Scale, Governance,
and SLA Guarantees
Enterprise QA teams need more than test automation — they need governance across distributed teams, infrastructure-scale load testing, multi-model consensus validation for critical changes, and contractual SLA guarantees. NexusQA Enterprise delivers all of this with on-premise deployment options.
Enterprise-Grade Quality Assurance
The Problem
Governance across distributed QA teams with different standards
NexusQA Solution
Organization-scoped Row Level Security ensures complete tenant isolation. The quality scorecard provides unified metrics across all teams and services. Configurable test schedules with cron expressions let each team run suites on their own cadence while reporting to a single dashboard.
The Problem
Scale testing for Kubernetes infrastructure
NexusQA Solution
k6 and Artillery simulate concurrent user loads with configurable ramp-up profiles. Auto-scale verification confirms your Kubernetes HPA triggers correctly — NexusQA tests that pods actually scale, not just that the config says they should.
The Problem
Multi-stakeholder approval workflows for critical changes
NexusQA Solution
MageAgent consensus validation sends critical remediation plans to 3-5 competing LLMs for independent review. A 3-layer consensus engine ensures no single AI model makes unilateral decisions about your production code. Human QA sign-off is always the final gate.
The Problem
QA contractor management is fragmented across platforms
NexusQA Solution
The freelancer marketplace integration connects to Upwork (GraphQL API) and Freelancer.com (REST API). Auto-post QA jobs when ticket backlog exceeds configurable thresholds. Contract management, billing reconciliation, and performance tracking in one place.
The Problem
On-premise deployment requirements for regulated industries
NexusQA Solution
The Unlimited tier supports on-premise deployment with dedicated infrastructure. You control where your data lives, which AI providers process your code, and what network boundaries exist. Technical Account Manager provides hands-on support.
No Single AI Makes Decisions Alone
For critical remediation plans, MageAgent sends the proposed fix to 3-5 competing LLMs. Each model independently reviews the plan and returns a verdict. A 3-layer consensus engine (competition, collaboration, consensus) synthesizes the results. Unanimous approval proceeds. Disagreements are escalated for human review. This is not a wrapper around one model — it is genuine multi-model decision science applied to code quality.
Enterprise QA, Built for Scale
Custom onboarding, dedicated infrastructure, and SLA guarantees. Let's talk.
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