QAaaS Enabled
Top AI Recruitment
Platform to Scale 10x
Hidden Brains re-engineered HireSales AI, a South African AI recruitment platform, with a QA-as-a-Service that took it from 50 users to running at 500+, cut the error rate from 60% to under 0.5%, and dropped API response time from over 5 seconds to 1.1 seconds.
Industry
AI-powered recruitment / HR-tech (SaaS)
Service
Quality Assurance As A Service
Engagement model
Managed Hire Services
Region
South Africa
Metric
Before
After (with Hidden Brains QAaaS)
Client Overview
HireSales AI is a next-generation, AI-powered recruitment platform built for intelligent job matching, real-time recommendations, and centralised recruiter workflows. It operates across South Africa with global expansion plans, and is used by HR teams, recruitment firms, and hiring managers.
The platform had genuine AI capability and real market momentum. But despite that, it suffered from severe backend and performance issues that hampered demos, threatened investor trust, and delayed go-lives. In recruitment tech, even an innovative AI platform can falter without a robust backend and disciplined test automation underneath it, and that was exactly the gap.
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The Challenges
Before Hidden Brains engaged, HireSales AI was stuck in a cycle of firefighting. Manual QA run through Excel sheets and brittle test scripts meant bugs were caught reactively, not prevented. Four failures kept recurring, each with a direct business cost:
API failures
What happened
Platform failed beyond 50+ concurrent users
Business cost
Disrupted live demos and client presentations
60% error rate
What happened
Occurred during peak load testing phases
Business cost
Raised investor concerns about scalability
Slow response
What happened
API response exceeded 5 seconds consistently
Business cost
Poor user experience and growing frustration
Backend crashes
What happened
Frequent backend crashes during UAT testing
Business cost
Blocked product releases and delayed launches
Root Cause Diagnosis
Hidden Brains ran a deep-dive audit and traced the instability to five specific, fixable causes:
Unoptimized database queries and missing MongoDB indexes
Blocking Python code and synchronous AI logic
Oversized API payloads slowing down transfer
Lack of caching on high-read endpoints
No structured load testing or CI QA processes
Why HireSales AI
Chose Hidden Brains
HireSales AI didn't need a rebuild; it needed a partner who could diagnose why a working product broke under load and fix it at the root. Hidden Brains was chosen for its ability to combine performance engineering, test automation, and hands-on developer collaboration.
One team, three disciplines: Performance engineering + test automation + hands-on developer collaboration in a single managed engagement.
Enterprise-grade process: CMMI Level 3 delivery discipline and a track record with AI and recruitment-tech products.
A clear mandate: Turn instability into scale, and uncertainty into investor-ready confidence, without stalling the roadmap.
The Solution &
Technical Approach
Hidden Brains deployed a QA-as-a-Service (QAaaS) strategy combining performance engineering, automation, and developer collaboration, moving QA from a reactive gatekeeper to a built-in engine of product reliability. It was a deep-dive performance audit that identified the exact bottlenecks above, so every fix was targeted rather than speculative.
Simulated real-world load
Load, spike, and soak testing with Apache JMeter to find breaking points before users did, reproducing the 500+ concurrent-user conditions of job fairs and bulk-hiring events in a controlled environment.
Automated test coverage
CI pipelines with Playwright and Selenium to catch regressions instantly, replacing brittle manual scripts with a modular, reusable test suite wired into the delivery pipeline.
Optimised AI delivery
Decoupled the inference logic and added batching and caching to speed up AI responses, directly addressing the blocking Python and synchronous AI-logic root causes.
Backend & data-layer hardening
Added the missing MongoDB indexes, optimised queries, trimmed oversized API payloads, and introduced caching on high-read endpoints to collapse latency and eliminate load-driven errors.
The technology stack was selected around the client’s business requirements, existing systems, integration needs, scalability goals, and long-term maintainability. Each technology played a specific role in supporting the solution rather than following a one-size-fits-all stack.
Technology Stack
The Results
Hidden Brains transformed quality assurance from a bottleneck into a business accelerator.
The direct before-and-after comparison
Before Implementation 
After QAaaS 
Manual Excel tracking 
TestRail + Jira workflow 
Fragile scripts Modular CI test suite 
Crashes @ 50 users Stable @ 500+ users 
60% 
<0.5% 
>5 sec 
1.1 sec 
Blocked by bugs 
70% 
99.95%
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Business value
delivered
Investor confidence
Seamless demo handling with a zero-crash record.
Readiness at scale
The platform now handles job fairs and bulk-hiring events.
Test coverage
95%+ automation across frontend, backend, and APIs.
Collaboration
QA, Dev, and Product now ship in a unified delivery rhythm.
Faster time-to-market
40% faster release cycles.
Strategic outcome
QAaaS became the backbone of product reliability, enabling faster, safer releases with reusable testing and preparing HireSales AI for global scale.
Related Capabilities
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Frequently Asked
Questions (FAQ)
Got questions about QA and how it impacts product quality, scalability, and business outcomes? We’ve got you covered.
Yes. Investors judge a platform on whether it holds up during live demos and load. Moving HireSales AI from a 60% error rate and crashes at 50 users to <0.5% errors and stable performance at 500+ users produced a zero-crash demo record, directly supporting investor confidence.
No. Coverage reached 95%+ across frontend, backend, and APIs. The biggest wins were in the backend and data layer: MongoDB indexing, query optimisation, payload reduction, caching, and decoupled AI inference.
Apache JMeter for load, spike, and soak testing; New Relic and Amazon CloudWatch for monitoring; Selenium and Playwright for automated regression in CI; and TestRail + Jira for QA workflow.
The test suites are modular and CI-integrated, so they adapt as the architecture evolves rather than being rewritten. Reusable performance and functional suites are designed to travel with the product.
Yes. The same QA-as-a-Service model, performance engineering, CI automation, and AI-delivery optimisation, is available as a managed engagement for startups and scale-ups facing similar load and stability challenges.
Yes. The approach is built on enterprise-grade practices (CI/CD, structured load testing, monitoring, CMMI Level 3 process) and scales from startup to enterprise workloads.
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