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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.
Tired of your CEO's questionable tweets & "vision" talks? Introducing Agentic CEO™
The ultimate C-suite upgrade. Fire your human boss, hire an agentic CEO. Zero exuberant salary, 100% uptime, infinite buzzwords!
Introducing custom text encoders on Vectara
We’re excited to announce custom text encoders, empowering users to integrate their preferred text embedding models directly into Vectara. This enhancement offers greater flexibility and control over the text processing pipeline, accommodating various project requirements.
From data silos to RAG Sprawl: why the next AI revolution needs a standard platform
History is repeating itself in AI. Just as enterprises once built siloed databases before embracing centralized data platforms, today’s rapid adoption of Retrieval-Augmented Generation (RAG) is leading to a fragmented mess of custom-built AI systems—what we call RAG Sprawl.
Unifying enterprise AI: overcoming the RAG Sprawl challenge
RAG Sprawl—the unchecked proliferation of Retrieval-Augmented Generation implementations across your organization—is silently eroding your AI investments. This growing problem is causing headaches for CIOs and IT departments while draining resources, compromising security, and creating inconsistent user experiences.
End RAG Sprawl: The case for platform standardization
As enterprises rush to integrate Retrieval-Augmented Generation (RAG), many are encountering RAG Sprawl—a fragmented, resource-intensive approach that leads to security risks, inefficiencies, and mounting technical debt.
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