Why your infra is the silent bottleneck in your AI systems?

Getting high-quality responses from an LLM is rarely a model problem; it is almost always an infrastructure problem.

Frontier models have the reasoning capabilities, but they are limited by the quality and accessibility of the context they are given. This is where Context Engineering—the intersection of RAG and Prompt Engineering—becomes the critical path.

The challenge is that enterprise context is fragmented. It's spread across DBs, SaaS platforms, and on-prem systems, varying between structured and unstructured, and heavily guarded by RBAC.

To solve the context bottleneck, I view the architecture through four pillars:

1. Connected Access: Use zero-copy federation. Access data where it lives rather than creating unfederated copies. This provides the LLM with immediate visibility.
2. Knowledge Layer: Implement entity resolution and institutional knowledge mapping on top of raw data to provide actual meaning.
3. Precision Retrieval: Prioritize data by intent, role, and policy. More context does not equal more knowledge; precision ensures relevancy.
4. Runtime Governance: Apply dynamic checks to determine if a specific data source should be queried based on the user's permissions. This makes the system defensible.

Ultimately, an AI system is only as effective as the context it can retrieve.

How are you handling context retrieval and RBAC in your current AI pipelines?