What I fix
Flaky suites, weak quality gates, slow regression, and automation that slows delivery instead of accelerating it.
Release Confidence Recovery for broken automation systems
Test Automation Consulting for Engineering Teams That Can No Longer Trust CI
I’m Rahul Kant Jha, a release-confidence recovery specialist for CTOs, VPs Engineering, QA leaders, and founders dealing with expensive release risk, flaky automation, noisy CI, and regression drag.
This is intentionally narrow: fewer scripts, clearer release signal, stronger quality gates, and a handoff your team can sustain. If you are deciding whether to hire another SDET, start with a free 20-minute fit call or a $450 CI Signal Triage.
Why teams hire me
I diagnose the bottleneck in days, stabilize the signal in weeks, and hand off a system the team can sustain. AI accelerates analysis; architecture, gates, ownership, and handoff create the moat.
When delay is expensive
If every rerun, bypass, or late regression defect creates leadership doubt, the cost is already bigger than a narrow expert diagnosis.
Shaped by
Oracle Logitech Flipkart
11+ years in automation leadership
Enterprise and product-scale systems
Global remote consulting
The real problem
Your QA automation is costing release confidence.
Flaky tests destroy CI trust.
When engineers cannot tell a real failure from a false alarm, they stop trusting the pipeline. Defects slip through. Releases feel risky.
Regression drains the release calendar.
Without architecture discipline, suites grow faster than maintainability. Each release cycle costs more calendar time, not less.
More QA headcount won’t fix it.
Adding people to a broken automation system multiplies chaos. The fix is structural: cleaner frameworks, better gates, clearer ownership.
Technical Expertise
Top Skills & Technologies
Selenium
Cypress
Playwright
Pytest
Jenkins
GitLab CI
Docker
Kubernetes
AWS
Terraform
SonarQube
OWASP
Rest-Assured
BDD/Cucumber
k6
Selenium
Cypress
Playwright
Pytest
Jenkins
GitLab CI
Docker
Kubernetes
AWS
Terraform
SonarQube
OWASP
Rest-Assured
BDD/Cucumber
k6
Strict fit profile
Built for teams with automation that already exists but no longer creates trust.
This is not generic QA outsourcing. It is for product engineering organizations where a broken release signal is now a leadership problem.
Best fit
B2B SaaS, e-commerce, marketplace, fintech-adjacent, or product engineering teams.
Typically 20-200 engineers, 3-40 QA/QE, weekly or faster releases, and meaningful automated tests already in CI/CD.
Buying trigger
Leadership no longer trusts the pipeline.
Flaky failures, retry habits, slow regression, weak gates, AI-generated test sprawl, or release decisions that still need manual heroics.
Poor fit
Greenfield automation or cheap execution capacity.
If you need generic staffing, device/localization scale, or thousands of new tests maintained by an external vendor, a managed QA provider is a better fit.
Problem to service
Match the release problem to the fastest useful next step.
Search traffic converts when buyers can recognize their own situation quickly. Use this map to choose a low-friction diagnosis or the deeper engagement that fits the risk.
Flaky tests or noisy CI
Start with CI Signal Triage or the Stabilization Sprint.
Best when failures are visible, trust is dropping, and leadership needs a quick read on whether the suite can be stabilized before the next release.
Compare triage and sprint pricingSlow regression or weak gates
Use test automation consulting to find the real bottleneck.
Best when the team feels the pain but is not aligned on whether the cause is coverage, CI, ownership, tooling, or release process.
See consulting deliverablesFramework debt or AI testing confusion
Use QA automation architecture before adding more tests.
Best when Cursor, Copilot, Claude, Playwright, Selenium, or Cypress are producing more code but not a more trustworthy release system.
Review the architecture offerThe release confidence method
Diagnose. Stabilize. Handoff. In 20 days.
I do not hand over a report and disappear. I diagnose the bottleneck, execute the fixes, and leave the team with a system they own.
Phase 1 — Diagnose
Days 1–7
Audit the suite, framework, CI pipeline, and ownership model. Within 7 days, you know exactly what is broken and what to fix first.
