01
Evidence over opinion
Every claim about AI quality is backed by measurable evals, reproducible tests, and audit trails. No "the AI feels good." Pass/fail thresholds defensible in a release meeting.
About GatekeeperOps
About GatekeeperOps
A specialist AI-QA and Agentic QE practice built for teams shipping AI features into production.
Why GatekeeperOps Exists
The quality gap AI features exposed.
Over the last two years, AI features moved from prototypes into production software. That shift exposed a quality gap most engineering teams were not ready for.
Traditional QA frameworks could not test these features. Automation suites validated UI behavior, not model behavior. Unit tests checked function outputs, not semantic accuracy. Coverage reports reported the wrong coverage.
AI features were shipping blind. Hallucinations were being caught by customers, not engineers. Prompt regressions surfaced as support tickets. RAG retrieval drift went unnoticed for weeks. Companies were taking on serious quality risk and most of them did not realize how serious it was.
GatekeeperOps exists to close this gap with a practical operating model that combines evals, automation, red-teaming, CI/CD gates, and release evidence into one system.
The Work
GatekeeperOps builds release risk gating for AI-native SaaS teams.
The practice tests AI features. LLM evaluations, RAG quality checks, prompt regression suites, hallucination detection, agentic workflow validation.
The practice red-teams AI features. Prompt injection probes, adversarial inputs, edge case generation, tool misuse scenarios, stale context simulation.
The practice gates AI features. Ship/no-ship dashboards. Failure thresholds tied to release approval. Executive risk reports. Engineering leaders get a clear view of what is safe to release, what needs review, and what should be blocked.
The practice also fixes the QA foundations underneath. Flaky automation, broken CI, unstable test suites, weak release signals. AI-QA cannot be added on top of a broken QA system and expected to hold.
The work is engineering. Not consulting. Not strategy decks. Production-grade output that engineering teams use every day.
Test
- LLM evals
- RAG quality checks
- Prompt regression
- Hallucination detection
- Agentic workflow validation
Red-Team
- Prompt injection probes
- Adversarial inputs
- Edge case generation
- Tool misuse scenarios
- Stale context simulation
Gate
- Ship/no-ship dashboards
- Failure thresholds
- Release approval gates
- Executive risk reports
Operating Principles
The principles that guide every engagement.
Non-negotiable. Visible to clients in the work itself.
02
Production engineering, not consulting
Code is written. Frameworks are built. CI/CD integrations are shipped. Engagements deliver running systems, not recommendation reports. When the engagement ends, the client keeps the infrastructure.
03
Practitioner depth over theatre
GatekeeperOps is built around practitioners who prove capability through real assignments, technical interviews, and production-grade automation standards. The vetting bar is the moat.
04
Honest about tradeoffs
AI quality has real tradeoffs. Faster releases versus higher confidence. Broader eval coverage versus longer CI times. Lower hallucination rates versus narrower model output. These tradeoffs are surfaced to engineering leadership instead of hidden.
05
Build for the team that stays
Every engagement leaves the client team stronger. Documented methodology. Runbooks. Trained engineers. The goal is for the internal team to own AI quality after the engagement. Not perpetual dependency.
Technical Foundation
Nine years of production QA brought into the AI era.
The practice methodology is anchored in nine years of SDET and automation engineering across enterprise SaaS, financial software, and consulting engagements. The methodology brings nine years of production QA engineering discipline into the AI-native software era.
Primary Stack
- ›Playwright + TypeScript
- ›Page object models
- ›API testing layers
- ›Visual regression
- ›Accessibility checks
- ›Parallel execution
- ›Allure reporting
- ›Self-healing utilities
- ›GitHub Actions / Jenkins / Azure DevOps
Secondary Stack
- ›Selenium + C#
- ›RestSharp (API automation)
- ›NUnit + SpecFlow
- ›Behavior-driven testing
- ›Azure DevOps CI/CD
AI-QA Stack
- ›Promptfoo
- ›DeepEval
- ›Ragas
- ›Garak + PyRIT (red-teaming)
- ›Custom Claude API integrations
- ›Helicone (observability)
- ›Supabase + pgvector
The Company
Hyderabad to London. Practice depth, not headcount.
GatekeeperOps AI Private Limited is incorporated in India, with operations in Hyderabad and a primary market focus on London-based AI-native SaaS teams. The four-hour time zone overlap between IST and BST means real-time collaboration during UK working hours.
The company operates a Hyderabad-to-London model. London-facing engagements run on UK working hours. Engineering delivery and methodology development happens in Hyderabad. Practice lead oversight on every client engagement.
The talent layer is structured as a vetted network rather than a traditional staffing firm. Engineers are sourced, screened across five stages, and deployed as embedded specialists or as part of GKO-managed delivery pods. The vetting bar is the company's most defended asset.
GatekeeperOps is structured around practice depth, not headcount. The practice lead oversees methodology, delivery quality, and talent vetting directly. As the company grows, the delivery model scales through vetted specialists while preserving practice-level quality control.
Company Information
GatekeeperOps AI Private Limited
EntityGatekeeperOps AI Private Limited
RegisteredIndia
OperationsHyderabad
Primary marketLondon-based AI-native SaaS teams
Contactpardha@gatekeeperops.ai
Vendor Onboarding
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Find out where your AI quality stands.
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