Nondeterministic behavior
The same input does not always produce the same result.
AI QualityOps · nexoqoai.tech
Ship AI agents you can trust.
Test, evaluate, monitor, and improve AI agents across every prompt, model, tool call, retrieval step, and workflow before failures reach production.
Book a meetingBuilt for production AI teamsAI · ML · Platform · Quality
One quality view
See what changed. Know whether to release.
Bring test results, quality signals, regressions, traces, and release criteria into one continuous workflow.
Evaluation suite
Run completeRelease quality overview
Quality score82weighted evaluation
Tests passed2 / 4one requires review
Release gateReviewcritical scenario failed
ScenarioScoreStatusLatencyChange
Refund workflow96Pass 1.8s+3%
Account recovery91Pass 2.1s+1%
Policy question73Review 1.4s−8%
Tool failure recovery68Fail 3.2s−12%
Sample metrics explain the product workflow and are not customer results.
Why QualityOps
AI systems need a new quality layer.
Traditional QA was designed for deterministic software. Production AI is probabilistic, multi-step, model-dependent, and constantly changing.
Hidden workflow failures
A plausible final answer can hide incorrect intermediate actions.
Prompt and model regressions
Small changes can create unexpected quality loss.
Production uncertainty
Teams often discover AI failure patterns only after users do.
The Nexoqo platform
One quality workflow from development to production.
Turn AI behavior into measurable engineering signals across the complete quality lifecycle.
01
Test
Build repeatable scenarios for agents, prompts, RAG systems, and workflows, including expected behavior, edge cases, and business-critical tasks.
02
Evaluate
Measure correctness, groundedness, tool usage, safety, latency, cost, and business-specific acceptance criteria.
03
Monitor
Continuously score production behavior, track quality over time, and surface emerging failure patterns.
04
Improve
Trace quality problems to prompts, models, context, retrieval, tools, orchestration, policies, or workflow logic and iterate with evidence.
Evaluate every step. Catch regressions early. Ship production AI with evidence.
Trajectory evaluation
Evaluate the entire agent journey.
A convincing final answer does not guarantee a correct workflow. Nexoqo is designed to examine the decisions behind a response, from retrieval and tool selection to intermediate actions and task completion.
- Validate tool selection and arguments
- Detect unnecessary or missing steps
- Evaluate recovery and final-state completion
Agent trace · sample evaluated
01
User requestIntent: resolve billing issue
02
Knowledge retrievalPolicy context found
03
Tool selectedbilling_lookup
04
Result validatedRequired fields present
05
Final responseTask completed
Illustrative product workflow, not customer production data.
How it works
Close the loop between testing and production.
Create a repeatable system that learns from real behavior and strengthens every future release.
01
Connect
Connect an AI application, agent, workflow, API, or evaluation dataset.
02
Define Quality
Choose what success means using metrics, evaluators, rules, and business-specific acceptance criteria.
03
Run Tests
Execute scenarios across prompts, models, agent versions, datasets, or workflows.
04
Analyze
Review scores, traces, failure clusters, regressions, and model comparisons.
05
Gate Releases
Use measurable quality thresholds to determine whether an AI release is ready for production.
06
Monitor Production
Evaluate real-world behavior and convert production failures into future test cases.
Build
Test
Deploy
Observe
Learn
Test again
Built around real work
Quality infrastructure for every AI workflow.
Define the behaviors that matter for each team, then evaluate them with one shared quality process.
Model agnostic
Add quality to the stack you already use.
Connect models, frameworks, retrieval systems, and engineering workflows without making one vendor your quality boundary.
Model providersAgent and application frameworksData and retrievalEngineering workflowModel providersAgent and application frameworksData and retrievalEngineering workflow
Categories describe compatibility direction and do not imply a vendor partnership or endorsement.
Common questions
A clear view of AI QualityOps.
The essentials about Nexoqo, continuous evaluation, and the systems it is designed to assess.
What is Nexoqo?
Nexoqo is an AI QualityOps platform for testing, evaluating, monitoring, and improving AI agents and LLM-powered applications.
What is AI QualityOps?
AI QualityOps is a continuous quality practice for AI systems. It combines automated evaluation, regression testing, production monitoring, and quality improvement throughout the AI development lifecycle.
What can Nexoqo evaluate?
Nexoqo is designed to evaluate AI outputs, agent workflows, tool calls, RAG systems, multi-turn conversations, task completion, safety, latency, cost, and custom business-specific criteria.
Is Nexoqo only for AI agents?
No. The broader product scope includes AI agents, LLM applications, RAG systems, copilots, conversational AI, and autonomous workflows.
Can teams use custom evaluation criteria?
Nexoqo is designed to support customer-specific quality criteria because different AI systems require different definitions of success.
Can Nexoqo compare multiple models?
Model and version comparison is a core QualityOps use case. Teams can evaluate multiple models using the same dataset and metrics.
How does Nexoqo help with regressions?
Teams can rerun established evaluation suites whenever they change prompts, models, retrieval logic, tools, or workflows and compare the results against a previous baseline.
Can production behavior be evaluated?
Production evaluation is part of the Nexoqo vision. Real-world traces can be scored, analyzed, clustered, and converted into future regression tests.
Bring your AI system
Book a meetingMake AI quality measurable.
See how Nexoqo can bring continuous testing, evaluation, and release confidence to your AI workflow.