Apps that stop
making users wait
Slow startup times, janky scrolling, and memory spikes are engineering problems with specific causes. Identifying them takes the right tools and someone who knows where to look.
See what we address
Honest about the effort involved
Performance work is not a one-afternoon fix. Diagnosing why an app stutters at frame 47 of a scroll list, or why a background fetch blocks the main thread under specific network conditions, takes methodical profiling across real devices and real usage patterns.
Source code, build configuration, and ideally production crash logs. Without visibility into the actual codebase, findings stay surface-level.
A thorough audit takes days, not hours. Implementing fixes and verifying them against regression takes additional time.
Performance problems often appear only on older hardware or under memory pressure. Testing on a flagship alone misses the majority of affected users.
You know where users drop off and what the support queue says. That context shapes where the investigation starts.
A food delivery app losing users at checkout
The team noticed checkout abandonment spiking on Android devices below 3GB RAM. Profiling revealed the image decoding pipeline was running synchronously on the main thread — a decision made during early development that went unnoticed until scale exposed it.
Moving decode operations to a background executor and introducing a lightweight memory cache reduced the checkout screen render time from 1.8 seconds to under 400ms on the affected device class. Abandonment rates on those devices dropped measurably within the first release cycle.
Confirm the issue on target hardware under real conditions
Capture traces with Perfetto, Instruments, or platform tools
Narrow from symptoms to a specific call site or data path
Apply targeted change, never broad rewrites without cause
Measure against baseline, check for regressions in adjacent flows
Bridge overhead, JS thread saturation, and native module timing
Shader compilation jank, raster thread stalls, and widget rebuild depth
AutoLayout pass cost, Core Data fetch strategies, and background task budgets
RecyclerView prefetch tuning, Doze mode behavior, and ANR root causes
The method changes. The discipline does not.
Each platform exposes performance differently. Instruments on iOS shows you CPU sampling and memory allocations. Android Studio Profiler gives you heap dumps and network timing. Perfetto traces reveal what both hide. Knowing which tool answers which question is most of the work.
Cross-platform frameworks add their own layer of complexity — a Flutter app's jank might live in Dart, in the raster thread, or in a platform channel call that blocks longer than expected. The investigation follows the evidence, not a template.
Situations that bring people to this kind of work
Most clients arrive after something specific happened — a bad App Store review thread, a failed launch on a new device family, or an internal escalation after analytics revealed a drop in session length.
Rarely does someone seek performance work proactively. Usually there is a concrete trigger and a deadline attached to it.
Taras Kovalenko
Lead Dev, Fintech"Our app passed QA fine but crashed on mid-range devices in the field. We needed someone who could read the traces, not just run the linter."
Olena Marchuk
Product, Logistics"Drivers were complaining the app was slow on their company phones. It took an outside pair of eyes to find the database query running on every screen transition."
What this kind of work actually covers
These are not service categories — they are the specific technical situations that come up repeatedly and require focused investigation rather than general advice.
Cold start taking longer than 2 seconds
Initialization sequences that block the first frame — SDKs initializing eagerly, heavy work on the main thread before the first layout pass completes.
Heap growing across sessions without release
Retained references, bitmap caches without eviction policy, or listeners never detached from lifecycle — each session costs more memory than the last.
Scroll lists that drop frames on specific content
Cell reuse misconfigured, image decoding on the main thread, or layout passes triggered by data changes that touch the entire visible range simultaneously.
Requests that chain when they could run in parallel
Sequential fetches where the second request depends on data the first could have included — adding 400ms to 800ms of unnecessary wait on every screen load.
Background work waking the device too frequently
Polling intervals set too short, location updates more precise than the feature requires, or background tasks that reschedule themselves on failure without backoff.
OOM terminations on devices with 2GB or less
The app runs fine on development hardware but gets terminated silently on devices that represent a significant share of the actual user base.