AI Service Framework
An engineering service framework built around large language models that provides a model abstraction layer, observability, governance and security, and cost and performance scheduling. It decouples business logic from specific models, allowing stability across model generations and changes of vendor.
Timeline (last 90 days)
A service framework needs built-in continuous monitoring of latency, throughput, cost, and output quality.
Data desensitization, access control, prompt injection protection, and output review should exist as middleware of the framework, not as after-the-fact patches.
A service framework is not a one-off project but a continuously evolving internal product that requires version management, documentation, SLAs, and an owner.
All Facts (3)
A service framework needs built-in continuous monitoring of latency, throughput, cost, and output quality.
50%UnverifiedData desensitization, access control, prompt injection protection, and output review should exist as middleware of the framework, not as after-the-fact patches.
50%UnverifiedA service framework is not a one-off project but a continuously evolving internal product that requires version management, documentation, SLAs, and an owner.
50%