Empowering Google Drive Search with AI-Powered Knowledge Graph QA

Kagen AI developed a Conversational AI-powered Knowledge Graph QA System that seamlessly integrates with Google Drive. This AI-driven solution enables enterprises to retrieve information using natural language queries while ensuring data security and integrity without requiring data migration.

40% Reduction in Search Time
98% Query Accuracy Rate
100% Real-time Sync Reliability

Business Requirements

Our large enterprise client faced challenges managing vast amounts of unstructured data stored across Google Drive. Traditional document search methods were inefficient, leading to lost productivity and difficulty retrieving relevant information. The company needed:

Direct access without requiring data migration.
Convert unstructured documents into a structured, searchable knowledge base.
Ensure the system reflects the latest document versions while maintaining history.
Implement strict access controls and authentication mechanisms.
Provide contextually relevant responses based on document relationships and previous interactions.

To overcome these challenges, they sought a Conversational AI solution capable of transforming unstructured documents into actionable knowledge, improving accessibility, and ensuring robust security measures.

Solution

Real-Time Voice Interaction

Implemented Twilio for seamless speech-to-text (STT) and text-to-speech (TTS) conversion.
Established an automated outbound calling system to initiate reminders and handle inbound queries.
Leveraged WebSocket for real-time audio streaming and communication.

Context Management

Integrated Redis stores and retrieves conversation history, ensuring continuity in multi-turn interactions.
Utilized vector databases (Weaviate) to match user queries with prior conversations for enhanced context awareness.

Natural Language Processing (NLP)

Integrated OpenAI’s LLM to process voice-to-text inputs and generate human-like responses.
Fine-tuned AI models for domain-specific conversations, ensuring relevant and accurate responses to associates.

Query Processing and Response Generation

Combined vector search with LLM to dynamically generate answers based on prior interactions.
Implemented caching mechanisms for frequently asked questions to reduce response time.
Used response templates to ensure consistency and clarity across different scenarios.

Query Processing and Response Generation

Combined vector search with LLM to dynamically generate answers based on prior interactions.
Implemented caching mechanisms for frequently asked questions to reduce response time.
Used response templates to ensure consistency and clarity across different scenarios.