A product that only gets faster
We continuously monitor and optimize your application's performance, catching regressions early and keeping load times fast as your product grows.
Overview
Performance tends to degrade gradually rather than all at once, which makes it easy to miss from the inside. A query that ran fine against a thousand rows starts taking noticeably longer at a million. A page that loaded instantly during development picks up a few extra scripts and images over successive updates until it's meaningfully slower without anyone deciding it should be. Users rarely file a support ticket about slowness, they just quietly leave and don't come back, which means the cost of unaddressed performance issues is largely invisible unless someone's actually measuring it.
We treat performance as something to monitor continuously rather than something to fix reactively after a complaint or a sudden traffic spike exposes it. Real-user monitoring tracks how your application actually performs for real visitors on real connections and devices, not just synthetic benchmarks run under ideal lab conditions that may not reflect what your actual users experience. Combined with backend monitoring, this gives a complete picture: where time is actually being spent, whether that's slow database queries, unoptimized image delivery, or backend response times that creep up under real production load.
Optimization work is prioritized by actual impact, not by what's technically interesting to fix. A slow query on a rarely-used admin report matters less than shaving meaningful time off your product page load, and we focus effort accordingly, since performance work that doesn't move a metric that actually matters to your users or business isn't worth the engineering time it consumes. The goal throughout is a site or application that stays fast as it grows and evolves, not one that's fast at launch and then quietly slows down as complexity accumulates.
What we do
Ongoing performance care that keeps your application fast, even as usage and data grow.
Real-User & Synthetic Performance Monitoring
We track performance from two complementary angles: real-user monitoring, which captures how your application actually performs for genuine visitors across their real devices, connections, and geographic locations, and synthetic monitoring, which runs consistent, controlled tests to catch regressions immediately after a deployment before real users are affected at scale. Real-user data reveals things synthetic testing alone tends to miss, like a specific page performing poorly for mobile users on slower connections even though it tests fine on a fast office wifi network during development. Synthetic monitoring, meanwhile, gives you an early warning system, catching a performance regression within minutes of a deploy rather than waiting for real-user data to accumulate enough volume to reveal the same problem, by which point more users have already had a degraded experience. Together, these two approaches close the gap between how performance looks in a controlled test environment and how it actually feels for the people using your product, which is ultimately the only measurement that matters for retention and conversion.
Database & Backend Query Optimization
Slow performance often traces back to inefficient database queries or backend logic that worked fine at a smaller scale but started struggling as your data volume or traffic grew. We identify the specific queries and backend processes actually consuming the most time under real production load, using query profiling and backend performance monitoring rather than guessing based on which code looks complicated. Common culprits include missing database indexes, queries fetching far more data than a given view actually needs, N+1 query patterns where a single page load triggers dozens of individual database calls instead of one efficient batch, and backend logic that recalculates something on every request rather than caching a result that rarely changes. Fixes are implemented and then measured against the same production-representative conditions that revealed the original problem, confirming the optimization actually delivers a measurable improvement rather than just looking better in theory. This is deliberately not a one-time cleanup; as your application's data volume and usage patterns continue evolving, new bottlenecks emerge, and we monitor for them on an ongoing basis rather than assuming a fix made once stays sufficient indefinitely.
Frontend Load Time & Asset Optimization
How fast a page feels to load depends heavily on frontend factors: image sizes and formats, how many scripts block rendering, whether critical content appears quickly even if the full page takes longer to finish loading, and how efficiently assets are cached and delivered. We audit these factors specifically, identifying oversized images that could be compressed or served in more efficient formats without visible quality loss, scripts that could be deferred or loaded asynchronously without breaking functionality, and opportunities to improve perceived load speed even where the total load time can't be dramatically reduced. This work is measured against real performance metrics that correlate with actual user experience and search ranking factors, not just an abstract sense that the site 'feels faster.' Because frontend performance tends to degrade gradually as new features, tracking scripts, and third-party integrations get added over time, this is maintained as an ongoing practice rather than a single audit performed once and never revisited, since the same discipline that got a page fast initially is what keeps it fast as the page continues to evolve.
