Generative AI
Test and Evaluation
Platform
Continuous Adversarial Testing and Evaluation for Model Safety
Developed for data scientists and engineers, Innodata’s Generative AI Test and Evaluation Platform provides continuous, automated safety and risk evaluations, helping to identify and protect against bias, hallucinations, security vulnerabilities, and compliance issues. Safety and risk testing isn’t just a step, it’s the cornerstone of responsible deployment. Don’t let unaddressed risks undermine your model’s potential; start with a foundation of safety.
Model Scoring
Continuously score model performance for safety and progress.
Risk Mitigation
Identify model weaknesses with ease and utilize custom datasets to retrain for improved performance
Human + Automated Evaluation
Assess your AI’s performance against real-world scenarios.
Red Teaming Services
Test your AI with both human-led and automated red teaming at a fraction of the cost of traditional testing and at scale.
Benchmarking for Base Model Selection
Selecting the right base model is an important first step in building high-performing, safe AI products. Utilize Innodata’s pre-built, domain-specific prompt libraries to benchmark against popular open-source models to select the ideal model for your specific use case and safety requirements.
Continuous System Testing for Model Performance Scoring
Configure automated testing to your requirements and receive actionable metrics about how your model is performing.
Monitor your model’s overall score to proactively identify and address potential safety issues, pinpoint areas where your model can be improved, and track your model’s progress over time.
Trend Analysis
Stay informed on how your models evolve with real-time trend analysis. Trend scores track results over time, allowing comparison of model versions. By monitoring version trends, you can detect and mitigate potential issues right away.
Track Model Risk Distribution
Get a clear view of your model’s risk profile with our comprehensive risk analysis. Easily assess risk distribution to pinpoint vulnerabilities and compare multiple models or versions side by side. Quickly
understand the right risk mitigation data needed for further model training.
Evaluation and Risk Mitigation Managed Services
Custom Training Data for Supervised Fine-Tuning & Human Preference Optimization.
A perfect pairing to the Generative AI Platform. Execute targeted fine-tuning with high-quality, domain-specific training data to eliminate the areas of model risks iden- tified by the platform. By leveraging Innodata’s curated custom training datasets, models can be optimized to align with enterprise needs, regulatory requirements, and ethical standards.
Model Evaluations:
Human + automated assessments provide necessary insights into a model’s real-world performance and identify potential for harm.
Complex Data for Fine-Tuning:
Prompt/response datasets, chain-of-thought reasoning, in-context learning, Q&A, and multi-turn dialog.
HPO Rankings for RLHF + DPO:
Leverage human evaluations for efficient hyperparameter optimization in reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO).
Multimodal:
Our expert LLM evaluators specialize in building high- quality datasets tailored for text, image, audio, and video models to suit any need.
How it Works
Innodata provides white-glove platform implementation service and dedicated client support for custom configuration to get your tests up and running smoothly.
Model Integration
Provide your API details and we will connect your Generative AI model to the Test + Evaluation platform.
Configure Testing Parameters
Specify your desired testing scenarios and initiate testing.
Analyze the Results
Gain insights into your AI’s performance and identify areas for improvement across risk vectors such as bias, accuracy, and data leakage.
Improve Performance
Improve your model with Innodata’s in-house, expert-driven, custom retraining data for fine-tuning and RLHF.
The Importance of Continuous Testing + Training for GenAI Models.
To meet regulations such as the California Generative AI Accountability Act (SB 896, 2024), the California Safe and Secure Innovation for Frontier AI Models (SB 1047, 2024), the Executive Order on AI (2023), and the White House Blueprint for an AI Bill of Rights (2022), AI systems require continuous monitoring and independent evaluation.
Brand Reputational Damage
Customer Loss From Negative Experience
Financial Fines, Penalties, + Other Legal Action
Operational Disruptions + Security Breaches
Regulatory + Non-Compliance
Real-World Incidents.
AI Customer Service Failures and Passenger Frustration
AI-Generated Financial Content and Credibility Issues
AI Bias in Recruitment and Discriminatory Hiring Practices
AI Chatbot Missteps and Inappropriate Responses to Minors
Why
Choose Us?
Industry-Leading Expertise
Innodata brings +35 years of data engineering expertise and a trained red teaming division that focuses on AI safety and compliance.
Client-Focused Approach
We work closely with our clients to understand their specific needs and challenges.
Comprehensive Testing Capabilities
We offer a robust testing methodology to address a comprehensive taxonomy of risk factors.
Commitment to Ethical AI
We believe in the responsible development and deployment of AI.
Schedule a Demo
Discover how continuous testing and evaluation uncovers vulnerabilities and actionable insights for your AI models.
CASE STUDIES
Success Stories
See how top companies are transforming their AI initiatives with Innodata’s comprehensive solutions and platforms. Ready to be our next success story?
