Internal teams struggle to find relevant information quickly on demand using existing tools. Traditional enterprise search tools do not handle natural language queries and variations well leading to incorrect results. Search tools provide a laundry list of links rather than the specific answer the user is looking for, making it difficult and time-consuming. This leads to increased response time, and usage of stale information leading to compliance risks and increased manual effort. ​

FractalGPT is an End-to-end framework to build a Gen AI-powered Knowledge Assist solution, integrated with LLMs of choice, that can be deployed on any channel with multi-lingual capability. The framework supports static Knowledge QnA, and transactions/ integrations with backend enterprise systems.​ FractalGPT follows the Retrieval Augmented Generation (RAG) to extract information from documents and answer questions in natural language. We leverage the ready-made components in the framework to accelerate build and deployment.

FractalGPT allows Automatic Document Ingestion of documents e.g. PDF, Doc, etc., and automatically performs parsing, chunking, and indexing to enable semantic search/summarization. FractalGPT Knowledge Assist solution uses scalable semantic search using the power of LLMs​ to get accurate information. The solution has Built-in hallucination and quality checks e.g. Jailbreak, toxicity. The solution produces In-depth analytics to understand trending questions, Identify unanswered questions, and improve responses.​

FractalGPT is built on top of standard and highly reliable Azure components like Azure functions, Azure cognitive search, Azure Document Intelligence, Azure OpenAI APIs, and Azure Cosmos DB.

Expected Impact after Implementing FractalGPT Knowledge Assist Solution:

• 50% reduction in onboarding time of knowledge workers.​
• Up to 20% reduction in average handling time improving SLA and first-time resolution.
• Up to 30% improvement in compliance and complaints related to agents​
• Improvement in average speed of response​
• 20% improvement in agent satisfaction​
• Improvement in the quality of the knowledge base​