system.ready
AI Evaluation for
LLMs, Agents & AI Products.
We test AI systems for accuracy, reliability, safety and real-world performance through structured human evaluation and quality assurance.
From LLM evaluation and AI agent testing to red teaming, hallucination detection and human feedback, NovaGaugeAI identifies where AI fails and turns those findings into measurable improvements.
Measure. Diagnose. Improve.
4
Service Groups
5
Step Quality Process
Multi
Language & Cultural
Human
Led Evaluation
why_human_evaluation
AI can generate an answer.
Humans determine whether it's a good one.
NovaGauge combines human expertise, structured evaluation and technology to help companies understand how their AI performs and where it needs to improve. From emerging AI startups to established organizations, we provide the human intelligence needed to build more accurate, reliable and responsible AI.
◇Hallucination Detection
⊡Response Comparison
△Reasoning Quality
⬡Code Verification
○Agent Testing
□Safety & Red Teaming
evaluation_services
AI Quality, powered by human intelligence.
Structured evaluation workflows for modern AI systems — from language models and agents to code, data and multilingual outputs.
service_ 01
AI Model Evaluation
For companies developing LLMs and generative AI.
Includes:
- LLM evaluation
- Response quality
- Hallucination testing
- Reasoning evaluation
- Instruction following
- Model comparison
- Human preference evaluation
service_ 02
AI Agent Evaluation
For companies deploying AI agents.
Includes:
- Tool-use testing
- Task completion
- Agent reliability
- Multi-step reasoning
- Failure analysis
- Human escalation
- Safety testing
service_ 03
AI Safety & Red Teaming
For teams that need to discover failure modes before deployment.
Includes:
- Adversarial testing
- Harmful output testing
- Prompt injection
- Hallucination
- Bias
- Safety
- Guardrail testing
service_ 04
Human Evaluation & Training Data
For companies that need high-quality human feedback.
Includes:
- RLHF data
- Preference data
- Annotation
- Human feedback
- Expert evaluation
- Multilingual evaluation
our_quality_process
How we measure quality.
Every evaluation is built around clear criteria, trained human judgment, and controlled quality assurance.
01▤
Evaluation Criteria & Rubrics
We define what good performance looks like using clear, measurable criteria and detailed rubrics.
02◉
Human Evaluation
Trained evaluators assess AI outputs against the agreed rubrics and guidelines.
03⊛
Calibration
Evaluators review sample tasks and align on scoring standards, edge cases, and interpretation before evaluation begins.
04◈
Quality Assurance
Independent QA checks help identify inconsistencies, errors, and deviations from the evaluation criteria.
05⚖
Agreement & Resolution
Disagreements and ambiguous cases are reviewed and resolved through structured assessment.
06▥
Insights
Evaluation results are transformed into actionable findings, failure patterns, and recommendations for improving the AI system.
Clear criteria→
Human judgment→
Calibration→
Quality control→
Reliable data→
Actionable insights
deliverables
What you receive.
Every evaluation program is structured to provide clear, usable evidence about how your AI performs.
⊟
Annotated Dataset
Human-labeled evaluation data with the agreed labels, categories, or judgments.
▦
Scoring Results
Scores and evaluation outcomes across the agreed criteria and dimensions.
⊠
Error Taxonomy
A structured classification of recurring errors, failure types, and problem patterns.
◎
Quality Metrics
Measurable indicators of performance, consistency, agreement, and other agreed quality dimensions.
△
Failure Analysis
Analysis of where and why the AI system fails, including recurring patterns and critical issues.
◇
Recommendations
Practical, evidence-based recommendations for improving model performance, safety, reliability, or user experience.
▤
Final Evaluation Report
A consolidated report containing the findings, metrics, key failure patterns, and recommended next steps.
quality_and_security
Built for reliable AI evaluation.
AI evaluation is only valuable when the results can be trusted. NovaGauge uses structured workflows built around calibrated evaluators, quality assurance and controlled access to support sensitive AI work.
◎
Quality System
[ 01 ]
Qualified Evaluators
Work is performed by evaluators selected and trained for the task, not anonymous crowdsourcing.
[ 02 ]
Clear Evaluation Criteria
Every task is scoped with explicit rubrics, scoring criteria and expected outputs.
[ 03 ]
Reviewer Calibration
Evaluators are calibrated against sample tasks and gold-standard examples before production work.
[ 04 ]
Independent Assessments
Multiple independent reviews are used where agreement and confidence matter.
[ 05 ]
Disagreement Resolution
Ambiguous or conflicting cases are escalated and resolved through structured review.
◈
Security System
[ 01 ]
Quality Assurance
Outputs pass through QA layers with spot checks and delivery validation before hand-off.
[ 02 ]
Controlled Access
Access is limited by project, role and task requirement to reduce unnecessary exposure.
[ 03 ]
Confidentiality
Reviewers operate under confidentiality agreements and strict data-handling expectations.
[ 04 ]
Secure Workflows
Workflows are designed to minimize uncontrolled data movement across the pipeline.
[ 05 ]
Reliable Delivery
Final outputs are packaged, reported and delivered in agreed, structured formats.
operational_pipeline
From AI output to actionable intelligence.
A structured five-step process that turns raw model output into insight you can act on.
step_ 01
Define
We understand your AI, objectives and evaluation requirements.
step_ 02
Evaluate
Qualified human evaluators assess your AI against clear criteria.
step_ 03
Verify
Our quality process checks consistency, accuracy and reviewer agreement.
step_ 04
Analyze
We identify errors, patterns, strengths and performance gaps.
step_ 05
Improve
You receive actionable insights and high-quality data to improve your AI.
// Evaluate. Verify. Improve.
novagauge_academy
Learn the skills behind better AI.
The future of AI needs people who understand how to evaluate, improve and responsibly train intelligent systems. NovaGauge Academy provides practical training to build that workforce.
- LLM Evaluation
- AI Data Annotation
- AI Training Data
- Human-in-the-Loop AI
- AI Quality Assurance
- Prompt Evaluation
- AI Safety & Red Teaming
- Certification Path
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from_the_blog
Insights & guidance.
Practical perspectives on AI evaluation, human feedback, testing and building reliable AI.
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ready_for_pilot
Put your AI to the test.
Start with a focused evaluation pilot designed around your AI system and objectives. Tell us what you're building and we'll design a custom pilot around your requirements.
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Let's build better AI.
Have an AI system that needs to be evaluated, tested or improved? Talk to NovaGauge.