Full method detailsPhase 2 — Stabilize
Days 8–17
Execute the fixes: stabilize flaky tests, restructure gates, harden CI. Measurable improvement in release confidence within 10 days.
Full method detailsPhase 3 — Handoff
Days 18–20
Leave ownership docs, runbooks, and a team that knows how to sustain the system. No dependency on me after day 20.
Full method detailsFastest route to a reply
Send the problem in plain language. I will tell you the fastest next step.
Use the contact page if you want a structured diagnosis request. It takes under 2 minutes and filters out the back-and-forth that slows decisions.
Results
Proof that the signal can be fixed.
Every metric on this page comes from actual role outcomes — not aspirational targets.
Oracle-related enterprise release-quality work: API automation, CI integration, regression reduction, and reliability improvement in a role context.
Logitech program work: multi-locale release operations, Jenkins execution discipline, Selenium/Cypress automation, and faster release motion.
Flipkart-scale quality systems: product-scale automation, stable integration checks, CI/CD release support, and strong defect containment.
Pricing
Flat USD pricing. Defined outcomes. No surprises.
Fixed-price engagements in USD with a 50% fee-at-risk term on the Stabilization Sprint against agreed release-signal KPIs.
Release Audit
$2,400
7-10 day diagnosis · Root-cause analysis · Prioritized action plan · Service recommendation.
See what's includedStabilization Sprint
$7,200
20-day execution · Flaky fixes · Gate redesign · CI hardening · Team handoff · KPI-based fee-at-risk term.
See what's includedAdvisory Retainer
$3,600/mo
Bi-weekly architecture reviews · Release readiness · Mentoring · Min. 3 months.
See what's includedSelf-diagnostic
Download the Release Confidence Diagnostic PDF.
8 pages, 15 sections, and a 24-point scorecard for leaders who need to know whether the problem is CI triage, framework architecture, AI-generated test debt, or release gating.
FAQ
Questions buyers ask before they engage.
What does week 1 look like?
The first diagnostic window reviews the suite, maps failure patterns, inspects CI, and identifies the 2-3 changes that will move the signal fastest.
What if my team cannot implement your recommendations?
I do not hand over a spreadsheet and disappear. I prioritize changes by effort and impact, and stay involved through the sprint or retainer.
Will this require rewriting our automation stack?
No. The first move is usually to fix the architecture you already have, remove avoidable noise, and only then decide whether larger changes are worth it.
How do you measure success?
Runtime, flaky failure rate, CI reliability, release signal quality, and whether leadership can trust the automation output again.
How is this different from hiring a freelancer?
Freelancers deliver code. I deliver a system: diagnosis, execution, handoff, and a team that sustains it. Fixed scope, defined outcomes, and an agreed delivery window.
What does the fee-at-risk term cover?
If the agreed release-signal KPIs are not measurably better after the Stabilization Sprint, I refund 50% of the fee. The KPI set and baseline are defined in the statement of work.
Buyer objections
Questions worth answering before a sales call.
“AI is taking over QA. Why hire a consultant?”
AI can generate tests and accelerate analysis. It does not decide ownership, release gates, risk appetite, measurement, or handoff. The work is turning AI-assisted output into a release system your team trusts.
“We already use Cursor, Claude, and Copilot.”
Good. The engagement does not fight those tools. It adds architecture discipline so AI-generated automation supports the right layers, uses the right data, and does not increase maintenance debt.
“Why not hire a full-time SDET?”
Hire full-time when you need ongoing ownership. Use me when leadership needs a fast diagnosis, stabilization, architecture reset, or a clear operating model before adding permanent headcount.
Trigger-event diagnostics
Start with the problem your team is already saying out loud.
Playwright tests became flaky.
Use a focused flakiness diagnostic before migrating tools again.
Open diagnosticCI has too many retries.
Measure noise ratio, retry recovery, and gate trust before adding tests.
Open diagnosticAI generated hundreds of tests.
Check whether AI output follows architecture, data, and release-risk rules.
Open diagnosticReady to fix your release signal?