Quentin McClellan

Sanitized public case study

StarkGrid: governed ingestion and operational evidence for artifact-heavy work.

StarkGrid is a private modular MVP/demonstration platform built by Quentin McClellan. It explores deterministic processing of digital and physical artifacts through clear module responsibilities, source-backed evidence, policy-gated orchestration, and read-only review surfaces.

Review proof snapshot Read caveats

Problem

Operational evidence is often scattered before automation even begins.

Files, scans, spreadsheets, workflow markers, and infrastructure observations can become difficult to trust when they are handled as one-off tasks. StarkGrid was built as a portfolio-safe proof artifact for a different approach: preserve identity, separate responsibilities, collect receipts, and make the current proof boundary explicit.

Architecture

Module boundaries are the product discipline.

IrisConnectors and external intake paths.
JanusIngestion gateway and artifact intake.
DemeterWatchers, segmentation, and deterministic work units.
ArgusOCR, text extraction, and structural extraction.
HermesMetadata enrichment, tagging, and classification.
MoiraiArtifact identity and lineage management.
MnemosyneRegistry, memory, and knowledge relationships.
HephaestusTransformation, vectorization pathway, and feature extraction.
HecateWorkflow orchestration and policy-gated actions.
AtlasStorage and artifact persistence.
AresDistributed compute pathway.
AthenaAnalytics, reporting, and read-only evidence review.

Validated proof snapshot

A final Rocketbook proof run produced source-backed distributed worker evidence.

The sanitized proof summary supports a controlled MVP/demo claim: StarkGrid can surface source-backed operational evidence and distributed worker visibility in a governed lab demonstration. It is not a production customer deployment claim.

Caveats and honesty boundaries

This is a sanitized public case study for a private repo.

  • The StarkGrid source repository remains private; this page is a public sanitized case study.
  • No production customer claim is made.
  • No production GPU OCR acceleration claim is made.
  • GPU OCR was intentionally not enabled for this proof path.
  • Embeddings were intentionally not enabled for this proof path.
  • Ultron GTX 1080 evidence demonstrates remote GPU-capable worker visibility, not production GPU OCR acceleration.
  • This export excludes private runtime evidence files, internal operational commands, secrets, tokens, SSH information, and machine access details.

What this demonstrates

Skills shown without overstating the proof.

Architecture judgment

Clear module ownership, deterministic processing boundaries, and evidence-aware scope control.

Platform discipline

Linux, Docker, workflow automation, source-backed summaries, and repeatable rehearsal practices.

Operator empathy

Automation framed as a way to reduce ambiguity, preserve receipts, and keep reviewable decisions visible.