Expertise

AI Observability & Cost Governance

Making AI features auditable: tracing every model call with tokens, prompts, responses and USD cost, and surfacing it where owners can act on it.

Capability statement
I can make an AI product’s spend and behavior fully transparent — per-call tracing, cost attribution and an audit dashboard.

Problems I solve

Unknown or surprising AI API bills
No record of what prompt produced a bad answer
AI behavior changing silently between releases

Approach

Trace at the model-router layer so nothing escapes instrumentation
Store full prompt/response pairs, not just counts
Compute cost per call, not per invoice
Pair tracing with evaluation cases for behavioral regression

Full description

AI features fail quietly — costs drift, prompts regress, and nobody can reconstruct why an answer looked the way it did. I instrument every LLM call at the routing layer so each request records its model, token usage, full prompt and response, and computed cost. On this platform that trace log powers an admin dashboard where any AI interaction can be audited end-to-end, and evaluation cases keep reasoning behavior honest over time.

Evidence standard

Strengths

100% coverage of calls routed through the traced client — enforced by making that client the only entry point, not by developer discipline
Cost visibility down to a single feature interaction

Current focus

Extending traces into automatic anomaly flags — surfacing cost or behavior regressions without manual review.