Case StudyDoc Chat
Architecting a scalable, multi-tenant AI automation platform.
Doc Chat allows users to deeply query their documents using AI. The core engineering challenge was establishing a secure, multi-tenant architecture where data isolation and inference speed were guaranteed.
Overview
Secure AI interactions with absolute data boundaries.
Allowing corporate clients to query their private documents via AI introduces significant data leakage risks if not properly isolated.
I led the platform architecture to ensure strict multi-tenancy. By decoupling the vector databases and employing robust orchestration, the system provides fast, accurate AI insights without ever crossing tenant boundaries.
Architecture
Providing AI insights across different organizations requires absolute data boundaries. I designed the platform using a strict multi-tenant architecture, separating tenant vector databases and applying rigorous authentication controls.
The infrastructure was orchestrated using a comprehensive `docker-compose` ecosystem. Background workers handle the heavy lifting of document parsing and embedding, while the Astro-based admin dashboard provides blazing-fast tenant management. This decoupled approach ensures that heavy AI processing never blocks web requests.
Outcome
- Architected a strict multi-tenant environment ensuring complete data isolation between corporate clients.
- Built a robust docker-compose orchestration for backend workers, AI processing, and an Astro-based frontend.
- Deployed a lightweight, embeddable chatbot widget that securely interfaces with the central API.