“it just sometimes doesn't work”
Bugs traced to the cause, not patched at the symptom
We diagnose what's actually broken rather than muffling the symptom: reproducing the failure from logs and reports, isolating the root cause, and shipping a fix guarded by a regression test so the same bug doesn't quietly return two releases later.
The chart
4 findings · in order
Line one is the one everybody skips. A bug you can't reproduce is a bug you can't reliably fix. All you can do is change something and wait to see whether the reports stop, which has never been the same thing.
Maintenance, Support & Operations
Troubleshooting that finds the cause and proves the fix
A bug you can't reproduce is a bug you can't reliably fix. This service is disciplined diagnosis: turning a vague report or a spike in the error logs into a reproducible failure, tracing it to the root cause instead of the nearest symptom, and closing it with a fix that a regression test locks in — so the repair holds and the same defect doesn't resurface under a new ticket.
What you get out of it
Fixes that actually hold
We trace a bug to its root cause and lock the fix in with a regression test, so the same defect doesn't quietly reappear a few releases later.
The hard-to-reproduce ones, caught
Failures that only show in production get reproduced from logs and real conditions, so the intermittent bug that hides in demos finally gets pinned down.
Clarity on what broke and why
Each fix comes with the diagnosis — what failed, why, and what changed — so your team learns the cause instead of just seeing a green checkmark.
What we do
Reproduction & diagnosis
Turning a report or an error spike into a reproducible failure, then isolating the root cause from logs, stack traces, and the code path involved.
- Log analysis
- Reproduction steps
- Root-cause isolation
Integration troubleshooting
Untangling failures at the seams — third-party APIs, webhooks, data mismatches — where the bug lives between systems rather than inside one.
- API failures
- Webhook debugging
- Data mismatch
Production-only defects
Diagnosing behavior that appears only under real load, real data, or real configuration, where a local environment stays stubbornly green.
- Load-condition bugs
- Config-specific issues
- Race conditions
Fix & regression guard
Shipping the repair with a regression test that captures the failure, so the same bug can't slip back in unnoticed.
- Regression test
- Verified fix
- Diagnosis notes
How it goes
From “sometimes” to on demand
Day 1
A report nobody could reproduce
A vague ticket or an error-log spike arrives, with no reliable way yet to trigger the failure on demand.
Day 2
The failure gets reproduced
Logs, reported steps, and real production conditions are reconstructed until the bug can be triggered reliably.
Day 4
The root cause is isolated
The failure is traced through the actual code path to its real cause, past the nearest plausible symptom.
Day 6
Fixed and guarded
The repair ships with a regression test that captures the failure, so the same defect can't quietly return.
Three kinds of hard
Reproduce & Diagnose
A report or an error-log spike gets turned into a failure we can trigger reliably, then traced through logs and stack traces to the actual root cause.
- Log analysis
- Reproduction steps
- Root-cause isolation
Integration Failures
Bugs that live at the seams between systems (a third-party API, a webhook, a data mismatch) get untangled where the failure actually happens.
- API failure diagnosis
- Webhook debugging
- Data mismatch tracing
Production-Only Bugs
Behavior that only appears under real load, real data, or real configuration gets reconstructed from production conditions. Nobody guesses at it locally.
- Load-condition bugs
- Config-specific issues
- Race conditions
How we work
Reproduce
Turn the report or the error logs into a failure we can trigger reliably, because a bug we can't reproduce is one we can't confirm fixed.
Isolate the cause
Trace the failure through logs, stack traces, and the code path to the actual root cause, not the nearest symptom.
Fix and guard
Ship the repair and capture the failure in a regression test so the same defect can't return unnoticed.
Verify in context
Confirm the fix holds under the conditions that triggered it, then document what broke and why for your team.
Why it pays
Never fixed blind
A bug gets reproduced before it's called fixed, so the repair is verified against a real trigger.
Root cause, not the nearest symptom
The fix targets what actually broke, so patched-over bugs stop resurfacing under a new ticket number.
A regression test locks it down
The same defect can't silently return, because a test now fails the moment it does.
Production-only bugs get found
The intermittent failure that never shows up in a demo gets reconstructed from real logs and conditions.
Unfamiliar codebases, no problem
Diagnosis works from the failing code path and logs. We don't need a full tour of a system we didn't build.
