ↓
Interpretation / Prioritization
   ↓
Report or Workflow Action
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The LLM is not replacing the crawler.

It is interpreting what the crawler found.

That distinction matters.

1. Automating SEO Issue Explanations

Most SEO crawlers are very good at identifying problems.

They can tell you:

174 pages are missing meta descriptions.

The crawler has done its job.

But somebody still needs to explain:

  • why that matters
  • which pages matter most
  • whether it deserves immediate attention
  • what the recommended action should be

An LLM can turn structured crawl findings into clear explanations.

For example, we could provide:

{
  "issue": "missing_meta_description",
  "affected_pages": 174,
  "important_commercial_pages": 12,
  "severity": "medium"
}
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The LLM could generate:

174 pages are missing meta descriptions, including 12 commercially important landing pages. Missing descriptions do not directly prevent indexing, but they reduce control over how these pages may appear in search results. Prioritize high-value landing pages before informational or low-traffic URLs.

That saves time without changing the underlying technical diagnosis.

2. Prioritizing Large SEO Audits

A technical audit can easily produce thousands of individual findings.

The challenge is rarely:

"Can we find SEO issues?"

The harder question is:

"Which issues should we fix first?"

This is one of the most useful applications for LLMs.

Instead of asking the model to invent priorities, give it structured context.

For example:

{
  "issue": "orphan_page",
  "pages_affected": 84,
  "page_type": "product",
  "organic_traffic": "high",
  "conversion_value": "high",
  "crawl_depth": null
}
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Compare that with:

{
  "issue": "long_title",
  "pages_affected": 250,
  "page_type": "blog",
  "organic_traffic": "low",
  "conversion_value": "low"
}
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A good workflow should prioritize the orphaned commercial pages before spending time shortening low-value blog titles.

The LLM can help group findings into categories such as:

Critical

  • important URLs blocked from indexing
  • broken canonical implementation
  • widespread server errors
  • accidental noindex directives
  • redirect chains affecting important pages

High

  • orphaned commercial pages
  • broken internal links
  • duplicate pages competing for the same intent
  • important pages buried deep in site architecture

Medium

  • missing metadata
  • weak heading structures
  • underlinked informational content

Low

  • minor formatting inconsistencies
  • cosmetic metadata opportunities

The model should not make these decisions blindly.

It should be given business and SEO context.

3. Metadata Analysis at Scale

Title tags and meta descriptions are simple until you have 50,000 URLs.

At that point, manual review becomes expensive.

An LLM-powered workflow can help classify metadata into categories such as:

  • missing
  • duplicated
  • generic
  • mismatched with page intent
  • overly repetitive
  • weakly differentiated
  • potentially templated incorrectly

Imagine sending this data:

URL: /services/technical-seo
Title: SEO Services
H1: Technical SEO Services
Primary topic: Technical SEO
Page type: Service
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A model can identify that the title is technically present but poorly aligned with the page's actual topic.

That is different from a simple character-count rule.

A deterministic rule can say:

Title exists.

An LLM can say:

The title is too generic compared with the page's H1 and topic.

This is where the combination becomes powerful.

4. Internal Linking Recommendations

Internal linking is another strong use case.

Traditional tools can show which pages have very few incoming internal links.

An LLM can help determine where relevant links could come from.

Suppose we have a target page:

Target:
Technical SEO Audit Services

Topic:
Technical SEO, site audits, crawlability, indexation
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And possible source pages:

1. Complete Guide to Technical SEO
2. How to Fix Crawl Errors
3. Social Media Marketing Tips
4. Schema Markup Guide
5. Local SEO Checklist
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The LLM can rank relevant internal-link opportunities based on semantic similarity.

It can also suggest natural anchor text such as:

  • technical SEO audit
  • professional SEO audit
  • identifying technical SEO issues

Again, human review still matters.

But the discovery process becomes much faster.

5. Schema Markup Generation

Schema markup is a natural area for automation because the output is structured.

For example, if we know:

{
  "name": "Piyush Kumar Soni",
  "jobTitle": "SEO Expert & AI/LLM Specialist",
  "url": "https://example.com/piyush",
  "worksFor": "Example Company"
}
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an LLM can help generate a Person schema template.

But I would never stop there.

The workflow should validate the JSON-LD after generation.

