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Nexoqoby nexoqoai.tech

AI QualityOps infrastructure for teams building agents, LLM applications, RAG systems, and autonomous workflows.

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© 2026 nexoqoai.tech. Nexoqo is the AI QualityOps platform.

Make production AI measurable, testable, and dependable.

Engineering solution

QualityOps for teams building production AI.

Create repeatable evaluations for prompts, models, retrieval, tools, and agent workflows, then compare every change against a defined quality baseline.

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Scenario design · Workflow evaluation · Regression analysis

  1. Home
  2. Solutions
  3. Engineering
Evaluation coverage

Measure each layer that can change AI behavior.

Build realistic scenarios around expected behavior, edge cases, required actions, and the criteria that determine whether the workflow succeeded.

01

Prompt behavior

Include this behavior in repeatable test scenarios and evaluate it against criteria defined for the workflow.

02

Model quality and reliability

Include this behavior in repeatable test scenarios and evaluate it against criteria defined for the workflow.

03

Retrieval quality

Include this behavior in repeatable test scenarios and evaluate it against criteria defined for the workflow.

04

Tool selection and arguments

Include this behavior in repeatable test scenarios and evaluate it against criteria defined for the workflow.

05

Agent workflow completion

Include this behavior in repeatable test scenarios and evaluate it against criteria defined for the workflow.

06

Release readiness

Include this behavior in repeatable test scenarios and evaluate it against criteria defined for the workflow.

Who it serves

A shared quality view for technical teams.

Bring the people building, validating, and operating the AI system into one measurable quality process.

  • AI engineers
  • ML engineers
  • Software engineers
  • QA and quality engineers
  • AI platform teams
  • Engineering leaders
Quality outcome measurable
Define expected behaviorAlign scenarios with the workflow and its users.
Evaluate the complete pathMeasure actions and intermediate steps, not only final text.
Compare against a baselineReview quality signals before and after each change.

Conceptual QualityOps workflow; no customer performance data is shown.

Standardize quality signals across the development lifecycle and make AI release decisions using measurable criteria.

Related capabilities

Build the supporting quality workflow in Nexoqo.

Connect this solution to the evaluation capabilities that measure agent behavior across development and production.

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AI Agent Testing

Test autonomous and semi-autonomous agents against realistic task scenarios, evaluating both the final result and the sequence of actions used to reach it.

Task understandingInstruction followingTool selection

LLM Evaluation

Run repeatable evaluations on LLM outputs using deterministic checks, semantic methods, model-based judges, custom evaluators, and human review.

Exact-match rulesKeyword and regex rulesStructured-output validation

Regression Testing

Run an established evaluation suite against prompt, model, retrieval, workflow, or agent updates and compare the results with a previous quality baseline.

Quality movementTask completionSafety

Release Quality Gates

Establish minimum quality thresholds that help teams determine whether an AI release is ready for production.

Pass rateWeighted evaluation scoreSafety thresholds
Engineering AI quality

Define the evaluation criteria your workflow needs.

Discuss how Nexoqo can structure scenarios, quality signals, and release checks around your AI system.

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