Zeo delivery method

Core Web Vitals & Speed

A field baseline you can still defend six weeks later, one fix shipped at a time, and a named person deciding whether each one stays.

A perfect lab score doesn't always mean real visitors feel it. We track down what's genuinely slow for real users, fix it, and check that checkout, forms, and accessibility don't break on the way.

We fix what the data actually points at, without breaking what pays the bills.

Look at your vitals togetherTechnical SEO
A person gives an approving thumbs-up next to an oversized stopwatch ringed with speed lines, marking a fast result.

Some of the 500+ brands we've worked with

See all references
  • Axa Sigorta
  • TRT
  • Gusto
  • DYO
  • Sportive
  • Odamax

Stages and gates

How we do it

Nothing moves on opinion here. Each stage runs on measured evidence, and a person signs off before anything ships.

  1. Agree what matters

    We pick the templates, journeys, devices, and regions that actually define your real-user speed question, while agreeing in advance on what remains out of scope.

    A scoped baseline with named owners and stop conditions attached.

    AI assist
    AI cross-references traffic volume and existing field-data coverage across every template to shortlist the handful that actually carry real-user risk.
    Human gate
    Our performance lead and your team agree on the final template and journey list, and put a name against everything explicitly out of scope. Scope and exclusions signed off before baseline work starts.

  2. Reproduce it in the lab

    We take the field's slowest real journeys and reproduce them under controlled lab conditions using the same device, network, and cache each time, so server, rendering, and third-party costs can be told apart.

    A bottleneck list backed by traces anyone can rerun, with the shaky ones flagged instead of hidden.

    AI assist
    AI reruns the same journey across the device, network, and cache permutations needed to isolate server, rendering, and third-party cost, and flags any run that won't reproduce cleanly.
    Human gate
    Whether a trace is clean enough to diagnose from, or still too noisy to trust, is our performance lead's call. Bottleneck list closed only once traces reproduce cleanly.

  3. Find and rank the real culprits

    Agents cluster repeated patterns across traces, component inventories, and release history, and link every candidate back to its raw evidence. Our performance lead tests the high-impact and edge cases before anything gets ranked.

    A list ordered by user impact, effort, and how reversible each change is.

    AI assist
    AI clusters repeated failure signatures across traces, component inventories, and release history, and links every cluster back to the raw evidence behind it.
    Human gate
    Our performance lead hand-tests the highest-impact and most awkward edge cases, and pulls any cluster that does not hold up before ranking. Ranking locked only after edge-case spot checks pass.

  4. Ship one fix at a time

    We release one bounded, reversible change and run it past functional, accessibility, analytics, and conversion checks before it reaches everyone.

    A live fix with a clear rollback path, tested on real journeys before it scales.

    AI assist
    AI runs the functional, accessibility, analytics, and conversion regression checks against the release candidate before anyone opens it manually.
    Human gate
    Regression results go to our performance lead, who approves the rollout percentage and the rollback trigger before anything reaches everyone. Rollout approved with a named rollback owner.

  5. Watch what actually happened

    We compare lab traces immediately and field results over the following weeks, while checking that conversion did not decline during the observation period.

    A documented decision to keep, adjust, or roll back the change.

    AI assist
    AI compares the post-release field distribution against the pre-release baseline continuously, and flags the moment a percentile or the conversion metric drifts outside its normal band.
    Human gate
    The keep, adjust, or roll back call belongs to our performance lead, who writes down the reasoning so nobody has to reconstruct it later. Keep, adjust, or rollback decision recorded with reasoning.

AI does the measuring and the re-running; a person decides what ships and what comes back out.

AI cross-references traffic volume against existing field-data coverage to shortlist the templates that carry real-user risk, reruns the same journey across the device, network, and cache permutations needed to isolate server, rendering, and third-party cost, clusters repeated failure signatures back to their raw evidence, runs the functional, accessibility, analytics, and conversion regression checks before anyone opens the release candidate manually, and compares the post-release field distribution against the baseline continuously. It does not decide. We do not sell a single synthetic score as your users' experience, we do not strip functional, consent, analytics, or accessibility behavior to move a number, and we do not promise a pass-by date when field traffic, platform ownership, or a third party is outside our control.

Deliverables and acceptance

What you get

Things you can act on. Nobody needs another slide deck about performance.

  • Brief

    Performance baseline

    Accepted when

    Ties your field data to one owner, one scope, and one exclusion list, so the definition of slow remains clear six weeks later.

  • Decision matrix

    Bottleneck evidence

    Accepted when

    Every finding traces back to a real lab run or field sample, with anything shaky flagged instead of quietly rounded up.

  • Prioritized backlog

    Prioritized fix backlog

    Accepted when

    Each item has an owner, a test that proves it's done, and a condition for pulling it back out.

  • Audit report

    Release validation report

    Accepted when

    Says what actually changed in the field and the lab, whether conversion held, and what we do next.

We call it done when: The baseline, bottleneck evidence, fix backlog, and validation report are done when the baseline names one owner, one scope, and one exclusion list, every finding traces to a real lab run or field sample with anything shaky flagged, every backlog item has an owner, a test that proves it, and a pull-back condition, and the report states what moved in the field and the lab, whether conversion held, and what happens next.

Fit and readiness

When you need this

Good enough for a lab test isn't good enough for a real visitor.

A good fit when

  • You want one team clearly responsible for real-user speed across your busiest templates.
  • You need proof that lab results and real visitor experience actually line up, and a clear read on where they don't.
  • You don't have a real performance baseline yet, or nobody agrees on what counts as fixed.

Better handled as other work when

  • You just want a single lab number, without splitting it by template, device, geography, or traffic.
  • You want speed fixes that skip checking checkout, forms, accessibility, and analytics before they ship.

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

We call it done when: You end up with a real baseline, an evidence pack behind every fix, a backlog engineers can genuinely execute, and a report on what happened after launch. Somebody is named for the next decision.

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

Videoby Mehmet AktuğAMP ile Daha Hızlı ve Daha Görünür Mobil Siteler Yaratmak - Mehmet AktuğArticleby Sena ÖnderWordpress Page Speed Plugins

Tools we use

Tools behind this work

  • PageSpeed Insights

    field and lab readings for representative slow templates

  • WebPageTest

    repeatable waterfalls, filmstrips, and simulated third-party failure conditions

  • GTmetrix

    request-level comparisons before and after each bounded fix

  • Pingdom

    synthetic alerts for uptime and recurring speed regressions

  • Google Search Console

    template-group field trends across mobile and desktop users

  • Google Analytics

    conversion guardrails for checkout, forms, and protected journeys

Next step

Review your Core Web Vitals with us

Whatever field or lab data you already have is a fine starting point. We will document the current state and its owners, then find the first evidence worth chasing.
Look at your vitals together

Before we start

Questions we hear

Agents compare traces, cluster repeated bottlenecks, and link every candidate to raw evidence. This covers the pattern matching that takes hours by hand. What they can't do is decide what's true, set priority, or ship anything. That stays with a Zeo specialist and your team.