INDUSTRY
Telco
Your AI-Optimized Call Center.
Equip your business with conversational AI with never-before-seen accuracy and full chat history metrics.
Create AI Agents that Drive Right to the Heart of Relevancy
With Vectara’s RAG-as-a-Service, telecoms can embed GenAI quickly and easily without the risk of data or privacy violations — and without a data science or machine learning team.
Use Cases
- Conversational AI agents that synthesize scattered customer data and product documentation to improve how agents help customers
- Research and analysis platforms that help R&D new materials and novel designs faster, more efficiently, and across languages
- Multilingual question-and-answer services available 24/7 to help make sense of changing policies, regulations, and compliance
- Intelligent, multilingual AI agents that automate routine customer service or vendor questions
Conversational History for Your AI Agent
Vectara chat offers a chat history that incorporates previous chats into context for a more intelligent response. With RAG-as-a-service at its core, Vectara chat empowers developers to build a chatbot with high security, high relevance, and reasonable cost with the following key features.
Cross-Lingual Operation
Vectara breaks down every word into the meaning space, allowing you to retrieve relevant information across languages and generate answers in any language. This means users can ask a question in any language and get a response in any language, without losing the fidelity of the response.
Advanced Generation for Better RAG Performance
Mockingbird achieves the world’s leading RAG output quality, with leading hallucination mitigation capabilities, making it perfect for enterprise RAG and autonomous agent use cases. It ensures data never leaves Vectara’s secure environment and consistently outperforms major models like OpenAI’s GPT-4 and Google’s Gemini 1.5 Pro in RAG output quality, citation accuracy, multilingual performance, and structured output accuracy.
Recommended Resources
Use Case: Research & Analysis
Head of Field Engineering, Justin Hayes, demonstrates the power of Vectara's semantic search LLM on this financial reporting research use case.
Watch VideoCUSTOMER STORIES
Maqsam: “Question Answering” in Telco
Maqsam, a call center automation company, was considering Vectara to address two primary use cases. The first was to upload a customer knowledge base into Vectara and use it to guide a chat agent on what to say next in a conversation. The second use case was to store all customer call transcripts in Vectara so that advanced analysis could be performed on behalf of the business. The main competition for solving the use case was a DIY approach undertaken by the highly technical team at Maqsam, which is composed of some of Jordan’s best software engineers.
