> forward_deployed
LLM Observability Layer
An LLM system without observability is a demo that has not failed yet. In forward deployed work the observability layer is also a trust surface: it is what the customer's stakeholders look at when they decide whether the system stays.
> what this looks like in practice
▸Engineered the LLM observability layer for the AI-Infused QE Platform: every call traced with model, tokens, latency, and tenant attribution, so cost questions have answers instead of estimates.
▸Built quality evaluation into the same layer: Acceptance Criteria Coverage, Test Design Coverage, and RAGAS run as living metrics, not a one-time benchmark.
▸Customer stakeholders use this telemetry live to track spend and output quality. That visibility is what turned the platform from a tool teams tried into a platform 17 QA teams adopted.
▸The same discipline carries into WealthOS: every agent decision is logged, graded against outcomes, and feeds a calibration score that adjusts each agent's future vote weight.
> where it shows up
The AI-Infused QE Platform (production observability and FinOps) and WealthOS (decision grading and agent calibration by design).