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HCMBench: an evaluation toolkit for hallucination correction models
HCMBench is Vectara’s open-source evaluation toolkit designed to rigorously test and compare hallucination correction models. With modular pipelines, diverse datasets, and multi-level evaluation metrics, it gives developers a powerful, standardized way to measure and improve the accuracy of RAG system outputs.
Vectara's Hallucination Corrector
AI hallucinations create significant business risks and erode user trust. Vectara's Hallucination Corrector (VHC) identifies inaccuracies, suggests fixes, and provides essential guardrails for your AI applications.
New HHEM and OpenAI chat completions endpoints
We’ve created a trusted platform for building safe and reliable AI applications and continue to invest in features that improve reliability, accuracy, and flexibility. Today, we're taking another step forward by introducing two powerful new capabilities: the Hallucination Evaluation Model (HHEM) and OpenAI Chat Completions endpoints.
Streaming truth and trust: reinventing financial services with AI and event-driven architectures
In financial services, transformation isn’t optional. It’s constant. Regulatory pressure, digital-first customers, and fractured data landscapes have created a race for real-time intelligence and faster, smarter decisions.
How Vectara is making regulatory compliance less of a headache
Every year, governments and agencies roll out thousands of pages of new rules, policies, and updates. For businesses and public institutions trying to keep up, it’s become a massive, expensive problem.
AI assistants and AI agents: The next wave transforming financial services
AI Assistants and Agents are revolutionizing financial services, offering hyper-personalized customer experiences, operational efficiencies, and enhanced risk management. Explore how Vectara’s GenAI and RAG-powered platform enables financial institutions to embrace this new wave of intelligent, autonomous technology securely and effectively.
Introducing Mockingbird 2 - Vectara’s crosslingual grounded generation model
Introducing Mockingbird 2: our latest grounded generation model optimized for RAG with advanced crosslingual support and improved performance. It runs securely in any environment—SaaS, cloud, or on-prem—delivering high-accuracy responses without data leakage risk.
Introducing Open RAG Eval: The open-source framework for comparing RAG solutions
Open RAG Eval is an open-source framework that lets teams evaluate RAG systems without needing predefined answers, making it faster and easier to compare solutions or configurations. With automated, research-backed metrics like UMBRELA and Hallucination, it brings transparency and rigor to RAG performance testing at any scale.
Towards a Gold Standard for RAG Evaluation
Vectara's new open-source framework for comprehensive RAG evaluation, open-rag-eval, represents a significant leap forward in ensuring that AI systems deliver accurate, relevant responses.
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