Virtual agents have turned routine informational queries into futile interactions by giving incorrect articles or generic information dumps since a long time. The inability to deliver a direct, accurate answer impacts both customer satisfaction and operational efficiency.

Why do traditional virtual agents fail?

Earlier, virtual agents relied on keyword matching for fetching relevant content and struggled to provide crisp and accurate responses. Their limitations often created a frustrating experience for customers seeking information. The gaps showed up in a few predictable ways:

• Limited understanding of customer intent: Traditional models were built to retrieve, not to interpret what customer actually meant. So, instead of providing a direct answer, they often returned a list of “maybe relevant” documents, forcing customers to sift through content to find the response they need.
• Inability to converse with the user: Legacy agents struggled to engage in natural human-like conversations. They couldn't summarize complex information from dense documents or extract step-by-step instructions from manuals. In troubleshooting scenarios, they'd often dump information rather than asking clarifying questions to diagnose the problem effectively.
• Lack of support for voice: Voice interactions demand concise and context-aware responses. Traditional systems were never equipped to condense knowledge for spoken delivery or maintain conversational context. Due to this, the voice channel was never automated.

The Strategic Fix: Smarter handling of customer queries

Level AI has built a differentiated approach to knowledge management and retrieval, transforming how virtual agents interact with customers. Our system moves beyond the limitations of legacy chatbots, delivering accurate and context-aware responses.

Our approach is made up of key building blocks that include:

Retrieval-Augmented Generation (RAG)

Imagine an expert researcher meticulously combing through a vast, organized library to find the most relevant facts, then handing them to a brilliant, articulate scholar who transforms them into a clear, contextual answer. That’s the essence of RAG. Our RAG based architecture uses semantic search to retrieve the right information and then leverages an LLM to generate a final response grounded in that retrieved context.

Vector-Based Knowledge Management

Level AI utilizes modern vector databases to store knowledge in an optimized, chunked format. When documents are ingested, they are broken into meaningful segments and converted into vector embeddings. This structure improves relevance because the system retrieves the exact portion of information tied to the user’s question—not entire articles.

Conversational Flow to diagnose user problems

Our virtual agent goes beyond summarizing information, it actively engages in conversation with the user to resolve their issues. It surfaces the root cause of an issue by asking clarifying questions. Thus, the virtual agent works with the user step by step to reach a resolution. This ensures that the users never feel stuck or abandoned.

Unified Knowledge Ingestion and Synchronization

We simplify the process of unifying all organizational knowledge into a single system. Information from diverse content sources - public website links, PDF files, Content management systems like Zendesk, Confluence and Google Drive can be seamlessly ingested. Automated syncing and refresh schedules ensure that this knowledge always remains up-to-date with minimal need for manual maintenance.

Ensuring Consistent Quality Through Automated Evaluation

To overcome the inherent unpredictability of AI, Level AI employs automated evaluations. Using a combination of customer inputs and an inhouse simulation framework, we generate realistic queries for testing. The response accuracy for these queries is benchmarked and the system is fine tuned until we achieve an accuracy of >90%. This continuous testing ensures high-quality responses across a wide variety of informational queries.

The Bigger Picture: Empowering CX with Intelligent Answers

AI in CX is entering into an era of autonomous resolution. The breakthrough isn’t another chatbot-it’s turning enterprise knowledge into a reliable system that fuels itself. Level AI pairs high-precision retrieval with LLM reasoning and rigorous evaluation so responses stay grounded in your sources, get delivered via intuitive chat and voice conversations, and are continuously validated. This reliability is what lets support scale, resulting in more issues getting resolved via self-serve, fewer tickets bounce between teams, and significantly lower time to first response - all without the experience feeling impersonal. With >90% response accuracy and 25–30% lower operational costs, Level AI turns support from a cost center into an intelligent, self-directed system.

Talk to us to turn your support into a reliable CX engine that drives growth →