AI Model Evaluations
Optimize accuracy, performance, and trust in your AI systems with expert-led model evaluations.
Unlock the True Potential of Your AI Models
Key Highlights:
- Evaluate models across precision, recall, F1-score, latency, and scalability
- Identify hidden biases and failure cases under edge conditions
- Benchmark performance against industry and domain-specific baselines
- Gain actionable insights to fine-tune and optimize models
Comprehensive Evaluation Across AI Modalities
1. Evaluation for Large Language Models (LLMs)
We assess LLMs on metrics like fluency, factual accuracy, toxicity, instruction-following, and hallucination rate. Custom prompts and domain-specific test sets are used to simulate realistic usage scenarios.
2. Evaluation for Computer Vision Models
From image classification to object detection and segmentation, we use task-specific metrics such as IoU, mAP, and top-K accuracy to validate performance under varied conditions, including low light, occlusion, and noise.
3. Evaluation for Multimodal Models
We test models that handle text, image, and audio inputs by validating cross-modal alignment, contextual consistency, and multi-turn interaction quality—ensuring outputs remain coherent and relevant.
4. Evaluation for Predictive Models (Tabular/Numeric)
For traditional machine learning or deep learning models, we assess accuracy, AUC-ROC, recall, confusion matrix, and model drift over time to maintain long-term predictive reliability.
Build Trust. Ensure Performance. Minimize Risk.
Trust & Transparency
Identify and mitigate bias, toxicity, and unintended behaviors in AI outputs before real-world exposure. Evaluate how your models treat sensitive variables like gender, race, or geography, ensuring outputs are aligned with ethical and inclusive standards.
Regulatory Compliance
Stay ahead of evolving AI governance laws and industry regulations such as the EU AI Act, GDPR, HIPAA, and SOC 2. Formal evaluations help document audit trails, risk assessments, and explainability reports, strengthening regulatory posture.
Performance Assurance
Validate your model’s ability to generalize across diverse conditions—including edge cases, low-resource inputs, adversarial attacks, and data drift. Benchmark performance using task-specific metrics (e.g., BLEU, mAP, F1, AUC-ROC) for consistent quality.
Business Impact
Reliable models drive better business outcomes. Evaluation ensures your AI systems enhance decision-making accuracy, reduce false positives/negatives, improve user satisfaction, and generate a higher return on investment across verticals like finance, healthcare, retail, and logistics.
Deployment Readiness
Understand the operational characteristics of your model—inference latency, scalability, resource consumption, and real-time behavior—so you're equipped for efficient, safe deployment in production environments.
Model Lifecycle Feedback & Continuous Improvement
AI evaluation is not a one-time event—it’s an ongoing feedback loop that fuels continuous learning and model refinement. Use evaluation results to inform retraining, fine-tuning, and prompt adjustment. Integrate evaluation into automated pipelines for adaptive systems. Enable closed-loop AI development by tying user feedback and monitoring data back into the training cycle.
Reliable, Scalable, and Tailored for Production-Ready AI
Technical Performance Evaluation
We assess core metrics like accuracy, precision, recall, F1-score, BLEU, IoU, mAP, and inference latency. Evaluations are tailored to the model type—NLP, CV, multimodal, or structured data—and benchmarked across real-world and synthetic datasets.
- Covers: Classification, Generation, Regression, Ranking, Summarization
- Tools used: Eval harnesses, prompt frameworks, API testing, unit tests
Bias and Fairness Audits
We perform sensitivity analysis, counterfactual testing, and subgroup evaluations to detect and mitigate demographic bias, stereotyping, or unintended harms. Reports include actionable recommendations for debiasing.
- Supports compliance with: Responsible AI, EU AI Act, Fairness ML
- Metrics: Equal opportunity, disparate impact, demographic parity
Robustness and Stress Testing
We simulate high-pressure scenarios such as noisy inputs, out-of-distribution data, low-resource languages, or adversarial attacks. These stress tests ensure models remain stable under uncertainty and adapt to edge cases.
- Includes: Adversarial text/image perturbations, fuzz testing, corrupted inputs
- Ideal for: Mission-critical applications, zero-shot/few-shot models
Domain-Specific Evaluation
We create custom evaluation protocols aligned with your domain—be it healthcare, legal tech, finance, or supply chain. Our experts factor in domain constraints, KPIs, and user context to validate models in their actual usage environment.
- Integrates: Domain-specific metrics, ontologies, data types
- Supports regulatory validation (e.g., HIPAA, FINRA, FDA, GDPR)
Explainability & Interpretability Reports
We help you understand why a model made a decision using tools like SHAP, LIME, Grad-CAM, and attention heatmaps. These reports improve stakeholder trust and are crucial for regulated environments.
