Our reсent Case Studies

Scaling Quality, Ending Chaos
Laying the QA Foundation
Unifying QA Across Apps
Stable, Fast Automation
Global Messenger QA
Agile QA for Insurtech
Legacy FinTech, Modern QA
QA for Due Diligence

From Chaos to Scalable Quality

Engagement Goal:

To build a scalable and sustainable foundation for quality — establishing structured QA processes, launching automation, and preparing the team for growth without compromising release stability.

Phases and activities delivered:

Phase 1: Audit & QA Strategy	2 weeks
Phase 2: Implementation & Process Integration	4 weeks
Phase 3: Automation Roadmap & Test Management Activation	6 weeks
Phase 4: Ongoing Support & Quality Scaling	7 weeks

What the Company gained:

Strategic Clarity
Process Standardization
Automation Kickoff
QA Leadership Enablement

30% off

Escaped Bugs Reduction

15% off

Fewer Hotfixes and Rollbacks

IN PROGRESS

“I need to ensure fast delivery without compromising quality — but I don’t have a system for quality ownership in my teams.”
Vp Rnd

From ad-hoc testing to a structured QA foundation

Engagement Goal:

To transform testing from a scattered, ad-hoc activity into a structured, owned, and trackable quality process — enabling Factify to support product growth and meet startup delivery speed without risking regressions or production issues.

Phases and activities delivered:

Phase 1: Audit & QA Strategy	2 weeks
Phase 2: Test Management Setup & Ownership Alignment	1.5 weeks
Phase 3: Automation Planning & Onboarding to Qase	2 weeks
Phase 4: Quality Coaching & Release Readiness Process	ongoing

What the Company gained:

CLEAR QA Strategy & Ownership
Centralized Test Management in Qase
Definition of Done for Releases
Startup-Ready Automation Roadmap
QA Mindset Shift Across the Team

EARLY RESULTS

Internal visibility into quality status on every build
Reduced testing-related delays in releases
1st step into automation with a sustainable plan
QA tasks became part of team planning and delivery

IN PROGRESS

Test automation implementation
Quality metrics tracking to begin in next sprint cycles.

“WE KNEW WE HAD QUALITY ISSUES BUT DIDN’T KNOW HOW TO STRUCTURE THE SOLUTION. NOW WE HAVE CLARITY, A ROADMAP, AND THE TOOLS TO BUILD QUALITY IN.” CTO

Revolutionizing QA across a multi-app ecosystem

Challenge:

The company had no automation framework. Frontend teams tried building one, but lacked QA expertise. The product suite was complex:

3 React Native apps (iOS & Android)
Web portals for hospitals
Internal back-office systems
Testing also had to support high-precision diagnostic algorithms, making reliability critical.

Solution:

Unified automation framework – one codebase for mobile, web, and API tests
Reusable infra (NPM package) – shared layer for standardization & easy updates
Modular product layers – isolated per app, but consistent in logic
Executive reporting – filtered by product line
Team enablement – onboarded devs, aligned QA best practices, broke knowledge silos

Impact

Manual regression effort ↓ 80%
QA cycle time ↓ 60%
CI feedback in 10 min / PR
Zero job queueing under load
Unified QA standards across 6 platforms

Advanced additions:

Diagnostic validation module – compares algorithm output to ground-truth datasets
CI on Kubernetes – dynamic pods spin up on demand
10-min feedback loops per PR – faster iteration, lower risk

Impact

Manual regression effort ↓ 80%
QA cycle time ↓ 60%
CI feedback in 10 min / PR
Zero job queueing under load
Unified QA standards across 6 platforms

Оptimizing automation stability & speed: data, websockets & smart infrastructure

Challenge:

The QA team had solid automation coverage and well-documented processes, but they were still dealing with test flakiness, slow environment setup, and maintenance overhead.

Flaky tests due to manual and slow test data setup
The existing framework wasn’t designed to properly simulate or validate WebSocket-based workflows
Frequent test breaks from UI locator changes
Painful cross-browser testing and infra maintenance

Solution:

Test Data Factory – reusable templates + seeded datasets → setup time ↓ 70% Async WebSocket Client – simulated message flows without UI → execution time ↓ 40% AI-based Self-Healing – auto-adapts to UI changes → locator failures ↓ 65%, maintenance ↓ 30% Custom Selenium Grid – Docker-based, auto-provisioned browsers → infra setup time ↓ 80%

Impact

Test execution time ↓ 45%
Maintenance workload ↓ 30%
Locator-related failures ↓ 65%
WebSocket testing now fast & stable
Binaries & browser management overhead ↓ 80%

From bottlenecks to breakthrough: Reinventing test automation for a global messaging platform

Challenge:

A global messaging platform’s in-house test automation became a blocker:

Fragile pipelines and siloed efforts slowed releases
Developers spent more time fixing infra than coding
Each test overbooked 3 devices, driving up costs

Solution:

1. Architecture Redesign

Migrated to a cost-optimized cloud device farm
Built a lightweight, modular automation framework (Kubernetes-native CI, plugin-based abstraction)

2. Efficiency Improvements

Introduced smart device allocation to cut infra waste
Integrated automation with Jira for streamlined test planning

3. Team Enablement. Efficiency Improvements

Hired and trained a new Automation Lead
Onboarded teams in days instead of weeks

Impact:

New test creation time ↓ >70%
Maintenance effort ↓ ~50%
Execution and analysis time ↓ ~75%
Device cost overhead ↓ ~65%
Onboarding time ↓ 80%

Outcome:

Full migration to cloud infra without cost spike
Restored developer productivity and test velocity
Built a scalable, sustainable QA foundation — in under 3 months

Impact:

New test creation time ↓ >70%
Maintenance effort ↓ ~50%
Execution and analysis time ↓ ~75%
Device cost overhead ↓ ~65%
Onboarding time ↓ 80%

Agent-Powered QA for a Lean Insurtech Startup

Challenge:

A stealth-stage insur-tech startup launched a digital-first platform with quoting, onboarding, identity verification, and payments.

