3 marks · shot before, shot after
The mark stays in the wall. Those aren't results. They're the line the next change gets measured against.
Slow paths found and tuned before users feel them
We measure where the product actually spends its time — Core Web Vitals in the browser, query time and memory on the server — then optimize the specific bottleneck: the N+1 query, the render-blocking asset, the leak that grows under load, so the fix targets the real constraint, not a guess.
Maintenance, Support & Operations
Optimization that fixes the bottleneck, not the guess
Performance work goes wrong when it optimizes what feels slow instead of what is slow. This service is measurement first: profiling the frontend against Core Web Vitals and the backend against query time, memory, and CPU, so we can name the actual bottleneck — a slow database query, a bloated bundle, a leak that surfaces under traffic — and tune that. The goal is a faster product for real users, verified by the same metrics that found the problem.
What you get out of it
The real bottleneck, not a hunch
Profiling shows where time and resources actually go, so effort targets the slow query or heavy asset that matters instead of a plausible-looking guess.
Speed your users notice
Core Web Vitals and load-time improvements land where perception lives — first paint, interactivity, layout stability — so the product feels faster, not just benchmarks better.
Headroom under load
Resource tuning and leak fixes mean the product holds its speed as traffic climbs, rather than degrading right when usage matters most.
What we do
Where the instrument gets pointed
Frontend performance
Core Web Vitals profiling and fixes for the render-blocking assets, oversized bundles, and layout shifts that slow the experience your users feel.
- Core Web Vitals
- Bundle & asset optimization
- Render-path tuning
Backend & query tuning
Profiling server response time to find slow queries, N+1 patterns, and missing indexes, then optimizing the specific hot path.
- Query profiling
- Index & N+1 fixes
- Caching strategy
Resource & memory analysis
Tracking CPU and memory under real conditions to catch leaks and inefficiencies that only surface once load climbs.
- Memory leak checks
- CPU profiling
- Load-condition testing
Performance baselines
Establishing measurable baselines so an optimization is verified against numbers and future regressions are caught against a known-good line.
- Metric baselines
- Before/after checks
- Regression watch
How it goes
From guessing to a line in the wall
Day 0
Slow, and nobody's sure why
A guess-and-check cycle tries plausible fixes that don't move the number, because nothing was actually profiled.
Week 1
Profiled front to back
Core Web Vitals in the browser and query time, memory, and CPU on the server, measured against real traffic.
Week 2
The real bottleneck named
The specific slow query, oversized bundle, or leak, identified by profiling and named plainly.
Week 3
Tuned and baselined
The fix verified against the same metrics that found it, then recorded as a baseline for future regressions.
The survey, in three passes
Audit & Profile
We review hosting, dependencies, queries, and assets to find where time and resources actually go, which is often somewhere other than where they seem to.
- Frontend Core Web Vitals profiling
- Backend query & resource profiling
- Technical debt affecting speed named
Monitor & Alert
Ongoing tracking of performance and resource health, so a regression surfaces as an alert instead of a customer complaint.
- Continuous performance tracking
- Resource & memory monitoring
- Early alerts before users notice
Tune & Baseline
The identified bottleneck gets fixed and verified, then the improved numbers become the baseline future changes are measured against.
- Targeted fixes, not guesses
- Before/after verification
- Recorded baseline for regression checks
How we work
01
Measure first
Profile the frontend and backend against real metrics so the bottleneck is identified by data, not by which part feels slow.
02
Find the constraint
Isolate the specific slow query, heavy asset, or leak that's actually limiting performance for your users.
03
Tune the hot path
Optimize the identified constraint and verify the gain against the same metrics that surfaced it.
04
Set the baseline
Record the improved numbers as a baseline so future regressions are caught against a known-good line.
Why it pays
Fixes the real bottleneck
Profiling finds the specific slow query or heavy asset, so effort doesn't get spent on a plausible-looking hunch.
Speed users actually notice
Improvements land in first paint and interactivity, where perception lives, as well as in the benchmark number.
Headroom under real load
Resource tuning means the product holds its speed as traffic climbs, right when it matters most.
A baseline, not a one-time fix
Recorded metrics mean a future regression gets caught early instead of discovered by frustrated users.
Verified, not assumed
Every optimization is checked against the same metrics that found the problem, so the gain is proven.
