AI Evaluation Services
Assuring Trust, Performance & Fairness Across the AI Lifecycle
Why AI Evaluation?
AI systems behave differently from traditional software. They learn from data, adapt over time, and make probabilistic decisions. As real-world data changes, even high-performing models can degrade silently introducing bias, drift, instability, and business risk. Without continuous evaluation and quality gates, AI failures often go unnoticed until they impact customers, revenue, or compliance.
Our AI Evaluation Services Help You
- Detect model drift, bias, and performance degradation early
- Reduce operational risk across AI-driven products and platforms
- Ensure fairness, explainability, and regulatory readiness
- Build long-term trust in AI systems for business-critical use cases
End-to-End AI Quality Engineering
We embed quality assurance into every stage of your AI lifecycle—from data readiness to model deployment —so AI systems meet performance, fairness, and reliability standards.
- Requirements gathering
- Data collection and ingestion
- Data preparation and labeling
- Feature engineering
- Model selection
- Model development
- Model evaluation
- Model validation
- Deployment readiness
- Continuous monitoring
Requirements gathering
- Business objective validation
- Bias sensitivity assessment
- Success metric definition
Data Collection & Ingestion
- Data quality profiling
- Bias detection
- Schema and integrity validation
Data Preparation & Labeling
- Transformation Reproducibility
- Leakage detection
- Label consistency checks
Feature Engineering
- Feature stability analysis
- Correlation and leakage testing
Model Selection
- Performance feasibility
- Explainability assessment
- Latency and cost estimation
Model Development
- Training reproducibility
- Performance benchmarking
- Convergence monitoring
Model Evaluation
- Cross-validation
- Fairness evaluation
- Robustness testing
Model Validation
- Shadow deployment
- Bias audits
- Threshold tuning
Deployment Readiness
- Pipeline health checks
- Versioning and governance controls
Continuous Monitoring
- Drift detection
- Performance tracking
- A/B testing and rollback strategies
Key AI Evaluation Metrics
- Precision
- Recall
- F1-Score
- AUC-ROC
- Ranking Metrics (NDCG, MAP)
- Demographic Parity
- Outcome Disparity Ratios
- Exposure Balance
- Group Fairness Metrics
- User level Fairness
- Adversarial robustness
- Edge-case stability
- Error pattern analysis
- Input data drift
- Model drift
- Feature stability monitoring
- Concept drift detection and many more
Frequently Asked Questions
- AI Evaluation is the systematic assessment of AI model quality across accuracy, fairness, robustness, and drift. IGS evaluates models at every lifecycle stage from data ingestion to post-deployment monitoring, using metrics including F1-Score, AUC-ROC, NDCG, and demographic parity.
- AI models produce probabilistic outputs that degrade silently over time - a phenomenon called concept drift. Traditional software testing cannot detect this. AI testing requires specialised evaluation across accuracy, fairness, and robustness metrics, plus continuous post-deployment monitoring.
- IGS evaluates Precision, Recall, F1-Score, AUC-ROC, NDCG, MAP, Demographic Parity, Outcome Disparity Ratios, Exposure Balance, adversarial robustness, edge-case stability, concept drift, and feature stability monitoring.
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