AI eval suites
Repeatable tests for LLM, RAG, hallucination, and prompt behavior
GatekeeperOps · AI-Native Quality Engineering
Release Risk Gating for AI-Native SaaS Teams
We test, red-team, and gate AI features and agentic workflows before they reach production, while fitting into your existing engineering workflow.
45-min call. Written report. No sales script.
LLM Eval CoverageRAG Quality GatesRed-Team TestingAgentic Workflow QACI/CD Integration
The Problem
Your AI features are shipping. Your AI quality system is not.
LLM features are landing in production weekly. Hallucinations are caught by customers, not engineers. RAG retrieval drifts silently as embeddings update. Prompt regressions surface in support tickets. Agentic workflows can take actions no one fully reviews.
Most engineering teams know this. They do not have the time, the right hires, or the methodology to fix it.
Internal QA was built for deterministic software. AI features do not fail like deterministic software. Existing test suites cannot tell you when an AI feature is safe to ship. Most teams are flying blind on the highest-risk surface in their product.
GatekeeperOps exists to solve this. We build the AI quality layer your team needs before something breaks publicly.
The Distinction
AI-Native QA is not traditional QA with extra steps.
Traditional QA validates expected product behavior. AI-Native QA validates output quality, reasoning risk, and behavior that changes with models, prompts, and data.
A unit test catches a regression in a sorting function. An AI eval catches a regression in how your model handles ambiguous customer questions. The first is deterministic. The second is statistical. The tooling, methodology, and engineering discipline required are completely different.
| Traditional QA | AI-Native QA |
|---|---|
| Validates expected flows | Validates behavior, output quality, and risk |
| Pass/fail on known inputs | Scores outputs across scenarios |
| Deterministic outcomes | Probabilistic outcomes |
| Code regressions | Behavior regressions |
| Stable test cases | Test sets that evolve with models, prompts, and data |
| Bugs are visible | Hallucinations and drift are subtle |
Teams that treat AI features like deterministic software will ship more incidents. The teams that win are the ones building real AI quality discipline now.
Methodology
The GatekeeperOps Methodology
A clear three-step system to build release confidence for AI features and agentic workflows. The methodology comes from production QA engineering, not generic consulting theory.
See Detailed MethodologyDeliverables
What your team gets from GatekeeperOps
Concrete outputs your engineering team owns, runs, and maintains after the engagement.
Release risk reports
Clear evidence showing what can ship and what should be blocked
CI/CD gates
Quality checks wired into GitHub Actions, Jenkins, or your release workflow
Agentic workflow tests
Validation for tool calls, API actions, browser flows, and recovery paths
QA system repair
Flakiness, broken CI, weak coverage, and unreliable test signals fixed
Vetted AI-QA talent
Engineers screened for automation depth, AI-QA skill, and client readiness
Executive risk summaries
Clear reporting for CTOs and engineering leaders, not just test logs
Services
AI-QA + Agentic QE Services for Safer AI Releases
Choose the service path that matches your current AI quality, release risk, QA system, or talent bottleneck.
Free AI-QA Maturity Audit
Review your AI testing maturity, eval coverage, hallucination controls, and release risk.
Learn moreAI-QA Foundation
Build evals, automation, CI gates, and reporting for your first serious AI feature.
Learn moreRelease Risk Gate
Run continuous AI-QA checks before release with clear ship/no-ship evidence.
Learn moreAgentic Workflow Testing
Validate agents that call tools, APIs, browsers, or workflows before production.
Learn moreQA System Rescue
Repair flaky automation, broken CI, unstable test suites, and weak release signals.
Learn moreContinuous AI-QA Operations
Ongoing AI-QA coverage, monitoring, red-team refresh, and executive risk reporting.
Learn moreAI-QA Talent Network
Deploy vetted AI-QA and Agentic QE engineers from India through GKO's network.
Learn moreQA System Rescue
Your AI features are at risk because your QA foundation is broken.
Most engineering teams trying to add AI quality discipline discover a deeper problem: their existing QA system is already broken.
Automation suites with outdated flows, disabled tests, and failure reports nobody trusts. CI/CD pipelines that fail randomly. Coverage reports that look good but mean nothing. Engineers bypassing the test gates entirely because the gates are not reliable.
Adding AI-QA on top of a broken QA system makes the risk worse, not better. AI evals get ignored alongside the rest. Hallucination tests join the pile of muted alarms. Release confidence drops further.
If this is where your team is, fix the foundation first. We have a specific service for this.
Talent Network
Hire vetted AI-QA and Agentic QE engineers without building bench.
The AI-QA talent market is small. Engineers who can combine automation depth with LLM evals, RAG quality systems, and agentic workflow testing are even harder to find. Most engineering teams cannot reach this profile through traditional recruiting.
GatekeeperOps runs a vetted network of AI-QA and Agentic QE engineers from India. The network is built around a five-stage vetting process designed to filter for real AI-QA skill, automation depth, and client readiness. Each engineer is screened across profile review, take-home assessment, live technical interview, debug exercise, and final round.
You hire from a network already trained on the methodology, the tools, and the production realities. They can work as embedded engineers on your team or as part of a GatekeeperOps-managed delivery pod.
- Tier S LeadSenior practitioners. AI-QA leadership and client-facing capability.
- Tier A SeniorIndependent delivery on AI quality, agentic workflows, automation.
- Tier B MidExecution support with strong fundamentals and AI exposure.
Methodology Origin
Built on production engineering, not consulting theory.
GatekeeperOps is a specialist AI-QA and Agentic QE practice. The methodology is built on nine years of SDET and automation engineering across enterprise SaaS, including production frameworks built from scratch in Playwright with TypeScript, Selenium with C#, and CI/CD ownership across GitHub Actions, Jenkins, and Azure DevOps.
The discipline behind GatekeeperOps comes from operating production QA systems, not from reading about them. Every methodology decision reflects what works in production engineering environments where release confidence is measured, defended, and audited.
Delivery is anchored by the practice lead and supported by a vetted network of AI-QA engineers screened against the same production engineering bar. Every engagement is overseen directly. Methodology quality is not delegated.
gko-release-gate · CI
$ promptfoo eval --config promptfooconfig.yaml Running 24 test cases... ✓ factuality 18/18 passed (100%) ✓ no-hallucination 12/12 passed (100%) ✓ rag-groundedness 8/ 8 passed (100%) ✗ adversarial 3/ 6 passed (50%) Threshold: 90% · Actual: 86% · BLOCKED Release gate: FAIL. Do not ship.
Release blocked. Adversarial threshold not met.
Methodology
Test · Red-Team · Gate
Practice Lead
Every engagement overseen
Vetted Network
Screened to the same bar
Writing
Recent Writing
Practitioner perspective on AI quality, release risk, and agentic engineering. No hype. No generic theory. Practical notes from building AI quality systems for production teams.
Why AI Features Need Release Risk Gating
Shipping AI features without release gates means your customers find the failures first.
Read moreLLM Evaluation Is Not Enough Without Release Gates
Running evals is only half the system. The other half is deciding what to do with the results.
Read moreWhy Broken QA Systems Become Worse in AI-Native Teams
Adding AI evals on top of a broken QA foundation does not improve confidence. It buries it.
Read moreFind out where your AI quality stands.
The Free AI-QA Maturity Audit takes 45 minutes. You get a written maturity report covering eval coverage, hallucination controls, RAG quality, agentic workflow risks, and release gating. No commitment, no sales script.
Book Free AI-QA AuditBuilt for AI-native teams shipping LLMs, RAG systems, and agents into production.