Creating AI models is just the beginning. Making sure they are trustworthy, accurate, and ready for the real world is what sets great teams apart. Here’s how we support you at every step:
How Vaikhari AI Helps You Build Better Models
Step 1: Scope & Design
We understand your AI system’s goal and use case, then design the right tests and data, covering edge cases and real-world conditions.
Step 2: Test & Diagnose
Your model is evaluated through automated tools and human reviews. We show exactly where it fails and why; saving $$$$ by avoiding costly production errors.
Step 3: Improve & Retrain
Get focused datasets and human-in-the-loop support to retrain efficiently. Save $$$$ on trial-and-error and reduce data sourcing costs.
Step 4: Certify & Compare
Once your model meets domain standards, we certify it and optionally list it on our public leaderboard for trusted comparison.
Step 5: Stay Ahead
We continuously improve metrics, data, and explainability tools so your models stay sharp, competitive, and ready for real-world use.
Why Vaikhari?
Real-world Benchmarks
Engineered for your mission-critical operations in high-stakes domains
RLHF Integration
To improve your models continuously using specialized human feedback
Certification Badges
That validate production readiness and compliance for high-stakes applications
Who needs Vaikhari?
Developers monitoring models while developing
Decision-makers driving AI excellence confidently
AI startups shipping fast without sacrificing quality
Researchers looking for consistent AI benchmarks
Enterprises evaluating AI for mission critical systems
Regulators and auditors demanding transparency
Shaped by Leaders From
Frequently Asked Questions
What specific pain points does Vaikhari solve?
1. Inconsistent evaluation standards: Vaikhari provides structured, reproducible, and transparent AI benchmarking.
2. Slow, expensive manual testing: Automates key evaluation metrics while allowing human oversight when needed.
3. Regulatory compliance risks: Includes fairness and robustness audits to meet ethical AI standards.
4. Lack of interpretability: Offers deep insights into model performance beyond raw accuracy.
What makes Vaikhari different from existing AI evaluation tools?
Unlike other evaluation platforms that are slow, manual, fragmented or overly simplistic, Vaikhari offers:
1. End-to-End Coverage: Automated and manual evaluations combined.
2. Actionable Insights: Not just numbers. but clear recommendations.
3. Deep Metrics and Datasets: Evaluates robustness, fairness, and reliability through proprietary metrics and datasets.
4. Global Language Support: In addition to major languages, covers languages from India, Thailand, Vietnam, Philippines, Latin America, and more.
5. Scalable Performance: GPU-powered, fast, and consistent.
Can Vaikhari integrate with existing MLOps workflows?
Yes, Vaikhari is designed for seamless integration with existing MLOps pipelines. It supports CLI-based access, API integrations, and a GUI-based portal for ease of use. This ensures that AI teams can evaluate models within their existing development and deployment environments without disruptions.
What types of AI models does Vaikhari support?
Currently, Vaikhari supports speech model evaluations in its Beta phase. We are actively expanding support to include text, video, multimodal and agent evaluations, with these additions expected by Q4 2025. Our goal is to become a complete evaluation platform for all AI modalities.
How can I start using Vaikhari?
Simply sign up for early access on our website and seamlessly integrate Vaikhari into your AI development and deployment workflows.
Got Questions or Interested in Early Access?
Reach out to us, and we’ll gladly discuss how you can join our early access program. Be the first to ship rock-solid AI and define the future of AI evaluation.