The architecture should be:

Entity Data
   ↓
LLM Schema Generation
   ↓
JSON Validation
   ↓
Schema Rules
   ↓
Final Output
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Do not blindly publish generated structured data.

LLMs can produce valid-looking JSON that contains inaccurate relationships or unsupported properties.

Schema automation should therefore combine:

LLM generation + deterministic validation + human review.

6. Entity Extraction for SEO and AI Search

Entity SEO becomes particularly interesting as search systems increasingly use semantic relationships rather than isolated keywords.

An LLM can extract entities from content such as:

  • people
  • organizations
  • products
  • locations
  • technologies
  • services
  • concepts

For example, from a professional profile it could identify:

Person:
Piyush Kumar Soni

Organization:
Lucky Digitals

Expertise:
Technical SEO
Generative Engine Optimization
AI Search
Large Language Models
RAG
Schema Markup
SEO Automation
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This can help teams examine whether their content consistently communicates important relationships.

That is useful for traditional search, knowledge graph clarity, and AI-driven retrieval systems.

7. GEO and AI Search Auditing

Generative Engine Optimization is still an evolving area.

I would be careful about anyone claiming there is a simple checklist that guarantees citations in ChatGPT, Gemini, Perplexity, or other AI systems.

There isn't.

However, we can audit factors that make information easier to interpret and retrieve.

An LLM-assisted GEO audit might examine whether:

  • the author is clearly identified
  • the organization is clearly identified
  • expertise is explicit
  • important entities are connected consistently
  • claims are supported
  • sources are cited
  • pages answer specific questions directly
  • structured data supports visible content
  • topics are covered with enough depth
  • terminology is consistent across pages

For example:

Person → worksFor → Organization

Person → knowsAbout → Technical SEO

Person → knowsAbout → AI Search

Article → author → Person
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These relationships can help create a clearer entity footprint.

The point is not to "trick an LLM."

The point is to make information less ambiguous.

8. SEO Reporting Automation

SEO reporting consumes a surprising amount of agency time.

Data might come from:

  • Google Search Console
  • Google Analytics
  • Ahrefs
  • Semrush
  • crawlers
  • rank trackers
  • spreadsheets

The challenge is combining these sources into a useful narrative.

An LLM can summarize structured metrics.

For example:

{
  "organic_clicks_change": "+18%",
  "impressions_change": "+24%",
  "average_position_change": "-1.8",
  "technical_errors_fixed": 34,
  "new_top_10_keywords": 12
}
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The model could generate:

Organic search performance improved during the period, with clicks increasing 18% and impressions rising 24%. Average ranking position improved by 1.8 positions, while 34 technical issues were resolved. Twelve additional keywords entered the top 10.

This type of automation is especially useful because the numbers come from deterministic sources.

The LLM is simply making them easier to understand.

9. Turning SEO Data Into Developer Tasks

SEO recommendations often fail at implementation.

An SEO says:

Fix canonicalization.

The developer asks:

Where? How? Which URLs? What is the expected behavior?

An LLM can translate SEO findings into more actionable tickets.

Instead of:

Canonical issue found.
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the workflow could generate:

Issue:
Category pages are self-canonicalizing correctly, but paginated URLs point to page 1.

Affected templates:
category-pagination.php

Expected behavior:
Each indexable paginated URL should use an appropriate self-referencing canonical unless the SEO strategy specifies otherwise.

Example:
Current:
/category/page/3 → canonical /category/

Expected:
/category/page/3 → canonical /category/page/3/
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That is significantly more useful to development teams.

10. Building SEO Content Briefs

Content workflows are another obvious application.

SEO tools can provide:

  • target keywords
  • related queries
  • competing pages
  • search intent
  • entities
  • subtopics

An LLM can combine those inputs into a structured brief.

For example:

Primary topic:
AI-powered SEO audits

Related topics:
Technical SEO
Python
LLMs
SEO automation
Schema
Internal linking
RAG

Intent:
Informational / technical
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Then the model can create:

  • proposed title
  • search intent summary
  • recommended sections
  • questions to answer
  • entities to cover
  • supporting examples
  • internal-link suggestions

But the brief should be based on collected data.

Otherwise we are back to generic AI-generated content.

11. RAG for Internal SEO Knowledge

One of the more advanced applications is combining technical SEO automation with Retrieval-Augmented Generation.

