Performance
Optimization

We collect field data — Core Web Vitals as experienced by actual visitors — and break it down by page template, device class, country and connection type, then reconcile it with analytics and Search Console. This is the step most often skipped, and skipping it is why teams spend weeks optimising a page that was already fast for the people who mattered.

Once we know which templates hurt, we reproduce the problem under throttled CPU and network conditions that resemble a mid-range Android phone rather than a developer laptop. Waterfall analysis, long-task traces and server timing tell us how much of the delay is network, how much is main-thread work and how much happened before a single byte was sent.

If the server takes a second to respond, no amount of image compression will rescue the page, so we look upstream first: response time, rendering strategy, cache hit rate and the queries behind the page. On our own platform, replacing full-table reads with narrow projections, batching inserts and moving per-row round-trips into aggregate SQL cut both response time and data transfer substantially.

Next we attack what the browser must download and execute: JavaScript that ships but is never used, hydration that blocks interaction, fonts that delay text, and images that are larger than the box they land in. Above-the-fold content should be visible from the server response, not appear only after a client bundle finishes — a failure mode we have found and fixed in production.

We define what can be cached at the CDN edge, what belongs in a server or application cache, what can be statically regenerated, and — the part that gets skipped — exactly how each layer is invalidated when content changes. Caching without a deliberate invalidation strategy produces the second-worst outcome in web engineering: fast pages showing wrong data.

After deployment we wait for field data to catch up, because real-user metrics move over weeks rather than minutes, then compare against the baseline and report what changed and what did not. We leave behind performance budgets and monitoring so the next regression is caught in a deploy rather than in a ranking drop three months later.