Services preparing for campaign or event traffic
A load model built from expected traffic and conversion ratios shows the breaking point before the event runs.
Performance Testing
A performance testing service that validates load, endurance, failure, and recovery against realistic usage patterns.
- Software QA
- Assessment
- Managed delivery
- Automation
01Overview
Service overview
Performance Testing turns real usage patterns into a load model and finds where the system stops holding under stress. A traffic peak, a long run, and a dependency going down are each reproduced, and every bottleneck is named with its likely cause and placed in a remediation order, so the choice between adding capacity and changing code rests on measurement.
02Who it fits
Where it fits
Systems with untested recovery and capacity assumptions
Database, cache, and external dependencies are cut one at a time to see whether the recovery procedure really works.
Teams that need evidence for performance bottlenecks
Segment response times and resource usage are collected together so the cause is narrowed by measurement, not guesswork.
03What we deliver
Scope of delivery
01
Load, stress, and endurance testing
Normal load, breaking load, and long continuous runs are separated because each answers a different question. Load is generated with JMeter, and the tool follows what the target system requires. Beyond the momentary peak, the resource leaks that only surface after days are visible too.
02
Failure injection and recovery validation
Dependency latency, dependency outage, and instance termination are induced on purpose to watch how recovery behaves. Whether the written procedure holds in the real environment is confirmed in advance.
03
Capacity and bottleneck analysis
The collected metrics are broken down by segment to name the resource that saturates first. Scaling and code work can then be compared with evidence when the budget is decided.
How the service works
From production traffic to improvement priorities
Model the request mix, investigate bottlenecks and retest the effect of each improvement.
Model production workloads
Investigate bottlenecks and causes
Retest before and after improvements
04Why this way
Points of difference
Each point lists the deliverable that backs it.
Workload models based on production traffic
We combine virtual user counts with real request patterns to model production load. Production logs reveal how traffic varies by time of day, how calls are distributed across features, and the share of demanding requests such as search and cart operations. Testing against these patterns gives your team measured limits for capacity and operations planning.
Backed by
- Risk map and validation priorities
- Test strategy and acceptance criteria
Client work · Artist platform
Artist platform, company A — reverse-engineering undocumented code, then verifying all of it
We reverse-engineered the code to recover the requirements, then verified every function against what we recovered.
Learn moreAnalysis linked to remediation priorities
We turn performance test results into improvement tasks for the next sprint. Each bottleneck comes with a likely cause, a method to verify it, and an estimate of the work involved so priorities are clear. Tasks that need more capacity are separated from those that need code changes, ready for capacity and development planning.
Backed by
- Execution report and defect log
- Reusable test assets and a handover guide
Client work · Travel product management
Travel product management, company M — collector load and data consistency
We put load on the collector to find where it gives way, and checked consistency between the database and what the collector returned.
Learn more05How to start
From first call to kickoff
Every stage ends with an agreed scope and a deliverable in your hands.
Step 01
Assess risk
We review product flows and the release calendar together and name the highest-impact failure points within five business days of kickoff.
DeliverableRisk map and validation priorities
Step 02
Design evidence
We link requirements, tests, defects, and the release decision in one traceability sheet and fix the acceptance criteria within two weeks of delivery starts.
DeliverableTest strategy and acceptance criteria
Step 03
Execute and improve
We run validation against the agreed criteria and report defects and residual risk weekly in one fixed format.
DeliverableExecution report and defect log
Step 04
Hand over and embed
We hand test assets and working practices to the internal team and stay available for questions for four weeks afterwards.
DeliverableReusable test assets and a handover guide
06Contract options
Ways to work together
- Remote delivery
07Service levels
Service levels
5business days
Risk map fixed after kickoff
Weekly
Defect and residual-risk report
4weeks
Questions answered after handover
Global distributed storage, company K — measuring load and the breaking point
The distributed storage product needed a measured answer to how far it holds.
What we doReal usage patterns became a load model, normal and breaking ranges were measured separately, and the resource that gives way first was named.
What it leavesWhat remained was a remediation list split between capacity and code, plus capacity guidance operations could use.
Artist platform, company A — reverse-engineering undocumented code, then verifying all of it
The development work we inherited had no documentation, only code.
What we doWe reverse-engineered the code to recover the requirements, then verified every function against what we recovered.
What it leavesWhat the product does, and how, went into documentation, and the point top-up and management feature was built on top of it.
Travel product management, company M — collector load and data consistency
About 1,900 travel products were being maintained by hand, one spreadsheet row at a time.
What we doWe put load on the collector to find where it gives way, and checked consistency between the database and what the collector returned.
What it leavesThe failures we found were in the data, not the features, and product-change handling now runs on its own on top of that.
Global test tool, company T — Japanese localization QA for a no-code test tool
A no-code test automation tool was heading for its Japanese launch and needed a team to own the localization verification.
What we doWe built and ran some 1,000 Japanese verification cases, hunting the places where wording and behavior diverge.
What it leavesSome 150 localization defects were caught before launch over seven months, and the verification cases remain as an asset for the next version.
Mobile app, company R — pre-release app security and server verification
An iOS and Android app heading for release needed its security checked from a position independent of the builders.
What we doWe verified the app and its servers together against some 70 security check items, reporting defects with severity and reproduction steps.
What it leavesSome 20 defects were found across the app and servers, and every one was confirmed fixed before release.
Ongoing
So that no file has to be edited every time a product changes: automating travel product management
So that people no longer chase every product change. We connected collection, storage, display, and ad integration, and checked both whether the data was right and where processing reaches its limit.
09FAQ
Frequently asked questions
Where possible we use a separate environment built to the same specification. If production is unavoidable, we agree the scope, the abort threshold, and who can stop the run first, then start at limited load during the quietest hours.
We divide ownership first: what the in-house team keeps and what we take over is written down, while defect severity criteria and the reporting format stay unified so the release decision rests on one set of evidence.
Yes. We weigh run frequency against maintenance cost and automate the highest-priority journeys first, replacing volatile screen areas with API-level checks or leaving them to manual validation.
You receive the deliverable listed for each stage exactly as it is. Reports come as a document plus the underlying data, and test cases and automation scripts are handed over together with repository access.
Remote delivery is the default, and on-site work is also available. If the test environment is accessible only from your internal network, we first define account access and data-export restrictions, then agree on on-site staffing and duration.
We report defects and remaining risks weekly in a consistent format. Each defect includes severity and reproduction conditions, alongside release readiness, so approvers can decide from the same evidence.
We share the findings from the first stage, then agree scope and schedule on that basis before delivery. A finished request for proposal is not required: the current environment and the outcome you want are enough to start.
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12Related resources
Resources for this service
Guides and checklists you can use during review and kickoff.
See all resources
Guides
2026.06.24
Pre-release QA review — a guide for buyers
What to weigh when a release date is set and you are deciding whether to bring in an outside review — timing, inputs, how scope is cut, and how long it takes.
Contact
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