Our Process
- 01
Baseline Performance Audit
We start by measuring your application's current performance across key pages and flows, using both real-user data where available and controlled synthetic testing to establish an accurate baseline. This audit identifies where time is actually being spent, whether that's backend response times, database query performance, or frontend rendering and asset delivery, rather than assuming where the bottleneck likely is.
- 02
Impact-Based Prioritization
Not every performance issue deserves equal attention, so we prioritize based on actual business impact: which pages see the most traffic, which flows are most tied to conversion or retention, and where the current performance gap is largest relative to what's achievable. This ensures optimization effort goes toward changes that will actually move metrics that matter, rather than technically satisfying fixes with limited real-world impact.
- 03
Targeted Optimization Implementation
We implement specific fixes for the highest-priority issues identified, whether that's adding database indexes, restructuring inefficient queries, optimizing image delivery, or deferring non-critical scripts. Each change is implemented individually where possible, so its actual impact can be measured cleanly rather than attributing improvement to a bundle of simultaneous changes.
- 04
Verification Against Production Conditions
After implementation, we measure the actual impact using the same monitoring tools that identified the original issue, confirming a genuine, measurable improvement under realistic conditions rather than just a theoretical gain. If a fix doesn't deliver the expected improvement, we investigate further rather than assuming the work is done simply because it was completed.
- 05
Ongoing Monitoring & Regression Detection
Performance monitoring continues after the initial optimization work, since new features, growing data volume, and added third-party scripts can gradually reintroduce the same kinds of issues over time. Synthetic monitoring in particular catches regressions immediately after deployments, so a performance-degrading change gets caught and addressed quickly rather than slowly eroding user experience unnoticed.
Performance monitoring technology stack
We use industry-leading performance monitoring platforms to keep applications fast.
Frequently Asked Questions
We use a combination of real-user monitoring, which captures actual visitor experience across real devices and connections, and synthetic testing under controlled conditions to catch regressions immediately after changes. This combination reveals both how performance genuinely feels to real users and gives an early warning system for anything that degrades after a new deployment.
It varies, but common culprits include inefficient database queries that worked fine at smaller data volumes, oversized or poorly formatted images, render-blocking scripts, and backend logic recalculating things that could be cached instead. We identify the specific cause through profiling rather than guessing, since the actual bottleneck often isn't what it initially appears to be.
Page speed is a known factor in search ranking, and slow load times are strongly correlated with higher bounce rates and lower conversion, particularly on mobile connections. While we can't promise a specific ranking or conversion improvement, meaningfully faster load times generally support both, which is part of why we prioritize optimization work by actual business impact.
Performance tends to degrade gradually as new features, data volume, and third-party scripts accumulate over time, so we treat this as ongoing monitoring rather than a single audit. Regular monitoring catches new bottlenecks as they emerge, which is generally far cheaper than letting performance degrade significantly before addressing it all at once.
Yes, we start with a full baseline audit regardless of who built the original application, since understanding exactly where time is currently being spent is necessary before any optimization work can be properly prioritized. This applies the same whether we're extending an application we built or taking over performance work on something built elsewhere.
We prioritize by actual impact, focusing on the issues affecting your highest-traffic pages or most conversion-critical flows first. A minor inefficiency in a rarely-used admin page gets much lower priority than a slow-loading product page, since engineering time is limited and should go toward changes that genuinely move metrics that matter to your business.
Every fix is measured against the same monitoring data that identified the original problem, under realistic production-representative conditions, not just a quick local test. If the measured improvement doesn't match expectations, we investigate further rather than considering the work complete just because a change was technically implemented.
Ongoing monitoring, particularly synthetic testing tied to deployments, is designed to catch this quickly. If a new feature or third-party integration introduces a performance regression, we typically identify it within days rather than months, since the monitoring infrastructure stays in place after the initial optimization work rather than being removed once the immediate issues are resolved.
Other Maintenance & Support Services
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