How to Do Technical SEO With AI: My Step-by-Step Workflow (2026) | CrawlRaven

How to Do Technical SEO With AI: My Step-by-Step Workflow (2026)

This workflow leans on one thing: real crawl data for AI to reason over. That's the half we automated — CrawlRaven runs a 200-point technical crawl and hands you a scored, prioritized issue list, so you can spend your AI time on judgment instead of spreadsheet wrangling. Starts at $9/month.

The workflow

My 6-step AI technical SEO loop

  1. Crawl the site
    Pull a full technical crawl, then let AI summarize what's broken.

  2. Prioritize with AI
    Feed the crawl export in and get an impact × effort ranking.

  3. Generate fixes
    Schema, robots.txt, redirect regex, hreflang — drafted in seconds.

  4. Validate everything
    Never ship AI output unchecked. Test schema, diff redirects.

  5. Make it AI-crawler ready
    Allow AI bots, server-render content, confirm it's actually readable.

  6. Monitor on a loop
    Re-crawl on a schedule so regressions surface fast.

First, get the expectations right

Every disaster I've seen with "AI SEO" comes from the same mistake: treating the model as an oracle instead of an analyst. AI is phenomenal at the boring, high-volume parts of technical SEO and genuinely bad at the parts that decide whether your work matters. Before any prompts, internalize this split:

What AI does well vs. what still needs you

🤖 AI handles the grunt work

🧠 You stay in the loop

Rule of thumb: let AI do the reading, sorting, and drafting — then verify every output yourself.

The stack I actually use

You don't need a new platform for this. My entire AI technical SEO setup is four things:

Step 1: Crawl first, then let AI read the crawl

AI can't audit what it can't see. So I start with real data: a full crawl that gives me status codes, titles, meta, canonicals, indexability, word counts, and response times for every URL.

Step 1 — summarize the crawl

You are a senior technical SEO. I'm attaching a CSV export from a site crawl 
(columns: URL, Status Code, Indexability, Title, Meta Description, Canonical, 
Word Count, Response Time).

Summarize the technical health of this site in plain English:
1. The 5 most serious issues, by how many URLs each affects.
2. Any patterns (e.g. a section returning 404s, canonical mismatches, thin pages).
3. Anything that looks like it could deindex pages or waste crawl budget.
Be specific and cite example URLs. Don't suggest fixes yet — just diagnose.

Step 2: Make AI prioritize by impact × effort

This is where AI earns its keep. I make the model do that scoring, then sanity-check it. Same export, new prompt:

Step 2 — prioritize the fixes

Using the same crawl data, build a prioritized action plan.
For every distinct issue type, give me a table with:
- Issue
- # of URLs affected
- Impact on rankings/indexing (1–5, with a one-line reason)
- Implementation effort (1–5)
- Quadrant: Quick Win / Major Project / Nice-to-have / Deprioritize
Sort so the highest-impact, lowest-effort items are at the top.
Flag anything that could remove pages from Google's index as CRITICAL, regardless of effort.

Step 3: Let AI draft the tedious fixes

Schema markup, robots.txt rules, redirect regex, hreflang clusters — this is finicky, error-prone, copy-paste work that AI is genuinely great at drafting.

Step 3 — generate JSON-LD schema

Generate valid schema.org JSON-LD for this page. I'll paste the content below.

Requirements:
- Use the most appropriate type(s) (e.g. Article, Product, FAQPage, BreadcrumbList).
- Only include properties you can fill from the content I give you — never invent ratings, prices, or dates.
- Output a single <script type="application/ld+json"> block, ready to paste.
- After the code, list any properties I should add manually and why.

Page content:
"""
[paste the page's visible content, headings, author, publish date here]
"""

Step 4: Validate everything (non-negotiable)

This is the step that separates “AI saved me hours” from “AI deindexed my blog.”

Step 5: Make the site AI-crawler ready

In 2026, technical SEO isn't just for Googlebot — it's for the AI engines increasingly sending (and answering) queries. Almost none of them render JavaScript.

Step 5 — analyze AI bot traffic in your logs

I'm pasting a sample of my server access logs. Analyze AI/search crawler behavior:
1. List every bot user-agent you see (focus on GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot, ClaudeBot, Google-Extended, Googlebot, Bingbot) and how many requests each made.
2. Which status codes are these bots receiving? Flag any non-200s they hit.
3. Are any of them being served redirects or errors on key pages?
4. Summarize: is anything blocking these bots from reaching my main content?

Logs:
"""
[paste a few hundred log lines]
"""

Step 6: Put it on a loop

A one-time audit is a snapshot; sites rot continuously. The real unlock with AI is that re-running this loop is cheap, so I schedule it instead of waiting for a quarterly panic.

The guardrails that keep this safe

  1. Real data in, or garbage out. Always feed AI an actual crawl, GSC export, or logs.
  2. Diagnose, prioritize, and fix in separate prompts. Mixing them lets critical issues hide behind cosmetic ones.
  3. Validate every output. Schema, redirects, robots — test before deploy, every time.
  4. Constrain the prompt. Prevent most hallucinations.
  5. You make the final call. AI ranks and drafts; you decide what matters to the business and own the result.

Key Takeaways