Case studies

Proof, not promises

Numbers from live engagements — measured, audited, repeatable.

Logistics

6 days → 2 days

Regression cycle time

A logistics enterprise stopped letting QA decide its release cadence

The problem

Regression cycles ran 5–6 days with roughly 70% manual testing, limited coverage visibility, and frequent automation breakages. Weekly releases were not feasible.

The solution

Nexus for requirement intelligence and coverage mapping. QE Agents for AI-driven script generation and self-healing. CI/CD-integrated execution with smart reruns.

-67% Regression cycle (6 → 2 days)+35% Defect detection-45% Manual effort-40% Flaky testsWeekly Release cadence established

Banking & Financial Services

60 days → 10 days

Audit preparation time

Regulatory AI audit prep cut by 83% for a leading private bank

The problem

Audit preparation consumed 60 days per cycle, with release confidence eroding and defects escaping to production under regulatory scrutiny.

The solution

CHEQ for obligation mapping and audit-ready evidence. QAVE evaluation integrated into the release pipeline for continuous compliance posture.

-83% Audit prep time+32% Defect detection effectiveness↓ Production incidents✓ Audit readiness improved

Retail & E-commerce

6 days → 2 days

Release cycle

Release cycle compressed 3× at a top-5 e-commerce platform

The problem

Automation maintenance was consuming the team: flaky suites, brittle locators, and validation cycles that could not keep pace with releases.

The solution

Self-healing QE Agents stabilized the suite; Nexus prioritized tests by risk; execution moved into CI/CD with parallel device-farm runs.

-60% Automation maintenance-40% Flaky tests3× Faster validation cycles

GCC / Talent

0 → 120 engineers

Team ramped in 90 days

Full AI engineering team ramped in 90 days for a UAE financial group

The problem

A UAE financial group needed an AI delivery capability from zero — hiring, platforms, and governance — on an aggressive timeline.

The solution

GCC Launchpad: end-to-end hiring across 8 talent tracks, six platforms deployed from day one, SURE-Q governance installed from the start.

120 Engineers in 90 days6 Platforms deployed day one94% 12-month retention

Tech & SaaS

45+

Failure scenarios caught pre-production

Continuous AI evaluation for an AI customer-support product

The problem

An AI support assistant was shipping with unknown failure modes — hallucinated answers and inconsistent responses surfacing only after customer complaints.

The solution

QAVE simulation across 40+ persona archetypes; Qurator pipelines wired into CI/CD so every release candidate passes structured evaluation before promotion.

45+ Failure scenarios identified pre-production+30% Response accuracyCI/CD Evaluation fully integrated

Next step

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