Flight Deck: email triage project overview
A project overview of an email-triage design for classification, routing, draft preparation, and human-review paths. Outcome metrics are omitted where the portfolio repository does not contain a dated measurement artifact.
Project context
Email triage and response-drafting workflow.
My contribution
Architecture, model workflow, integrations, and operational design described in this case study.
Inspectable evidence
Public portfolio source and a UI visual are inspectable. The older metric and architecture illustrations repeat case-study assertions and are not treated as independent measurement evidence.
Boundary
No dated dataset with a denominator, window, baseline, and deployment context is attached here for prior throughput or outcome figures.
Project overview
Flight Deck is presented as an email-triage and response-drafting project. The documented design intent is to ingest incoming messages, classify and route them, prepare drafts, and surface work for human review or escalation. This page describes that engineering shape without inferring a client deployment or publishing outcome numbers that cannot be traced to a dated measurement artifact.
Documented control points
The source write-up describes confidence-aware routing, human review for uncertain or decision-sensitive work, prompt/version controls, evaluation, staged rollout, auditability, and fallback paths as design controls. Those statements are presented as the project’s documented engineering intent; the portfolio repository does not contain a separate runtime evidence bundle that would justify turning them into quantified outcome claims.
Measurement and assurance boundary
The repository does not contain an independent dated dataset for the previously published daily-volume, routing-rate, guardrail/misroute, noise-reduction, response-time, or customer-outcome figures. Those numbers are therefore omitted. The same evidence gap prevents blanket project-specific claims of GDPR compliance, EU-only residency, no training, or an automatic learning mechanism. Human review by itself is not evidence that a model is being trained or automatically learning.