I engineer high-scale web platforms and the autonomous systems that power them.
From multi-model AI orchestration to zero-trust cloud infrastructure, I build deterministic architectures that eliminate operational drift. My work spans edge runtimes, secure multi-tenant boundaries, fault-tolerant distributed storage, and autonomous pipelines that ship code safely. Explore my live systems blueprint to see how it fits together.
Live systems
Deployed and running right now.
System infrastructure
My production cloud
Fourteen edge Workers, each on its own database and stage, backed by a hybrid byte store that bridges on-prem servers and object storage. AI agents drive an autonomous runner through the whole pipeline: it claims a task, passes it through the gates and ships the code.
Products
My commercial SaaS product suite
I build and operate specialized web platforms that help developers and enterprise data engineers solve hard, real-world data problems:
- Orbgem.com
A hosted knowledge network that turns flat documentation into an active reference system. Through an open MCP server and your choice of Claude, GPT or Gemini, it surfaces the context you need when you need it, so a note comes back the moment it matters without you ever organizing it.
- Orbslate.com
An agentic operations engine that puts your AI inside production infrastructure: Kubernetes clusters (Rancher and OpenShift among them), Datadog, Dynatrace and Harness. It reads logs and metrics, correlates them across systems and troubleshoots in real time, acting only where your role allows and auditing every call.
- Orbstrata.com
An enterprise data mesh engine built on a virtual semantic map. It reflects scattered schemas where they live (PostgreSQL, MySQL, SQL Server, Oracle and more), so an AI agent can query across databases in real time and stitch one customer's history together from every source, without a costly, risky data migration.
Practice
How I build ecosystems you can count on
I keep AI hallucinations out of production with strict, mandatory deployment guardrails. Every batch an autonomous agent tries to land must pass gates I write, which fail on a leaked name, a hard-coded color or a broken test. A failure mode, once found, becomes an automated check, so agentic work comes out predictable and production-grade.
Competence
What I am good at.
Four things, each proven in production rather than claimed.
High-throughput ingress
Tuned reverse-proxy and autoscaling paths that keep a public gateway answering at hundreds of millions of requests a day, with autoscalers that watch the signal that fails first, like an event loop's lag, and the telemetry to prove where every millisecond went.
Data and tenant boundaries
Multi-tenant stores where isolation is a function every query passes through, access granted from pre-approved roles with every step on the record, exports that need no vendor, and backups that have been restored, not just taken.
Payments and ledgers
Payment processors and fraud dashboards at ten million transactions, built by someone with a Master of Accountancy who reads a system the way an auditor reads a ledger.
Agentic engineering
MCP servers, custom chatbots, three products built for AI agents to work through, and an unattended runner that turns a queue of tasks into deployed code, with every failure mode converted into a gate.
Forensics ledger
Stories from production.
The scale, the failure, the diagnosis, the fix. Everything but the employer.
- 1B+ req/dayCore ingress at a billion daily operationsOwning the architecture and stability of a 30-service estate, with one public gateway at 450 million requests a day.
- OOM killThe invisible pod kill-signalFinding an undocumented throughput regression inside a monitoring agent another team had injected into our pods.
- 10M+ paymentsPayment rails and Tier 1 PCIHigh-volume cryptocurrency and card processing microservices, and the fraud tooling around them.