700+
automation workflows built across revenue and marketing operations
I help marketing teams make AI part of how work actually gets done.
I embed into real GTM workflows, build practical tools and agents with the team, and turn early wins into repeatable systems that marketers can keep using on their own.
Most relevant for roles like
- Forward Deployed AI Accelerator
- AI GTM Engineer
- Marketing AI Enablement
- Growth & Revenue Operations
Embedded
working inside real campaign, ops, SDR, and reporting workflows instead of designing from a distance
Repeatable
turning one useful tool or workflow win into reusable patterns other teammates can adopt quickly
AI-First
moving teams from manual process and one-off prompting toward default AI-assisted execution
What I Build
How I modernize GTM teams without breaking what already works
My advantage is not just building tools. It is understanding how marketers and revenue teams already work, then using AI, automation, and lightweight internal systems to remove toil, improve judgment, and strengthen the stack they already depend on.
Campaign research and creative systems
Built internal AI research workflows that inspect competitor sites, static code, network traffic, and live ad libraries to surface tech stack clues, campaign patterns, and practical competitive insight.
Marketing ops modernization
Used automation plus AI to handle lead normalization, smart routing, intent analysis, campaign briefs, market research, and customer engagement signals inside the systems teams already use.
Sales workflow and SDR enablement
Built AI workflows that parse meeting transcripts, score readiness, extract structured fields, sync CRM updates, and generate personalized outbound context for SDR and sales engagement teams.
Reporting, data cleanup, and signal delivery
Normalized messy channel data, matched fragmented fields, merged reporting sources, and used Python plus AI to generate cleaner analysis, faster alerts, and more useful operating signals.
How I Scale Change
The workflow pattern I use to move teams toward AI-first work
The highest-leverage work is usually not a flashy tool. It is finding a painful daily workflow, building the first practical version with the people doing it, then turning that win into something other teammates can reuse without needing me in the loop forever.
Find the workflow that is losing time or judgment
I start by understanding the real deliverable, the messy handoff, and the repetitive decisions inside the team’s day-to-day work.
Build the first working version with the user
I build agents, prompts, skills, automations, or lightweight internal tools alongside the team so the solution fits real work instead of theory.
Turn the win into a repeatable team pattern
Once something works, I document it, simplify it, and help other teammates adopt it so the change scales beyond one person.
Practical AI Proof
AI work tied to real operating problems
These examples show the kind of workflow transformation work I have already done: practical tools, embedded systems, and AI support tied to real GTM outputs.
System
Technical Competitor Intelligence
AI workflows that analyze sites, traffic patterns, and live ads to map real competitor capability.
System
Meeting Intelligence + CRM Sync
Transcript parsing, readiness scoring, JSON outputs, and automatic CRM updates for faster follow-up.
Case Study
AI Vetting and Intelligent Routing
AI used as a judgment layer for messy lead data, enrichment, and routing.
Article
AI Lead Scoring Assistant
Qualification support built for better routing priority and follow-up timing.
Demo
Marketing Report Assistant
Turns scattered channel inputs into structured summaries without manual reporting toil.
Article
LLM Reporting Workflow
Inserted AI into a messy monthly reporting process without losing analyst control.
Article
SEO and Content Systems with AI
Research and drafting support built around search intent and editorial control.
Demo
CreativeOS
AI support for creative generation, iteration, and asset ideation.
Hiring Lens
Why this background maps well to teams trying to make AI the default way work gets done
Embeds with teams, not just systems
I work from the real workflow outward, which makes it easier to spot where AI can remove friction without creating more of it.
Turns one-off wins into reusable patterns
My goal is not a clever demo. It is to turn useful workflow changes into repeatable playbooks, prompts, agents, and internal tools other teammates can pick up.
Builds and teaches adoption at the same time
The strongest systems are the ones people keep using after the first win, so I design for adoption, trust, and self-sufficiency instead of one-time novelty.
Reusable Architecture
How I turn useful workflow wins into systems other teams can keep building on
This is less about tool collecting and more about operating structure: where signal comes in, where judgment happens, how actions run, and how the output becomes visible and reusable.
Signal Capture
Traffic, forms, and intent
Landing pages, paid traffic, SEO entry points, outbound responses, and top-of-funnel signals enter the system here.
- Webflow
- Paid + SEO
- Inbound forms
Decision Layer
Enrichment, scoring, routing
This is where messy lead data becomes usable: qualification support, priority logic, CRM ownership, and next-step decisions.
- Salesforce
- HubSpot
- Pipedrive
Automation Layer
Triggers, nurture, AI assist
Once the logic is clear, automation takes over: follow-up, lifecycle branching, enrichment loops, and AI-assisted execution support.
- Nurture flows
- AI vetting
- Workflow orchestration
Measurement Layer
Reporting, feedback, iteration
The system closes when marketers can actually see what happened and act on it through reporting, attribution, and experiment readouts.
- GA + dashboards
- Tableau
- Experiment review
Salesforce
Marketo
Outreach
Salesloft
6sense
Demandbase
ZoomInfo
Clay
Segment
Selected Work
Selected systems that show how I modernize GTM infrastructure
Marketing Ops
Automation Engine Across 700+ Workflows
Routing, qualification, and operational workflows built to reduce manual work at scale.
See Case Study
Web + Experimentation
Webflow as a Governed Growth Platform
A scalable website operating layer for launches, SEO, experimentation, and routing.
See Case Study
ABM / Growth
Enterprise ABM and BD Infrastructure
Targeting and execution structure for a more repeatable enterprise motion.
See Case Study
Growth Programs
Multi-Region ABM for High-Value Accounts
Multi-region programs powered by strong account logic, signal interpretation, and sales alignment.
See Case StudyHow I Operate
Execution principles I bring into growth teams
Automation should remove toil, not hide confusion
If a workflow is unclear, AI will only make it faster in the wrong direction. I prefer cleaning up the operating logic first, then automating what deserves to scale.
Experiments should feed infrastructure
I like quick tests, but I am most useful when we turn the winners into reusable templates, governed workflows, or repeatable launch systems.
Reporting should answer what to do next
I care less about decorative dashboards and more about whether the team can diagnose funnel movement, routing issues, or campaign performance quickly enough to act.
For Hiring Teams
Looking for someone who can modernize a GTM stack without losing how teams actually work?
This variant is meant to make that read clearer: practical AI, growth execution, cross-team systems thinking, and operational infrastructure in one profile.