Services
I own the layer between marketing strategy and revenue.
Three connected practices, delivered as a fixed-scope audit, a build project, or a fractional retainer. We pick the format that matches the problem.
AI GTM Transformation
Put AI into the work, not the slideware
I embed AI and LLM agents into the everyday operations of your go-to-market team — triaging requests, enforcing governance, and turning briefs into launched campaigns — so the work gets faster and more consistent, not just more talked-about.
The problem
Most "AI strategy" stops at a deck. Meanwhile your ops team still hand-builds campaigns, chases UTM typos, and answers the same Slack questions ten times a day. The opportunity isn't a chatbot bolted on the side — it's AI wired into the systems where the work actually happens.
Tools
What is AI GTM transformation?
AI GTM transformation means embedding AI directly into the systems your go-to-market team runs on — campaign operations, lead routing, tracking governance, internal support — so the machine does the repetitive work and people do the judgment work. It is not a strategy deck or a standalone chatbot. It is production tooling: agents that triage requests in Slack, interfaces that enforce your campaign taxonomy, and pipelines that turn a brief into a launched program. The test is simple — after the engagement, is the everyday work measurably faster and more consistent? If the AI only lives in a slide, it hasn’t transformed anything.
Where AI actually moves the needle in GTM
The highest-leverage places are the ones nobody puts on a roadmap: the 200 small, repeated operational tasks that quietly eat a team’s week. Request triage, UTM and campaign-link governance, QA on campaign setup, first-draft segmentation, and brief-to-launch execution are all well-suited to AI agents because they are high-volume, rule-bound, and currently manual. I start where the time is actually going — not where the hype is loudest.
How I work
I begin with an audit of your workflows and data to find the agent-shaped problems, then ship one production agent end to end so the value is concrete before we scale. Agents are built on an extensible skill framework your team can grow, with governance baked in so speed never comes at the cost of clean data or broken taxonomy.
Outcomes
- → Repetitive ops work (campaign setup, link governance, request triage) handled by agents
- → A brief-to-launch pipeline that turns a request into a live program and page
- → Org-wide guardrails so AI speeds the team up without breaking taxonomy or data
- → Hours per week returned to strategic work instead of manual execution
Deliverables
- · AI-readiness audit of your GTM workflows and data
- · One or more production AI agents (request triage, conversational tooling, brief-to-launch)
- · Prompt + skill frameworks your team can extend
- · Runbooks, governance rules, and team enablement
Marketing Operations & Martech Architecture
Run Marketo, Salesforce & HubSpot as one system
I architect and operate the full martech stack — automation platform, CRM, data pipelines, lead lifecycle, scoring, and attribution — so marketing strategy turns into pipeline you can measure and trust.
The problem
Marketing ops is often a pile of disconnected tools held together by manual work and tribal knowledge. Leads fall through routing gaps, attribution nobody believes, scoring that hasn't been touched in a year, and a Marketo–Salesforce sync that breaks quietly. The result is a team that can't move fast or report with confidence.
Tools
What does a marketing operations engagement cover?
It covers the connective tissue between marketing strategy and revenue: the automation platform (Marketo or HubSpot), the CRM (Salesforce), the lead lifecycle that moves a person from unknown to closed, the scoring that decides who sales calls, the attribution that explains what worked, and the data pipelines that keep all of it accurate. I own this layer end to end — as one system, not a collection of tools — and I write the Python, SQL, and JavaScript to connect things when native features fall short. The goal is a stack your team can run fast on and a set of numbers leadership can actually trust.
Marketo, Salesforce, or HubSpot — which do you need?
The right platform depends on motion, team, and data, not vendor marketing. HubSpot wins on speed-to-value and a unified data model for smaller or sales-led teams; Marketo plus Salesforce wins on flexibility and scale for complex, data-heavy B2B motions. I’ve built and migrated both directions and have no stake in the answer — I’ll recommend the stack that fits your go-to-market, then make the migration boring and incident-free.
What you can expect
Engagements typically start with an audit that maps the current stack and surfaces the gaps costing you pipeline. From there we rebuild the lifecycle, scoring, routing, and reporting in priority order, and stand up the data and enrichment pipelines that keep records complete. You end with documented, durable systems — not a dependency on me.
Outcomes
- → A lead lifecycle and scoring model that sales actually trusts
- → Marketo/HubSpot and Salesforce operating as one coherent system
- → Attribution and pipeline reporting leadership can make decisions on
- → Data and enrichment pipelines that keep records clean and complete
Deliverables
- · Full martech stack audit and architecture map
- · Lifecycle, lead scoring, and routing rebuild
- · Multi-touch attribution and pipeline reporting
- · Data enrichment waterfall and CRM hygiene automation
- · Vendor strategy: evaluation, selection, negotiation, and migrations
Tracking, Analytics & Compliance
Trustworthy data, captured compliantly
I build the measurement layer — Google Tag Manager, GA4, server-side tracking, consent, and offline-conversion pipelines — so you can capture the data you need to optimize spend without tripping over privacy regulation.
The problem
Tracking is where marketing data quietly goes wrong. Tags fire twice or not at all, consent isn't wired correctly, GA4 doesn't match the CRM, and ad platforms optimize on incomplete signal. Then a privacy review (GDPR, DMA, Consent Mode v2) lands and the whole setup has to be rebuilt under deadline.
Tools
What does this service fix?
It fixes the gap between “we have tracking” and “we trust our numbers.” I rebuild the measurement layer so events fire once and correctly, consent is enforced the way regulators expect, and the signal reaching your ad platforms is complete enough to optimize on. That includes Google Tag Manager and Segment architecture, GA4 and BigQuery as the reporting foundation, server-side tracking, and reverse-ETL pipelines that push offline conversions back to Google, Bing, and LinkedIn. The outcome is data marketing and finance can both stand behind.
How do you stay compliant without losing measurement?
By treating consent as an architecture problem, not a checkbox. Consent Mode v2, GDPR, and the DMA don’t have to gut your analytics if the tracking is decoupled and modeled correctly — you capture consented data cleanly and use consent-aware modeling for the rest. I’ve led compliance-critical Google Tag Manager and Segment rebuilds that shipped ahead of schedule with zero tracking incidents, which is the bar: get compliant without going dark on measurement.
Why it matters for spend
Ad platforms are only as smart as the signal you feed them. When offline conversions and high-intent events flow back correctly, optimization improves and cost per acquisition drops — the same discipline that took one paid program’s cost per demo from $350 to $90. Better data in, cheaper pipeline out.
Outcomes
- → Clean, deduplicated tracking across ad and analytics platforms
- → Consent handled correctly (Consent Mode v2, GDPR/DMA) without losing measurement
- → Offline conversions flowing back to Google, Bing, and LinkedIn for better optimization
- → Marketing and revenue numbers that reconcile
Deliverables
- · Tag and tracking audit across all platforms
- · Google Tag Manager / Segment architecture and rebuild
- · Consent Mode v2 and privacy-compliant implementation
- · Server-side tracking and reverse-ETL offline-conversion pipelines
- · GA4 / BigQuery reporting foundation
Not sure which one you need?
Most engagements start with a short call to scope the real problem. Let's figure out where the leverage is.