Managed AI Server - Developer Platform

A shared AI backend so developers across the organization can add AI capabilities without managing infrastructure. One platform serving RAG, document ingestion, and content generation to every team.

Developers across the organization wanted to add AI features to their applications, but each team was standing up its own infrastructure independently. The result was duplicated effort, inconsistent implementations, and no shared patterns for common capabilities like document ingestion or retrieval-augmented generation.

Challenge

Every team was solving the same infrastructure problems from scratch: choosing a vector store, building ingestion pipelines, designing API contracts, and deploying model endpoints. The inconsistency made it difficult to maintain quality or share learnings across projects. The organization needed a centralized platform that provided AI capabilities as a service without creating a bottleneck for individual teams.

What We Built

We built a centralized API server using FastAPI that provides AI capabilities to consuming applications. Features include retrieval-augmented generation with Qdrant for vector storage and PostgreSQL for metadata, document ingestion with chunking and embedding generation, and email template generation.

About 25% of the 160 hours of effort went into infrastructure: Docker, database setup, and vector store configuration. The remaining 75% went into application logic: the RAG pipeline, document ingestion workflows, template design, and API contracts. Docker-based deployment supports horizontal scaling as demand grows.

What Changed

New teams can now add AI features through a clean API without managing their own model infrastructure. Centralizing AI infrastructure reduced per-project overhead and ensured consistency. The investment pays off across every downstream application, and the platform continues to grow as new capabilities are added.