Zeo delivery method

Technical & Crawl Audit

A short backlog of findings that reproduce on a named cohort, each tied to a likely root cause, an owner, and an acceptance test that says whether the fix held.

A crawler can return thousands of warnings before lunch, but that does not mean your site has thousands of separate problems. We test the patterns on live URLs, distinguish template defects from noise, and prioritize only the findings your team can act on.

You get a focused, evidence-backed backlog that connects each confirmed issue to its root cause, affected templates, and acceptance tests.

Turn the crawl into a backlog with ZeoSEO Audit
A Zeo analyst organizes a large pile of crawl alerts into root-cause groups on an evidence board, then moves a short priority stack to engineering.

Some of the 500+ brands we've worked with

See all references
  • Hyundai
  • Findeks
  • Watsons
  • Yemek.com
  • İstanbul Gedik Üniversitesi
  • Desa
  • Doğtaş

Stages and gates

How we do it

Five stages (lock scope, reproduce the states, find the shared cause, hand off testable work, then rerun the evidence).

  1. Define the crawl population

    Before collecting data, we agree on seed sources, scope rules, authentication, user agent, rendering mode, exclusions, protected journeys, and the release window.

    A reproducible crawl with a clear record of what was included and excluded.

    AI assist
    The seed-source and exclusion list gets drafted from the site's existing templates and URL taxonomy, which grounds the initial scope in its current structure.
    Human gate
    Authentication, rendering mode, and protected-journey exclusions are approved by our technical lead, because those three choices decide what is in scope. Crawl scope approved before collection

  2. Reproduce crawl and index states

    For the same cohort, we connect crawl rows with Search Console inspections, server requests, source HTML, rendered HTML, sitemaps, and live URL checks.

    A findings register in which every issue links to the evidence that confirms it.

    AI assist
    AI cross-references crawl, index, server-log, and rendered-HTML records for each URL in the cohort, removing the need to join them manually.
    Human gate
    Samples of the joined evidence get checked against the live pages before the register is accepted as proof of anything. Joined evidence spot-checked before use

  3. Group symptoms by root cause

    Agents cluster repeated redirects, canonicals, directives, rendering gaps, broken links, and template states. A specialist tests the pattern and keeps the counterexamples visible.

    A short list of shared causes instead of thousands of duplicates.

    AI assist
    Clustering repeated redirects, canonicals, directives, and rendering gaps by shared shape across the whole crawl.
    Human gate
    A specialist tests the clustered pattern against counterexamples before it's allowed to explain the whole group. Root cause tested against counterexamples

  4. Build an actionable engineering queue

    We prioritize findings according to affected eligible URLs, user and search impact, confidence, recurrence, dependencies, reversibility, and ownership.

    Each ticket includes examples, an owner, a completion test, and a clear condition for stopping or rolling back.

    AI assist
    AI sorts candidate tickets by affected URL count and recurrence, giving the team a more useful starting point than an alphabetical list.
    Human gate
    Confidence, dependencies, and reversibility get weighed against what engineering can actually absorb before the priority order is approved. Priority order approved for engineering

  5. Recheck the same cohort

    After the fix, we recrawl the same URLs, repeat representative inspections and rendering checks, and compare protected outcomes before closing anything.

    Findings close only when the defect is gone without creating another, and recurring root causes become platform tests.

    AI assist
    Rerunning the original crawl and inspection checks against the same cohort and flagging any URL where the fixed state didn't hold.
    Human gate
    A specialist confirms the protected behavior held before closing the finding and decides whether a recurring cause becomes a platform test. Finding closed after recheck

AI joins the evidence and clusters the symptoms; specialists prove the cause on live URLs.

