End-to-End RAG Platform for Enterprise Knowledge Access
Overview
Design and delivery of an end-to-end Retrieval-Augmented Generation (RAG) platform enabling enterprises to make internal knowledge securely accessible to selected target audiences through AI-assisted interfaces.
The platform supports multiple document producers and consumers, asynchronous processing pipelines, and fine-grained access control, while operating at enterprise scale across cloud environments.
Focus areas
- Enterprise RAG platform architecture
- Asynchronous document ingestion and processing
- Vector search and embedding pipelines
- Fine-grained access control and governance
- Cloud-native, multi-cloud infrastructure
Context
Large organizations generate and maintain substantial volumes of internal documentation across departments such as legal, accounting, marketing, product, and operations. This information is often fragmented across systems and difficult to make accessible in a controlled and up-to-date manner.
The goal of this engagement was to provide a platform allowing document owners to upload and manage internal content, while enabling selected consumer groups — such as end customers, service providers, manufacturers, or employees — to access relevant information through AI-assisted search and retrieval.
Challenges
- Supporting multiple document providers and consumer groups with different access requirements
- Ingesting and processing large volumes of documents from heterogeneous storage systems in a scalable and reliable way
- Ensuring asynchronous, fault-tolerant processing of document updates
- Providing accurate vector search and AI-assisted access while enforcing fine-grained authorization rules
- Operating the platform reliably across cloud-native, distributed environments
Solution
- Designed and implemented a cloud-native RAG platform deployed on managed Kubernetes infrastructure
- Built asynchronous document ingestion and processing pipelines using ValKey (Redis-compatible) Streams for event delivery and decoupling
- Integrated multiple document storage backends, including cloud object storage, external file systems, and managed document repositories
- Implemented document chunking and embedding workflows using managed embedding models, with vectors stored in Azure AI Search
- Enabled AI-assisted access through integrated assistants capable of dynamically selecting information sources via Model Context Protocol (MCP), while enforcing access rights to specific documents
- Implemented ingress-level access control using Traefik with custom ACL middleware to protect platform endpoints and services
Technology stack
- Managed Kubernetes (AWS)
- Azure AI Search (vector storage)
- Azure Blob Storage and external document repositories
- AWS-managed SQL database
- ValKey Streams for event-driven processing
- Traefik ingress controller with custom ACL middleware
Outcome
- Production-ready enterprise RAG platform supporting multiple document producers and consumer groups
- Reliable, asynchronous document ingestion and processing at scale
- Secure, governed access to internal knowledge via vector search and AI assistants
- Flexible, multi-cloud architecture supporting future extension and integration
- Reusable platform foundations for additional AI-driven knowledge access scenarios
Why this mattered
This engagement demonstrated how enterprises can move beyond siloed document repositories toward a governed, AI-assisted knowledge platform without exposing sensitive information or relying on monolithic, proprietary solutions.
By combining asynchronous processing, vector search, and fine-grained access control, the platform enabled secure knowledge sharing across organizational boundaries while maintaining ownership, compliance, and architectural flexibility.