Built for growth
Performance work accounts for where traffic is headed as well as where it is right now.
What you get
Performance profile
A measured breakdown of where the product spends time and resources across frontend and backend.
Optimization set
The specific tuning changes applied to the identified bottlenecks, with before-and-after numbers.
Performance baseline
Recorded metrics that define the improved state and anchor future regression checks.
Industry expertise
E-commerce & retail
Checkout and catalog performance tuned where slow load directly costs conversions.
SaaS & B2B platforms
Dashboard and query performance kept fast as account data and usage scale.
Media & content platforms
Page load and Core Web Vitals tuned where speed affects both users and search visibility.
High-traffic consumer apps
Resource and memory profiling for products that need to hold speed under real spikes.
Internal tools & operations
Backend query and resource tuning for tools your team depends on daily.
Data-heavy applications
Query and index tuning for products where the database is the real bottleneck.
Product feeling slower than it should?
Tell us where it drags — we'll profile the real bottleneck and tune the hot path with before-and-after numbers to prove it.
Why us for this
Measurement before any fix
We profile first, always. No optimization ships without data showing it targets the real constraint.
The specific bottleneck, named
You get the actual slow query or leaking process identified, well past a general 'it's probably the database.'
Verified against the same metrics
Every fix is checked against the numbers that found the problem, so the improvement is proven.
A baseline that catches regressions
Improved metrics become a known-good line, so future slowdowns are caught early, before users discover them.
Frontend and backend, together
Browser-side Core Web Vitals and server-side query and resource profiling are both part of the same engagement.
Built for the load that's coming
Tuning accounts for where traffic is headed, beyond where it sits on the day we profile.
Working with Flaidex
We don't optimize what feels slow
We profile what's actually slow, even when it isn't the part everyone assumed was the problem.
We hand over the baseline with the fix
You keep the recorded metrics, so your own team can catch future regressions without us.
We explain the bottleneck in plain terms
You understand what was actually slow and why the fix works, beyond the fact that the number improved.
We check under real conditions
Load and traffic patterns are part of the profiling, alongside the clean, quiet staging environment.
We stay honest about the ceiling
If the architecture itself is the real constraint, we say so and stop chasing diminishing tuning returns.
We can keep monitoring after the fix
Ongoing tracking is available so the gain holds as features accumulate.
Questions
Asked before the first shot
BM01How is performance monitoring different from uptime monitoring?
They answer different questions. Uptime monitoring asks whether the product is available and responding at all — is it up, is it reachable. Performance monitoring asks how fast it is when it is up: page load, query time, resource use. A product can be fully available and still painfully slow, and it's that slowness — the lag your users feel even when nothing is technically down — that this service measures and optimizes.
BM02Why measure before optimizing? Can't you just speed things up?
Because intuition about performance is unreliable, and optimizing the wrong thing wastes effort while the real bottleneck stays. Profiling shows where the product actually spends its time — often a single slow query or an oversized asset accounts for most of the lag. Measuring first means the fix targets the genuine constraint, and measuring after proves the change helped rather than just moving the slow part somewhere less obvious.
BM03What kinds of bottlenecks do you usually find?
The common ones cluster in a few places: on the frontend, render-blocking scripts, unoptimized images, and bloated JavaScript bundles that delay interactivity; on the backend, N+1 query patterns, missing database indexes, and expensive uncached operations on hot paths. Under load, memory leaks and CPU-bound work that stay invisible at low traffic. The specific culprit varies, which is exactly why we profile instead of assuming.
BM04Will speeding up the product require a rebuild?
Rarely. Most meaningful performance gains come from targeted changes — adding an index, caching a repeated query, deferring a script, compressing assets — not from rewriting the application. We optimize the specific hot paths the profiling identifies, which keeps the work surgical. A rebuild only enters the conversation if the architecture itself is the constraint, and even then we'd name that explicitly rather than assume it.
BM05How do you keep performance from slowly degrading again?
By recording a baseline. Once the optimizations land, we capture the improved metrics as a known-good line, so a future change that reintroduces slowness can be caught against it rather than noticed months later by frustrated users. Performance tends to erode quietly as features accumulate, and a baseline turns that slow drift into something measurable and catchable.
Let's talk
Running a large platform, shaping a first MVP, or getting a product ready for a funding round? Tell us where you are. We'll shape the process around it, and stay with you after launch.
