RAG-Based Enterprise Knowledge Assistant
DS Enterprises — Intelligent Knowledge Discovery Platform
DS Enterprises — Intelligent Knowledge Discovery Platform
Mist Avinya Technologies LLP designed and deployed a secure, serverless Retrieval-Augmented Generation (RAG) platform on AWS, enabling DS Enterprises employees to access accurate, citation-backed internal knowledge using natural language queries.
DS Enterprises is an SMB enterprise software company that develops cloud-hosted workflow automation products for mid-market organizations. The company maintains a large internal knowledge base distributed across multiple repositories and collaboration platforms.
With approximately 350 employees across engineering, operations, and customer success teams, DS Enterprises faced growing operational inefficiencies caused by fragmented documentation spread across Confluence, SharePoint, Git repositories, and S3-hosted files.
Employees struggled to quickly locate accurate and up-to-date information, impacting onboarding speed, engineering productivity, and incident resolution workflows.
The engagement focused on improving enterprise knowledge accessibility, reducing employee search time, and delivering grounded AI-generated responses with secure access controls.
Decrease the average time employees spend searching for internal knowledge by at least 50 percent.
Reduce new-hire ramp-up duration from 6 weeks to under 4 weeks using AI-powered contextual assistance.
Provide a conversational interface capable of retrieving accurate, citation-backed answers from enterprise documentation.
Enforce role-based access control ensuring employees only retrieve information they are authorized to access.
Mist Avinya implemented a Retrieval-Augmented Generation platform using Amazon Bedrock, OpenSearch Serverless, and serverless AWS services to deliver grounded enterprise search and conversational AI capabilities.
The solution leveraged AWS-native AI, serverless, observability, and security services to build a scalable enterprise knowledge platform.
Used Claude 3.5 Sonnet for grounded conversational response generation with citation support.
Managed RAG orchestration, retrieval configuration, and integration with vector storage.
Served as the vector database supporting scalable semantic search with metadata filtering.
Generated semantic vector embeddings for documents and user queries.
Powered ingestion pipelines, chunking workflows, re-ranking logic, and API operations.
Extracted structured text from scanned PDF documents and legacy image-based content.
Security, governance, grounding validation, and enterprise-grade Responsible AI controls were integrated throughout the architecture.
The RAG-based knowledge assistant significantly improved operational productivity, onboarding efficiency, and enterprise knowledge accessibility.
The solution was designed using a fully serverless AWS-native architecture optimized for scalability, reliability, and operational efficiency.
All AWS services including OpenSearch Serverless, Lambda, Bedrock, API Gateway, Cognito, and Amazon S3 operate across multiple Availability Zones with no single point of failure.
The platform dynamically scales using serverless AWS services, OpenSearch Serverless auto-scaling, Lambda concurrency scaling, and API Gateway throttling to support high query volumes efficiently.