A route-level performance baseline
Review available field Core Web Vitals separately from reproducible lab traces, grouped by representative template and device. Use TTFB and FCP as diagnostic timings where useful; they are not Core Web Vitals.
Core Web Vitals
Core Web Vitals improvements grounded in real page behavior
I connect each performance symptom to the browser and application work behind it, distinguish user-facing metrics from diagnostic timings, and prioritize changes that fit the product.
Discuss your projectA good fit when
AP®Diagnose loading, interaction, and visual stability across important routes using field and lab evidence where available. Trace bottlenecks through fonts, images, CSS, JavaScript bundles, third-party scripts, hydration, and server work.
- Key templates have poor or inconsistent field performance
- A redesign or feature release has slowed pages or introduced layout shifts
- Teams need help separating real-user signals from one-off lab results
- Performance work needs to fit alongside SEO, accessibility, and product constraints
What you get
AP®Root-cause engineering work
Trace LCP, INP, or CLS symptoms to likely causes such as server work, hydration and main-thread JavaScript, bundle and CSS cost, font/image loading, third-party scripts, or unstable layout. Implement agreed fixes or provide a prioritized engineering plan.
Comparable remeasurement
Repeat the same lab journeys and, when enough field data exists, monitor the relevant user groups after release. Record test conditions and remaining bottlenecks; do not treat one Lighthouse score as field performance.
How the work moves
AP®- 01
Select the important journeys
Agree on representative URLs, devices, user actions, and the field data that best reflects the experience.
- 02
Trace the bottlenecks
Use browser traces and code inspection to separate server response, rendering/hydration, resource priority, CSS/layout, fonts/images, bundles, and third-party work.
- 03
Improve and recheck
Make focused changes and compare under the same device, route, and lab conditions. Review field data only when it exists and has adequate coverage; document trade-offs and follow-on work.
Relevant capabilities
AP®LCP, INP, and CLS diagnosisTTFB and FCP as diagnostic timingsNext.js rendering, hydration, and cachingFont and image loadingJavaScript bundle and CSS costThird-party script reviewField and lab data separationTemplate-level optimizationComparable regression checksPerformance handover
Common questions
AP®Can you guarantee a particular score?
No. Scores vary by device, network, traffic, and test conditions. I focus on repeatable improvements to representative user journeys and field signals.
Do you work on real-user data?
Yes, when available. Field data helps establish how users experience the site; lab tests help isolate causes and compare changes.
Is this only for Next.js?
No. Next.js is a common context, but the same diagnosis and optimization approach applies to other web stacks.
Have a defined brief, or just a stubborn problem?