The constraints:

Only one manual QA
Zero automated test coverage
Compliance-sensitive flows

Quality risks were growing – but the team couldn’t afford to slow down.

Solution:

Agent-Powered QA with Prompt Interface We deployed a headless browser QA agent controlled via natural-language prompts. The QA could test entire flows by simply describing them – no selectors, no code. The QA could test entire flows by simply describing them – no selectors, no code.

System Highlights:

Runs in staging, fully browser-driven
Prompt-based control
Auto-fills forms, validates UI, captures screenshots
Dashboard for monitoring & reporting

Smart Coverage Split:

Agent: routine UI, form validation, regressions
QA: edge cases, identity flows, legal & DB validations

80% of repetitive scenarios were delegated to the agent – freeing QA to focus on what matters.

IMPACT

Test coverage ↑ by 50%
(from 30% to 80%)
Code freeze time ↓ by 80%
(from 2 days to 4 hours)
Manual QA hours ↓ by 66%
(from 30/week to 10/week)

WHAT’S NEXT:

We’re integrating the QA agent into CI:

Tests triggered on PR merge/nightly builds
Fully headless in containers
Reports sent to Slack with screenshots

No Cypress. No scripts. Just results.

Bypassing the UI: fast, stable QA for a Legacy FinTech platform

Challenge:

A FinTech company with a 20-year-old legacy product faced major test automation pain:

Mixed tech stack: JSP legacy + React Native pages
Slow, fragile UI tests due to:
Missing/non-unique IDs
Fragile XPaths
Unreliable waits & long page loads
Modern automation worked for new UI – but choked on legacy

Refactoring the old codebase wasn’t an option – yet quality had to be guaranteed.

Solution:

Unconventional Approach: Bypassing UI with AJAX Validation Instead of pushing brittle UI automation, we built a custom module to intercept and validate AJAX responses directly – bypassing the flaky front-end. How it worked:

Validated core flows by parsing HTML from AJAX responses
No UI waits
Wrapped in a clean facade layer with clear functional methods for maintainability

Brought stability to a fragile legacy test suite
Avoided costly refactoring while keeping coverage high
Empowered the QA team to move fast with confidence

IMPACT:

Test execution time ↓ 83%
(from 2h 50m → 29 min)
Maintenance effort ↓ ~60%
(less brittle code, fewer false positives)
Time to delivery: solution shipped in just 25 days

Outcome:

Brought stability to a fragile legacy test suite
Avoided costly refactoring while keeping coverage high
Empowered the QA team to move fast with confidence

A smart workaround that delivered real value – fast.

IMPACT:

Test execution time ↓ 83%
(from 2h 50m → 29 min)
Maintenance effort ↓ ~60%
(less brittle code, fewer false positives)
Time to delivery: solution shipped in just 25 days

Preparation for Technical Due Diligence: Performance Testing for a Real-Time Influencer Commerce Platform

Challenge:

A real-time influencer commerce platform was preparing for launch in just 30 days. Investors demanded proof of scalability.

Key constraints:

Only 14 days to uncover critical bottlenecks
Live-streaming + chat + instant checkout required real-time, high-throughput validation
No existing performance test infrastructure

Solution:

Risk-based test scope – focused on high-impact user flows: onboarding, streaming, checkout
Hybrid load testing suite – k6 for REST + custom WebSocket injectors for real-time events
Autoscaled test infra – Kubernetes + spot nodes simulated 10,000+ users affordably
Live observability – real-time dashboards in Grafana with latency/error/throughput KPIs
CI integration – GitHub Actions pipeline for on-demand & scheduled test runs

Key fixes & findings:

DB write collisions in registration
Checkout API mutex blocking at 3.2K users
Cart service race conditions
WebRTC nodes maxed at 8K viewers
DB pool exhaustion under full spike

IMPACT:

Checkout P95 latency ↓ from 4.6s → 1.1s
Error rate ↓ from 17% → <0.8%
Stream latency ↓ from 2.9s → <500ms
Test run cost ↓ from $85 → $19

Outcome:

Identified and resolved all critical performance issues in <14 days
Platform launched successfully with 4,500+ live users
Full CI-integrated performance suite retained post-launch
Real-time dashboards and test artifacts shared with investors as part of due diligence

Investor result:

Technical due diligence passed successfully
Investment secured and milestone-based rollout approved
Skipper Soft’s testing outputs directly contributed to investor confidence and deal closure

IMPACT:

Checkout P95 latency ↓ from 4.6s → 1.1s
Error rate ↓ from 17% → <0.8%
Stream latency ↓ from 2.9s → <500ms
Test run cost ↓ from $85 → $19

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Industries

We deliver expertise across diverse domains – from web, API, and mobile applications to complex solutions such as embedded tech and algorithm validation. Our experience spans multiple industries, including: