Portfolio polish: validate end-to-end AIOps story, metrics, security, and clean-clone quickstart

[CoreyLeath-code]

Context

A senior AI engineer recommended turning the remaining production-readiness caveats into verified engineering evidence and making each flagship repository more distinctive.

Goals

• Validate the complete story from raw logs → tokenization/parsing → feature generation → clustering/anomaly detection → alerting.
• Add an architecture-flow section that explains data movement and operational boundaries.
• Measure and document latency percentiles, throughput, workload size, environment, sample count, and limitations.
• Verify unit/integration test coverage and enforce a realistic CI threshold.
• Confirm security workflows produce visible results for code, dependencies, secrets, and containers.
• Measure Docker image size and document the exact command/image tag.
• Run the quickstart from a clean clone and correct every failing or ambiguous step.
• Surface the strongest verified metrics near the top of the README.
• Add Engineering Decisions & Trade-offs, including SIMD/tokenization choices, alert-quality failure modes, false positives, and the next production step.
• Fix typos and filename inconsistencies.

Acceptance criteria

1. The documented clean-clone path runs successfully.
2. CI reports and enforces the documented coverage threshold.
3. Benchmark claims are reproducible and distinguish measured results from targets.
4. README provides a concise, project-specific end-to-end narrative.
5. Security and container evidence is visible and linked.
6. No metrics or production claims are invented.

Submit the work through a focused pull request and include validation commands/results.