Work

Client work built for growth, confidentiality, and measurable execution.

Most ZOAK engagements are stealth by default. We share public names only where appropriate and describe the rest by workstream.

Named client

Beacon Grove

Beacon Grove is an AI-native digital marketing services firm and a ZOAK client. The engagement covered the full operating system build:

Before ZOAK:

Undefined positioning, manual campaign workflows, no operating cadence for reporting or experimentation, and disconnected tools across sales and delivery.

What we built:

Clear market positioning and service packaging, AI-enabled content production and research workflows, structured campaign operating cadence, performance dashboards with weekly review rituals, and onboarding systems for new team members.

Operating artifact left behind:

A repeatable delivery system that functions independently of ZOAK — including playbooks, reporting templates, approval flows, and a management rhythm the team runs weekly.

Visit beacongroveweb.com

Stealth portfolio

Anonymized outcomes measured against client baselines.

Each metric below is measured as percentage improvement against the client's own pre-engagement baseline, tracked over 60–90 day engagement windows. The details stay private. The operating patterns are consistent.

+180%

Pipeline system rebuild

Reworked positioning, funnel stages, lead handling, and reporting for a B2B professional services firm. Measured as qualified opportunity volume over 90 days vs. prior 90-day baseline.

+52%

Operating throughput lift

Mapped recurring work, removed approval drag, and introduced AI-supported research and reporting workflows for a mid-market services firm. Measured as deliverables per team member per week.

+74%

Decision-cycle acceleration

Rebuilt KPI logic, dashboard review, and management cadence for a healthcare data company. Measured as average days from signal identification to executive decision.

Methodology: All percentage figures are internal improvements measured against each client's own pre-engagement baseline. They are not extrapolated, annualized, or compared to industry benchmarks. For context, the BCG × Harvard Business School study found AI-assisted knowledge workers completed 12.2% more tasks and 25.1% faster for frontier-appropriate tasks. HBS Working Paper 24-013

How work is framed

No vanity case studies. Just operating problems and measurable improvement.

What decision or workflow is slowing growth?
What data is trusted enough to manage against?
Where can AI raise speed or quality without increasing risk?
Which operating habit will make the improvement repeatable?

Private Measured Repeatable

Confidential by default

We keep client operating details off the homepage and use anonymized patterns.

Percentage lift

Work is evaluated through throughput, conversion, adoption, and decision-speed improvements — always measured against the client's own baseline.

Systems over moments

Every engagement aims to leave behind a management loop the team can keep running.

Next step

Have a growth, AI, or operating problem similar to these?

Walk us through the constraint. We'll tell you whether we're the right fit and how we'd scope the diagnostic.

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