AI drafts the seed-source and exclusion list from the site's existing templates and URL taxonomy, cross-references crawl, index, server-log, and rendered-HTML records for each URL in the cohort, clusters repeated redirects, canonicals, directives, and rendering gaps by shared shape, sorts candidate tickets by affected URL count and recurrence, and reruns the original checks against the same cohort to flag any URL where the fixed state did not hold. It proves nothing by itself. We do not bypass authentication, crawl prohibited private data, or overload your servers, we do not treat crawler warnings as confirmed defects or inflate the total with duplicate URLs, we will not hide contradictory samples that weaken a tidy explanation, and we do not guarantee indexation or ranking gains from a finding.

Deliverables and acceptance

What you get

Everything here is shaped to go straight into the engineering queue.

  • Audit report

    Reproducible findings register

    Accepted when

    Every finding includes the precise cohort, raw evidence, representative URLs, confidence level, counterexamples, and likely owner.

  • Prioritized backlog

    Impact-priority backlog

    Accepted when

    The backlog ranks reproduced findings and explains why each item sits ahead of or behind another.

  • Tracking plan

    Implementation tickets

    Accepted when

    Every ticket identifies the relevant rule or template, examples, acceptance test, protected behavior, rollout sequence, and rollback.

  • Decision matrix

    Verification & recurrence record

    Accepted when

    The record uses the same cohort after release and converts repeated root causes into tests, rules, or ownership checks.

We call it done when: The findings register, priority backlog, implementation tickets, and recurrence record are done when every finding carries its precise cohort, raw evidence, representative URLs, confidence level, counterexamples, and likely owner, the backlog ranks only reproduced findings and explains each position, every ticket names the rule or template, examples, acceptance test, protected behavior, rollout sequence, and rollback, and the record reuses the same cohort after release and converts repeated root causes into tests, rules, or ownership checks.

Fit and readiness

When you need this

A lengthy crawler export is not an audit. It is the starting material for deciding which warnings are real, which repeat, and which deserve attention first.

A good fit when

  • Your crawler exports are full of warnings, but you cannot reliably separate confirmed defects from duplicates and false positives.
  • The same technical issue appears across many URLs, and you need to find out whether a shared template or rule is responsible.
  • Your engineers need tickets with precise examples, affected cohorts, acceptance tests, and a defensible priority order.

Better handled as other work when

  • You only want a crawler score or issue count, without checking the problem on live URLs and rendered states.
  • Authorized crawl, index, server, or template evidence is unavailable, and there is no technical owner who can review the findings.

If one of these is closer to your situation, start here instead: SEO Audit

We call it done when: Every high-priority finding can be reproduced on a defined cohort, each ticket names a likely root cause and an owner, and a repeat crawl says plainly whether the fix held.

The specialists behind this SEO work

Zeo's SEO work goes back to 2006, when we started what we call the first SEO blog in the MENA region. The consultants shown here are doing that work today, matched to what this page covers.

Content we've produced on this topic

Articleby Elif Naz Akan KarakoçCommon Mistakes in Robots.txt FilesVideoby Mehmet AktuğCrawl Bütçesini Optimize Ederek SEO Görünürlüğünü Artırmak - Mehmet AktuğArticleby Metehan UrhanWhat is Robots.txt File, and Why is it Important?Articleby Ruhan TiryakiWhat is Mobile First Indexing (MFI)? What Should Be Considered?

Tools we use

Tools behind this work

  • Screaming Frog

    creates the reproducible crawl population and raw findings register

  • Sitebulb

    clusters repeated crawl warnings into likely shared template causes

  • Lumar

    tracks technical patterns across large template and release cohorts

  • PageSpeed Insights

    adds field and lab performance evidence to affected templates

  • WebPageTest

    replays rendering defects with detailed request and visual timelines

  • Google Search Console

    checks crawler findings against Google's observed URL and index state

Next step

Turn the crawl into a backlog you can trust

Bring your latest crawler export, even if it's messy. We'll help you separate confirmed shared defects from noise and find the first fixes worth handing to engineering.
Turn the crawl into a backlog with Zeo

Before we start

Questions we hear

A crawler gathers possible issues. The audit checks those candidates against live URLs, index evidence, server behavior, and rendered states, groups duplicates by root cause, and sends only confirmed patterns into the engineering queue.