- Helps in: Audits, Compliance, Model Debugging, Board-Level Reporting
- Formats: Visual Dashboards, Scorecards, Narrative Summaries
Benchmarking & Competitive Analysis
Compare your model’s performance against state-of-the-art baselines, open-source LLMs, or even commercial APIs like GPT-4, Claude, or Gemini. We give you a competitive edge by mapping performance gaps and strengths.
- Ideal for: Go-to-market planning, internal R&D, product validation
- Includes: Leaderboard generation, runtime comparisons, ROI estimates
A Proven Framework for Validating AI with Confidence
- Inputs: model architecture (LLM, CV, tabular), target benchmarks (latency, accuracy), compliance goals, edge-case concerns
- Outcome: a tailored evaluation blueprint outlining the tools, metrics, data strategies, and expected deliverables for maximum contextual relevance.
We build or curate high-quality test datasets that reflect your real-world operating environment.
Our datasets include a blend of real-world samples, synthetic data, and adversarial examples,
specifically engineered to assess performance across different dimensions like fairness, coverage, and resilience.
- Focus Areas: data diversity across age, gender, region, language, intent; privacy-preserving test data
- Sources: proprietary datasets, open corpora, and Vervelo-generated synthetic variations
- Objective: ensure comprehensive, unbiased evaluation coverage with controlled test conditions.
We identify quantitative and qualitative metrics based on your goals.
Whether it’s fluency in LLMs, object detection accuracy in vision models, or toxicity reduction,
our team aligns performance tracking with your functional and business KPIs.
We benchmark your model against industry baselines, previous versions, or competitor models.
- Metrics Include: F1, BLEU, ROUGE, perplexity, AUC-ROC, mAP, fairness indices, response latency, hallucination rate
- Toolkits: OpenAI Evals, Hugging Face Evaluate, AllenNLP, custom-built dashboards for stakeholder visibility
We conduct comprehensive testing combining automated tools, manual audits, and scenario-based stress simulations.
Our multi-layered testing framework includes unit-level validation, prompt response evaluations,
bias and fairness audits, robustness to adversarial inputs, and explainability checks.
- Types of Evaluations:
- Performance Testing: latency, throughput, accuracy
- Bias Audits: demographic disparity detection
- Interpretability: SHAP/LIME insights
- Robustness Checks: token drop, prompt injection, perturbation stress tests
- Deliverables: scorecards, risk maps, performance heatmaps
We deliver actionable insights and strategic recommendations in a structured, visually compelling format.
Our report breaks down your model’s strengths, failure points, compliance gaps, and optimization opportunities—
along with specific next steps for re-training, prompt refinement, or infrastructure upgrades.
- Deliverables:
- PDF reports with visualizations
- Interactive dashboards for product and engineering teams
- Executive summary for stakeholders
- Add-On: Optional debrief session with your ML/DevOps/Product teams to align roadmaps
For teams with dynamic models or frequent versioning, we implement continuous evaluation systems
within your MLOps pipelines. This allows for ongoing quality checks, drift monitoring,
and automated alerts as the model evolves.
- Tooling: MLflow, Hugging Face Hub, Google Vertex AI, LangSmith, Weights & Biases
- Integration Points: CI/CD workflows, model registries, Slack/Teams alerting
- Benefits: proactive issue detection, reproducibility, auditability, and long-term performance governance
Choose the Deployment Model That Works Best for You
1. API-Based Integration
Automated. Scalable. Developer-Friendly.
Connect our evaluation engine directly to your CI/CD or MLOps workflows using secure REST APIs and SDKs. Easily trigger evaluations, fetch performance metrics, and generate automated reports as part of your development pipeline.
Best for:
- Productized AI teams
- Continuous deployment pipelines
- Engineering-focused organizations
2. On-Premise Deployment
Private. Compliant. Fully Controlled.
Run our full evaluation suite entirely within your own infrastructure. Designed for sensitive environments where data privacy, compliance, and sovereignty are critical.
Best for:
- Healthcare, Finance, Legal, and Government sectors
- Environments with strict data governance
- VPC-isolated and container-based systems (Docker, Kubernetes)
3. Cloud-Hosted Dashboards
Visual. Interactive. No-Code Friendly.
Use our secure, browser-based dashboards to review evaluation results, explore failure cases, and share insights across teams. Perfect for decision-makers, compliance officers, and non-technical stakeholders.
Best for:
- Ad hoc model reviews
- Cross-functional collaboration
- Executive or compliance reporting
4. Embedded Workflow Integration
Seamless. Customizable. MLOps-Ready.
We integrate directly into your tools—whether you’re using notebooks, custom UIs, or platforms like Vertex AI, MLflow, or LangChain. Your evaluation process becomes a native part of your development lifecycle.
Best for:
- Teams with in-house AI platforms
- Model lifecycle management
- Full-stack AI product teams
