What does a typical engagement look like?

Most engagements start with a 60-minute intro call, followed by a short discovery phase (1–2 weeks) where we map your current workflows, identify the highest-leverage AI intervention points, and prioritize. Then we move into an execution sprint. Some clients stay on for ongoing system maintenance and iteration.

Who do you work best with?

B2B companies (SaaS, tech, professional services) with an existing marketing function that’s ready to move beyond content production and toward system-level leverage. You don’t need to have AI in place — most clients start without it. You do need to have a growth problem worth solving.

How do you think about where AI fits?

In three layers, worked in that order: workflows, then agents, then tools. Most teams buy AI at the tool layer, because that’s the layer you can purchase — and it’s the one with the least leverage. The compounding returns sit in the workflow layer, where strategy lives, and the agent layer, where AI carries the judgment steps inside a workflow. Getting the order right matters more than getting the tool right.

Are you available for full-time roles?

I’m currently Head of Marketing at GoodVision, so I’m not available for full-time employment. If you’re looking for a fractional CMO or a consulting engagement, let’s talk.

Available for select engagements

Build the system that works automatically.

Most B2B marketing teams are running the same playbook they were running in 2022, with AI tools bolted on the side. The teams pulling ahead aren’t buying more tools — they’re redesigning the system the tools live in. That’s the work I do.

Start with an intro call

The operating model

Three layers, worked in order.

I think about AI-augmented marketing in three layers — and most teams are working them in the wrong order. Start at the top and the returns compound; start at the bottom and you just collect subscriptions.

Workflows

The repeatable sequences that produce outcomes — a campaign, a nurture track, a product launch. This is where strategy lives, and where almost no AI investment goes. Start here.

Agents

The AI processes that execute judgment tasks inside a workflow — lead scoring, content routing, pipeline triage. Agents turn workflows from batch processes into real-time systems.

Tools

The SaaS surface area: HubSpot, Apollo.io, Claude Code, Webflow. The most visible layer and the easiest to buy — but the least important to get right first. Tools without a workflow are just more subscriptions.

Where the business stands

Crawl. Walk. Run.

The three layers are how you build the system. This is the other axis: how far it’s actually gotten. Each stage has its own job — context, then signal, then loops — and they’re not stages you graduate from once. You cycle through them every time you open a new channel, market or product line.

Crawl — Context before campaigns

Everything looks too big, and every channel looks urgent. That’s shiny object syndrome, the default state for most teams: no clear ICP, no positioning, chasing tactics instead of clarity. The move here isn’t a new campaign. It’s mapping who you actually sell to, naming the growth paths worth testing, and setting a handful of objectives you can measure. Context first, channels later.

Walk — Signal over guesswork

You know your ICP now: you can name who you sell to and how you talk to them. What you don’t know yet is which channels hold up under real spend, or what breaks once you push volume. This is where you test: a few channels, real budget, real tracking. Inbound is arriving more regularly, and the data you started collecting in Crawl finally shows a pattern. You’re forming real hypotheses about what scales, not hoping the next campaign works.

Run — Loops, not campaigns

Positioning and tracking are solid, so you run: multiple channels, multiple offers, constant testing. You measure every loop by what it costs to acquire and what it returns. You’ll go further than expected, and that’s exactly when discipline matters most. Not every channel is worth scaling. Pick the ones that compound into loops. Cut the rest before they cost more than they return.

Track record

Results from the last eight years.

60% YoY pipeline growth GoodVision

Built by wiring Apollo.io, HubSpot, Claude Code, and Webflow into one workflow.
0 to 100K+ installs Medfox Digital

A digital-health app, scaled from launch.
8+ years in B2B SaaS marketing GoodVision, STRV, GroupM/WPP, Škoda Auto

Across in-house teams, agencies, and enterprise brands.

Frequently asked questions

What does a typical engagement look like?: Most engagements start with a 60-minute intro call, followed by a short discovery phase (1–2 weeks) where we map your current workflows, identify the highest-leverage AI intervention points, and prioritize. Then we move into an execution sprint. Some clients stay on for ongoing system maintenance and iteration.
Who do you work best with?: B2B companies (SaaS, tech, professional services) with an existing marketing function that’s ready to move beyond content production and toward system-level leverage. You don’t need to have AI in place — most clients start without it. You do need to have a growth problem worth solving.
How do you think about where AI fits?: In three layers, worked in that order: workflows, then agents, then tools. Most teams buy AI at the tool layer, because that’s the layer you can purchase — and it’s the one with the least leverage. The compounding returns sit in the workflow layer, where strategy lives, and the agent layer, where AI carries the judgment steps inside a workflow. Getting the order right matters more than getting the tool right.
Are you available for full-time roles?: I’m currently Head of Marketing at GoodVision, so I’m not available for full-time employment. If you’re looking for a fractional CMO or a consulting engagement, let’s talk.

Let’s talk.

If you’re building a B2B marketing function that needs to compound, send me an email. I’ll respond within two business days.

Send an email Or find me on LinkedIn.