After 28 years in contact-centre operations and workforce management, I build practical systems for operational problems that generic software usually hides.
My work is moving from individual automation tools toward Helix Codex: an accountable AI operating organization that understands business context, coordinates governed workflows, remembers decisions and outcomes, and improves through evidence without silently taking control.
The current proving foundation is Helix Prime.
Verified Phase 1 status:
- controlled-pilot ready;
- 445 tests passing;
- governance and security checks passing;
- synthetic call-centre and restaurant demonstrations;
- read-only Zendesk, Salesforce, and Clay connector contracts;
- governed memory and evidence-gated improvement proposals;
- local-first and cloud-ready interfaces;
- production remains NOT_READY until external evidence and human approval exist.
This is a controlled-pilot prototype, not a production platform or universal AI employee.
| Project | What it demonstrates | Status |
|---|---|---|
| Helix Prime | Governed operations core, cockpit, workflows, memory, evidence, and capability packs | Controlled-pilot ready |
| Helix Education | Event-sourced learning state and citation-grounded content | Alpha |
| Study Studio | Local AI learning and audio workflows | Actively used; local-first |
| L&D Command Center | Desktop learning, language, career, and media workstation | V1 ship in progress |
| LIVE Support Assistant | Small explainable browser support prototype | Portfolio prototype |
| WFM Forecasting Calculator | Erlang C workforce-planning foundation | Reference precursor |
- Local-first development; cloud only when customer value justifies it.
- Governance before autonomy.
- Evidence before claims.
- Human authority at consequential boundaries.
- Memory with provenance, classification, and tenant isolation.
- Improvement through evaluated proposals, never silent self-modification.
- Small, measurable pilots before large infrastructure.
I am building toward Founder/CTO work at the intersection of operations strategy, customer-success engineering, AI workflow architecture, security and governance, local and cloud systems, and measurable business outcomes.
The portfolio is intentionally honest: it shows what is verified, what is in progress, and what still requires a real design partner.
MIT
I'm open to senior AI-engineer, ML-platform, and founder-advisor roles where governed, evidence-gated automation matters — especially in contact-centre operations, workforce management, and learning systems. I value teams that treat honest "not-ready" signals, auditability, and local-first privacy as features, not blockers. If you're building accountable AI for real operations, I'd like to talk.