Consulting that moves the numbers.

Refocus a struggling train on outcomes, not just ceremonies.

When a release train has come off cadence, is drifting, or is newly formed, I run the readiness diagnostic, design the relaunch and PI planning, and stand up an operating cadence your teams keep after I leave. This has included multi-team release trains at enterprise scale, working alongside the Scrum Masters, Product Owners, and process leads already in place. Certified AI-Empowered SAFe® Release Train Engineer.

What's included

• ART relaunch readiness diagnostic: working sessions, Scrum Master 1:1s, prioritized findings, and a go/no-go readiness checklist
• Quantified PI baseline established before a relaunch date is committed
• Relaunch and PI planning design, including a planning retrospective
• Coordination cadence design: fewer meetings, async-first where it fits, keeping what already works
• Jira-native risk and dependency mechanism: risk-to-objective linking, ROAM-complete workflow, portfolio-consumable reporting
• Coaching for Scrum Masters, interim RTEs, and process leads

Replace single-point promises with confidence intervals.

Cycle time, WIP, work item age, and throughput from your real Jira or Azure DevOps data, with Service Level Expectations set from your actual distributions. Monte Carlo forecasts replace single-point promises with confidence intervals. This is how CPower found a hidden upstream dependency queue and cut cycle time 42%.

What's included

• Flow metrics baseline from your existing tooling: cycle time, throughput, WIP, work item age
• Service Level Expectations set from your actual distributions, not from benchmarks
• Throughput-calculated WIP limits and explicit flow policies
• Monte Carlo forecasting for delivery dates and scope
• Demand vs. capability analysis: planned vs. unplanned work ratios, failure demand identification
• Executive dashboards connected to Jira or Azure DevOps

Reporting built for the people funding the work.

Scrum Master cohort programs covering flow metrics interpretation and executive narrative, plus reporting built for the people funding the work: flow briefings, portfolio rollups, capitalization-aware capacity views. At Hilton Grand Vacations this ran as a briefing series for Senior Directors and a cohort program across seven teams.

What's included

• Scrum Master cohort training on flow metrics interpretation and executive narrative
• Flow-Based Metrics briefing series for director and VP audiences
• Portfolio rollups and capitalization-aware capacity views
• Service Delivery Review cadence connecting flow data to leadership decisions
• Communities of Practice design and launch across QA, DevOps, Engineering, and Scrum Master groups
• Team feedback-loop, meeting, and ceremony design coaching

Structure the work, evaluate the output, stay accountable.

I operate a production software product, Zera Fit, built and maintained through AI coding agents I direct: multiple agents with enforced file-ownership boundaries, an LLM evaluation harness that gates releases in CI, and an adversarial suite mapped to the OWASP LLM Top 10, governed by 130+ numbered standing decisions. I bring that operating model to teams adopting AI agents: how to structure the work, evaluate LLM output before it ships, and stay accountable for what agents produce. I do not write most of the code; I design the system that writes it.

What's included

• Multi-agent orchestration with enforced file-ownership boundaries and a defined halt protocol
• Versioned governance: numbered standing decisions instead of tribal knowledge
• LLM evaluation harness combining deterministic scorers and LLM-as-judge, gated in CI
• Adversarial regression suite mapped to the OWASP LLM Top 10
• Cost-aware model routing with a hard monthly evaluation spend cap
• AI-assisted coaching workflows that turn flow data into retrospective prep and executive-ready narratives