Free Audit
Free AI-QA Audit
A structured review of your AI testing maturity, eval coverage, hallucination controls, and release risk profile.
Learn moreServices
Every service is an engineering engagement, not a consulting package.
GatekeeperOps tests, red-teams, and gates AI features and agentic workflows. Each service delivers running infrastructure: evals, CI gates, automation, and release evidence that the team owns and operates after the engagement ends.
How to Choose
Match the situation to the service.
Each service addresses a specific engineering situation. Find the one that matches the current problem. If none is obvious, start with the Free AI-QA Audit.
Situation
Recommended Service
Shipping the first LLM or RAG feature and have no AI-QA coverage yet.
AI QA Foundation
Builds evals, CI gates, and quality reporting from the ground up before the first release.
Releasing AI features regularly but lack a repeatable gate between build and production.
Release Risk Gate
Installs continuous AI-QA checks with clear ship/no-ship evidence before every release.
Running agents, tool-calling workflows, or multi-step AI pipelines that have not been stress-tested.
Agentic Workflow Testing
Validates agents against adversarial inputs, tool misuse, and recovery failures before they reach users.
Existing automation is flaky, CI is broken, or test suites are not giving reliable release signals.
QA System Rescue
Repairs the QA foundation so that AI-QA layers have something stable to build on.
Need AI-QA operating continuously as part of the engineering workflow, not as a one-time project.
Continuous AI QA Operations
Embeds an AI-QA practice into the team for ongoing eval maintenance, red-teaming, and release gating.
Scaling an AI-QA program and need vetted engineers deployed fast without a long hiring process.
AI QA Talent Network
Provides screened AI-QA and Agentic QE engineers from the GKO vetted network, ready to embed.
Not sure where to start or what level of AI-QA risk the current product is carrying.
Free AI-QA Audit
A 45-minute structured review that produces a written report on testing maturity, eval coverage, and release risk.
Service Catalogue
Seven services. One operating model.
Each service is scoped to a specific stage of AI-QA maturity. They can run independently or in sequence depending on where the gaps are.
Build
AI QA Foundation
Build evals, automation, CI gates, and quality reporting for the first serious AI feature.
Learn moreGate
Release Risk Gate
Continuous AI-QA checks before every release, with clear ship/no-ship evidence.
Learn moreStress-Test
Agentic Workflow Testing
Validate agents that call tools, APIs, browsers, or multi-step workflows before production.
Learn moreFix
QA System Rescue
Repair flaky automation, broken CI, unstable test suites, and weak release signals.
Learn moreOperate
Continuous AI QA Operations
An embedded AI-QA practice operating as part of your engineering workflow.
Learn moreTalent
AI QA Talent Network
Vetted AI-QA and Agentic QE engineers from India, deployed through the GKO network.
Learn moreMethodology
Every service runs on the same methodology.
The GatekeeperOps methodology defines how evals are structured, how red-teaming is conducted, how release gates are set, and how quality evidence is generated. Understanding the methodology gives context for how each service produces its output.
Read the MethodologyPricing
Why there are no public rates on this page.
GatekeeperOps does not publish rates because AI-QA scope varies significantly between teams. A team with no existing automation needs a different engagement than one with a mature CI pipeline that needs AI eval layers added. Publishing a rate table without that context produces misleading expectations on both sides.
The Free AI-QA Audit exists specifically to solve this. After the audit, both sides have a shared view of the gaps, the recommended service, the scope, and the expected deliverables. Pricing is discussed against that shared view, not against a generic tier.
The only price on this site is free, which is the cost of the audit.
What you get before pricing is discussed
- ›A 45-minute structured diagnostic call
- ›A written report covering AI testing maturity
- ›Eval coverage and hallucination control gaps identified
- ›Release risk profile for the current product
- ›A recommended service and engagement scope
What does not happen
- ›No sales presentation
- ›No discovery call disguised as a free service
- ›No upsell during the audit call
- ›No commitment required to receive the report
FAQ
Common questions.
Standard QA validates deterministic behavior against known inputs. AI-QA tests probabilistic outputs, model behavior under distribution shift, prompt regressions, hallucination rates, and semantic accuracy. Traditional test frameworks cannot measure these things. A green CI pipeline does not mean an LLM feature is safe to ship.
For most teams, the Free AI-QA Audit is the correct first step. It produces a written report showing exactly where the gaps are, which services address them, and in what order. Starting without that diagnostic risks building the wrong coverage first.
Yes. Every engagement is structured as embedded delivery, not external advisory. GatekeeperOps works inside the existing workflow, using the team's repositories, CI pipelines, and deployment tooling. The output is infrastructure the internal team owns and operates after the engagement ends.
Yes. AI-QA cannot produce reliable release signals on top of broken automation or unstable CI. If the foundation needs repair first, QA System Rescue addresses that before AI-QA layers are added. Some engagements run both in parallel depending on the situation.
Engineers in the GKO network are screened across five stages against production-grade automation standards, not just a CV review. They are assessed on real tasks, not self-reported skills. The vetting bar mirrors the standard applied to GatekeeperOps delivery directly. Staffing firms do not apply an engineering-specific vetting bar.
The Free AI-QA Audit has no minimum commitment. For build and operate engagements, the scope is scoped to the actual problem, not a predetermined tier. Engagement scope is discussed after the audit report so both sides have a shared view of what needs to be done.
Find out where your AI quality stands.
The Free AI-QA Audit is a 45-minute structured review. No commitment required. The output is a written report covering AI testing maturity, eval coverage, hallucination controls, and release risk.
Book Free AI-QA Audit45-min call. Written report. No sales script.