Clarity your team can learn from
Every fix ships with a written diagnosis, so the cause is understood as well as checked off.
Selected work
What you get
Root-cause diagnosis
A written account of what failed, why, and the code path or integration responsible.
Verified fix
The repair itself, confirmed against the conditions that originally triggered the failure.
Regression test
A test that captures the bug, so it fails if the same defect is ever reintroduced.
Industry expertise
Fintech & payments platforms · Intermittent billing and webhook bugs get reproduced from production logs instead of being shrugged off as one-offs.
E-commerce & retail · Checkout and cart failures under real traffic get root-caused before they cost another sale.
Healthcare software · Patient-facing defects get traced to the actual cause, with a regression test proving the fix holds.
Travel & booking platforms · Booking-flow bugs that only appear under real load get reconstructed and fixed at the source.
B2B SaaS platforms · Integration failures at the API and webhook seams get untangled without needing a full system rewrite.
Teams inheriting unfamiliar code · Diagnosis starts from the logs and the failing path. Having built the system isn't a requirement.
Chasing a bug that keeps coming back?
Send us the symptoms and the logs — we'll reproduce it, trace the root cause, and ship a fix that a regression test locks down.
Why us for this
We reproduce before we touch code
A fix we can't verify against a real trigger isn't a fix we'll ship. Reproduction comes first, always.
We trace to the actual cause
The nearest plausible symptom isn't good enough. We follow the failure to where it genuinely originates.
Every fix ships with a regression test
The bug is locked out for good, so it can't quietly resurface under a new ticket.
We specialize in the hard-to-reproduce ones
Production-only defects that hide in every demo are exactly the diagnostic work we're built for.
We work in codebases we didn't write
Most of our troubleshooting starts cold, orienting from logs and the failing path.
We document what broke and why
Your team gets the full diagnosis along with the green checkmark, so the cause is understood going forward.
Working with Flaidex
We won't patch a symptom and call it done
If the real cause isn't found yet, the ticket stays open. A fix that doesn't hold isn't worth shipping.
We prioritize by impact, honestly
A full outage gets triaged ahead of a cosmetic glitch, and we're upfront about that ordering.
We reconstruct production instead of guessing
Real logs and real conditions drive the diagnosis for bugs that never show up locally.
We leave a regression test behind
The fix comes with proof it can't silently return, so you aren't relying on our word that it's handled.
We explain the fix as well as apply it
Your team walks away understanding the cause, which is what actually prevents the next one.
We're honest about what needs more time
If a bug needs a deeper structural fix instead of a quick patch, we'll say so plainly.
Questions
Asked before we take the ticket
What happens when something breaks in production at an odd hour?
The honest answer is process, not a promise of instant coverage. When a report or alert reaches us, we prioritize by impact — a full outage is triaged ahead of a cosmetic glitch — and follow the escalation path we've agreed with you to reach the right person. What we commit to is a disciplined response: reproduce, diagnose, and fix in that order, with clear communication about where things stand, rather than a claim to be watching every second of every night.
Why do you insist on reproducing a bug before fixing it?
Because a fix you can't verify is a guess. If we can't trigger the failure ourselves, we can't be sure the change we made actually resolved it rather than just moving it somewhere less visible. Reproducing the bug — from logs, reported steps, or the production conditions that caused it — gives us a concrete failure to fix against and to confirm the fix has genuinely closed.
What about bugs that only happen in production and never locally?
Those are the ones we specifically dig into. Production-only defects usually come from something the local environment doesn't replicate: real data volume, actual load, a specific configuration, or timing between services. We work from production logs and traces to reconstruct those conditions, because the bug that stays hidden in every demo is exactly the one worth the diagnostic effort.
How do you stop a fixed bug from coming back?
We capture it in a regression test. Once the failure is reproduced and fixed, we write a test that fails if that specific defect is ever reintroduced, then it runs with the rest of the suite. That's the difference between patching a symptom and closing a bug: the test turns 'we fixed it' into 'it can't silently return,' which is what keeps a backlog of recurring issues from forming.
Can you fix bugs in a codebase your team didn't write?
Yes — most troubleshooting work is in code we're seeing for the first time. We orient by reading the logs and the failing code path rather than needing a full tour of the system, then trace the specific defect from there. Diagnosis is a skill in reading unfamiliar code under pressure, and it doesn't require having built the thing to find where it's going wrong.
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.