Imagine an SEO agency has:

  • internal SOPs
  • audit templates
  • client documentation
  • past recommendations
  • development standards
  • schema guidelines

Instead of relying only on the model's general knowledge, the system can retrieve relevant internal documentation before generating recommendations.

The workflow becomes:

Technical SEO Finding
         ↓
Search Internal Knowledge Base
         ↓
Retrieve Relevant SOP
         ↓
LLM
         ↓
Context-Aware Recommendation
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This allows recommendations to follow the organization's actual processes.

For example, an agency may have a specific canonicalization policy.

RAG can retrieve that policy before producing the recommendation.

That is much more useful than asking a generic model what to do.

12. AI Agents for SEO Workflows

Once individual automations are working reliably, they can be connected into agent-style workflows.

For example:

Crawl Agent
     ↓
Technical Analysis Agent
     ↓
Entity Analysis Agent
     ↓
Internal Linking Agent
     ↓
Report Agent
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I would still keep critical SEO checks deterministic.

The agents should orchestrate tasks, not invent facts.

A safer architecture is:

SEO Tools / APIs
      ↓
Verified Data
      ↓
AI Agents
      ↓
Analysis & Recommendations
      ↓
Human Approval
      ↓
Implementation
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That human approval layer matters.

What LLMs Should NOT Automate Blindly

Automation is useful until it creates more problems than it solves.

I would not allow an LLM to automatically:

  • redirect large numbers of URLs
  • change canonical tags sitewide
  • add noindex directives
  • delete content
  • rewrite important commercial pages
  • change robots.txt
  • modify structured data at scale
  • change internal linking across thousands of pages

without validation and approval.

These decisions can have significant SEO consequences.

AI should accelerate the workflow.

It should not remove accountability.

A Practical LLM + SEO Stack

A simple stack might include:

Data Collection

  • Screaming Frog
  • Sitebulb
  • custom Python crawler
  • Google Search Console API
  • Google Analytics API

Processing

  • Python
  • Pandas
  • SQL

LLM Layer

  • OpenAI API
  • other LLM APIs
  • LangChain where orchestration is useful

RAG

  • embeddings
  • vector database
  • internal SEO documentation

Output

  • Google Sheets
  • CSV
  • dashboards
  • client reports
  • project-management tasks

You do not need all of these at once.

Start with one repetitive task.

Automate it well.

Then expand.

The Bigger Opportunity: SEO + AI

The most interesting part of this shift is not that AI can write title tags faster.

That is useful, but small.

The bigger opportunity is building systems that connect:

Technical SEO

Entity SEO

Generative Engine Optimization

AI Search

Structured Data

LLM Integration

RAG

Automation

SEO professionals already work with large amounts of structured and unstructured information.

LLMs are very good at working with that type of information when they are given the right context.

That makes SEO a natural environment for practical AI automation.

Final Thoughts

LLMs can automate a significant portion of technical SEO workflows, but the strongest systems do not ask the model to do everything.

Use crawlers and APIs to collect facts.

Use code and rules to validate those facts.

Use LLMs to interpret, classify, prioritize, summarize, and communicate.

Use SEO professionals to make the final strategic decisions.

That combination is much more reliable than trying to build a completely autonomous "AI SEO expert."

The future of technical SEO is unlikely to be humans versus AI.

It looks much more like:

SEO expertise + reliable data + automation + LLM intelligence.

And that is exactly where some of the most interesting SEO engineering work is happening right now.

About the Author

Piyush Kumar Soni is an SEO Expert & AI/LLM Specialist with 15+ years of experience across SEO, web development and digital marketing. He works on Technical SEO, GEO, AI Search, LLM integration, SEO automation, Entity SEO, Schema Markup, RAG and AI agents.

Learn more: Piyush Kumar Soni

Top comments (1)

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bulti_global profile image
Bulti •

Putting rules and validation before the LLM is the right architecture. I’d apply the same separation to section 7: author identity, supported claims, and schema-visible-content consistency are auditable inputs, not GEO outcomes. In a 284-brand Korean DTC scan we ran across 50 AI shopping questions per brand, 65.5% had zero appearances and the mean was only 0.648 out of 50. So a site could pass every entity-clarity check and still be absent. I’d keep two ledgers: deterministic readiness checks, then repeated prompt-level mention and citation results. Have you tested whether any of your proposed audit factors actually predict movement in that second ledger after controlling for brand demand?