Internal AI Platform
(Foundry + RAG)

The firm had AI projects appearing across multiple business units at the same time. Internal copilots. Document retrieval systems. Analytics workflows. Support tooling. Prototype agents. Every team was solving the same infrastructure problems independently before they could even start solving the actual business problem.

Each new initiative started with the same cycle: provision infrastructure, wire up models, build retrieval, stand up APIs, deploy containers, handle authentication, connect data sources, create chunking pipelines, configure embeddings, test inference behavior — then rebuild large parts of it again when requirements changed. Different teams were making different architectural decisions for essentially the same underlying problem. The duplication became expensive fast. Projects sat on the roadmap for months because the infrastructure cost of starting them was too high to justify.

The larger issue was fragility. Model providers changed APIs. New models introduced different token behavior and context limits. Retrieval pipelines drifted between implementations. One model upgrade could break downstream workloads because every project had tightly coupled assumptions baked into it. Engineering time was going into maintaining infrastructure glue instead of improving AI outcomes. Teams were spending weeks building foundations that should have already existed.

AOtech designed the platform as shared internal AI infrastructure rather than a single-purpose application. The goal was straightforward: build the core AI plumbing once, make it reusable everywhere, and isolate workloads from model churn. The architecture centered around three major layers working together.

Azure AI Foundry became the orchestration and model abstraction layer. Instead of hard-coding workloads directly to specific model vendors or APIs, workloads interface with a centralized model layer capable of routing requests across different LLM providers and configurations. This made the environment model-agnostic by design. Swapping models stopped being a redevelopment project and became a controlled configuration change. Microsoft Fabric became the unified data backbone — providing a centralized analytics and data environment that feeds multiple AI workloads simultaneously without every team standing up their own ingestion and transformation stack. The platform standardized how data moved, how it was classified, and how it became retrievable inside downstream AI systems.

The shared retrieval and inference layer was built as a custom Python RAG pipeline deployed on Azure Container Apps. This became the reusable operational engine for retrieval, embeddings, chunking, vector search, orchestration, and inference workflows. Instead of rebuilding RAG infrastructure for every project, teams plugged into a common platform layer already designed for scale and multi-workload operation. Multiple AI systems now run simultaneously on top of the same core retrieval architecture without duplicating infrastructure.

The operational difference was immediate. New AI workloads no longer started from zero. Projects that had been sitting on the roadmap for months — deprioritized because the infrastructure cost of starting them was too high — shipped within two weeks. Teams moved from concept to working prototype in days because the infrastructure, retrieval, deployment, and model integration layers already existed. Engineers stopped rebuilding the same pipelines repeatedly and started focusing on the actual business logic of the workload itself.

The platform also reduced the operational risk tied to model evolution. Because workloads were abstracted from underlying providers, changing models no longer meant rewriting downstream systems. Testing new models became practical instead of disruptive. Teams could evaluate performance, latency, and cost tradeoffs without destabilizing production. When a better model became available, the platform evaluated and adopted it — individual workloads didn't need to know it happened.

Most importantly, the platform changed how AI development scaled internally. Instead of every new project multiplying infrastructure complexity, the environment compounded in value as more workloads were added. Improvements to retrieval, orchestration, monitoring, or deployment benefited every workload simultaneously. Every department in the organization now runs on the same shared RAG platform. The firm has internal AI infrastructure — not a collection of one-off pipelines that scale with headcount.