MirrorFly AI chatbot is a RAG-based custom AI chatbot solution specifically designed for developers and technical teams to build and deploy multi-modal AI chat assistants at low latency.

The solution uses modern tech stacks like LangChain and LangGraph to connect the agent with the LLM providers, structure their conversations with reasoning, and manage their state. It supports flexible data ingestion to convert the business-specific dataset into dynamic knowledge bases in PDF, CSV or TXT formats along with web syncing that the agent can understand easily. These datasets are stored in vector databases like Pinecone, FAISS, or MongoDB where the semantic meaning of the information is processed so the agent can retrieve highly accurate information.

MirrorFly's custom LLM chatbot is flexible in terms of allowing developers to choose between multiple LLM providers and assign them to specific chat agents or task agents as per their preference. It features an intuitive visual workflow builder with a drag-and-drop interface, allowing teams to define the chat flow logic, branching logic, API triggers along with custom nodes like 'Begin' and 'Response'. To ensure reliability, Contus AI includes a Retrieval Testing (RAGFlow) interface to check how effectively the agent retrieves intended data before deployment.

For voice-enabled applications, the platform integrates Deepgram for real-time speech-to-text and MirrorFly Call Services for WebRTC-based voice sessions, complete with IVR navigation and interruption sensitivity. A standout capability is the Model Context Protocol (MCP), which enables dynamic interaction with external systems like HubSpot. JWT authentication, automated data redaction, and customizable AI guardrails that restrict topics or filter PII are used for security, which is also customizable for business-specific use cases.

Along with this, the solution supports post-call analytics, sentiment scoring, and token usage tracking, making it a strong fit for AI chatbots for customer service deployments that demand transparency, reliability, and the tools necessary to build and scale high-performance conversational experiences.

References

• How to Build a Custom AI Chatbot

• Build a Custom LLM Chatbot

• AI Chatbots for Customer Service