In an impressive collaboration, TensorOps and Panaya have successfully launched the "SeeMore" AI Agent into production within just three weeks. Utilizing TensorOps' advanced Retrieval-Augmented Generation (RAG) approach, this production-grade AI Agent seamlessly integrates with Panaya's data sources, thanks to a tailored framework developed by TensorOps.
Navigating the landscape of technical documentation is challenging for users of large enterprise systems. Whether they are internal or external, interacting with these systems requires deep understanding and familiarity with SaaS solutions. This is why companies are eager to leverage AI. The power of Retrieval-Augmented Generation (RAG) systems, embedded into chat experiences, is significant and can help users interact naturally with the company's knowledge base.
This is the challenge Panaya aimed to solve with SeeMore, their core business assistant that helps users answer questions based on the documentation. Whether in the context of a single page or across multiple pages, Panaya's SeeMore serves as the source of truth for users.
But how do you develop such am AI Agent?
As the new generation of chatbots becomes one of the most sought-after Gen-AI foundations in the current LLMOps landscape, companies face the critical decision: build or buy. While many solutions perform well in demos, they often require significant customization for real-world applications. TensorOps addresses this challenge with a comprehensive package that combines code and professional services, ensuring the solution works effectively in your specific environment.
Key Highlights
Swift Deployment: A production-ready AI Agentic chatbot capable of assisting users in navigating Panaya’s Success Center documentation was built and launched in just three weeks.
Tailored Integration: TensorOps' solution effortlessly connected to Panaya's data sources and architecture.
Framework Advantages: The TensorOps framework ensured a smooth, fast, and flexible deployment. With this framework, any source of data can be used as a RAG with the assertive support of the development team.
Solution Scope
The AI Agentic chatbot framework by TensorOps includes:
Guardrails: Ensuring data privacy and security.
Data Integration: Effortless connection with Panaya's existing data sources.
Specific Architecture: Custom architecture tailored to Panaya's needs.
Template Flexibility: A versatile template that adapts to different architectures, providing robust and scalable solutions.
One-Stop Shop: Comprehensive end-to-end service from integration to deployment.
Conclusion
Panaya's integration of TensorOps' Agentic chatbot framework highlights the efficiency and adaptability of TensorOps' solutions. This significant achievement demonstrates TensorOps' capacity to rapidly deploy and integrate AI-driven chatbots, providing high-quality, production-ready solutions. By utilizing cutting-edge technologies and a customized framework, TensorOps effectively addresses the unique requirements of each client, ensuring swift and successful implementation.
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