Platforms in production.

Five bodies of platform work — plus the open-source proof — written the way it actually happens: constraints first, numbers where they're real, diagrams instead of proprietary detail. Together they are one system — the architecture set the foundation, the streams feed the lakehouse, the lakehouse feeds the semantic layer, and the whole thing is observed as code.

01 · Flagship · AI platform

The Claude plugin marketplace

Spearheaded, architected, and run: an enterprise marketplace of 12 Claude plugins with authored domain skills spanning six business domains, enabled company-wide as the semantic-analytics solution. ~200 monthly active users — 40% of the company, five C-suite regulars — run 2K+ queries a month at 30% lower token consumption than unassisted LLM use, assembling QBRs, reserve reviews, financial reports, and canonical dashboards. I own the roadmap, the model evaluations, the governance, and the support.

Read the case study →
Plugins12
Business domains6
Monthly users~200 · 40% of company
02 · AI & semantic analytics

Semantic analytics in production

A production Claude + Snowflake Cortex layer giving Claims, Policy & Underwriting, Finance, and Legal natural-language analytics over governed data. The quarterly reserve review went from 3 days to under 30 minutes, QBR prep from 4 days to under an hour — with consistent regulatory reporting and cross-domain metrics (policy erosion among them) that were previously impractical.

Read the case study →
Reserve review3 days → 30 min
Domains in production4
StackClaude · Cortex · dbt
03 · Streaming platforms

The CDC platform rescue

Inherited a half-built replication platform mid-build, shipped it end to end, then re-architected it: Azure Event Hubs → Apache Kafka (Strimzi on Kubernetes) — Debezium CDC connectors across SQL Server, PostgreSQL, and Oracle (LogMiner), plus custom Java source connectors where none existed. The event-driven backbone now feeds every downstream domain, with type-2 period-fact modeling enabling historical questions, Dagster sensors alerting policy teams inside their decision window, platform-wide GitOps delivery via Argo CD and Helm, and PySpark compaction jobs that keep Apache Iceberg tables query-efficient as CDC writes accumulate.

Platform cost cut$30K+/yr
OpsHelm · ArgoCD · GitOps
04 · Observability

Observability as code

Zero dashboards built in a UI: Datadog and Prometheus dashboards and monitors ship exclusively through GitHub Actions CI/CD. Forecast monitors predict OOM and strain — 5 OOM incidents prevented and ~20% infra cost avoided by scaling on signal instead of overprovisioning. Observability run as FinOps.

As code60+ dashboards/monitors
Scalingon signal, not fear
05 · Enterprise architecture

The data architecture foundation

Before the streams and the semantic layer, the foundation: served as reporting architect on the core design committee that designed the enterprise data architecture end to end — data lake, EDW, and the reporting layer — and helped build the reporting solution the company scaled on, with core reports for Underwriting, Finance, Claims, and Operations. I run the BI tenant today, so the governed number is the same in every surface that shows it.

Scopelake · EDW · reporting
StewardshipBI tenant, run today
Outcomescaled with the company
06 · Open source

Waybill — bring receipts

An open-source Claude Code plugin for token accounting on AI-assisted work: it deterministically attributes agent token spend to the work items that shipped — metered from local transcripts and git history, never estimated — with evidence tiers, sealed pre-registered estimates, per-session conservation checks, and a verification pack the recipient can re-run offline. Public code, public docs, CI on every commit: the quality bar is inspectable.

github.com/Jakeintech/waybill ↗
Attributionmetered, not estimated
Integrityverifiable offline
LicenseMIT · plugin marketplace

Want the detail behind any of these — architecture, tradeoffs, costs? That's a good first conversation.

Email info@jakeawilliams.com