Accelerate people without boutique software stacks. Create repeatability.
Risk A small engineering team, and not a lot of resources to maintain lots of ideas.
Risk Metric-driven decision making for all teams, from planning to closing.
People first
Metric driven
Scale what works
Stay in the work
Day 30 — listening, and drawing the system
Listen. Sit with the people who do the work, and draw the system so everyone can see it.
Each of the engineering leads, the product teams, and client delivery. One client, shadowed.
One architecture diagram. EMRge: what exists, what clients touch, what it costs, and where SOC-2 is already a habit. The AI OS: Claude, Glean, ClickUp, MCPs, integrations, and services. Infrastructure: what reads and writes data, insights, tools, and platforms.
The budgets, and who owns them. Cloud, Databricks, model inference, and the other large bills.
A weekly half hour with key leadership. An open office hour each week.
Hold me to
A diagram the whole firm can read, and a name on every large bill.
Close one loop. Build something fresh, and see how easily engineered ideas scale when the goal metric is right.
Golden paths for building, releasing, infrastructure, and observability.
Governance reviewed for access, credentials, audits, and least privilege.
A small R&D incubator. Find out how easily engineered ideas scale with the right goal metrics.
Name who is already accelerating, and protect that. Look for what others can repeat.
The hiring wants and needs.
Hold me to
One incubator project, and a read on how easily those ideas scale when the goal metric is right.
Define the year. Scale what moved the number. Deprecate what did not. Leave the rest alone.
A solution graduation process, and stronger data management policies. Metrics drive the decision. A pilot that moved the number goes to scale.
Name what isn’t working. Deprecate the solutions, services, and workflows that aren’t metric-driven.
The EMRge bets, and the work we leave alone. A sharp core, then the thin layer a client uses. What isn’t worth owning, and what is worth outsourcing.
What’s on the edge of our capabilities.
A hiring plan, and the upskilling to future-proof every team.
Hold me to
A graduation process with stronger data management, the bets we will own, and a hiring plan with the upskilling beside it.
We need to protect innovation and creativity by building foundationally strong core frameworks for those creators to utilize.
Shaped by time with Troy, Carrie, Alex Looker, Dianne, Beth, Ericka, Bonnie, and others across the firm. Erin and Kyle already let me sit down and build. I plan to keep showing up that way.
Measurement
Count what grows the business.
Traditional metrics optimize for what’s easy to count. EMR optimizes for what actually grows the business — incremental revenue, healthier customer files, and profitability.
The industry is moving from single-source attribution to triangulation. Ovative is already there, with a unified framework and the platform to operationalize it.
Enterprise revenue
Online and off. Store and wholesale included. If that dollar is missing, the picture is wrong.
Incrementality
The causal part. Sales and members who would not have arrived without the marketing.
Future customer value
New and reactivated. The long health of the file, beyond this week’s order.
Profitability
Margin. The number finance can stand behind.
ROAS and last click
Sees the online order, hands the credit to the final touch, and misses the store, the brand, and everything privacy changes knocked out. Cookie loss. iOS ATT.
Platform-reported metrics
Google, Meta, and the rest each claim the same conversion. Added together, the reports routinely outrun the growth of the business.
Basic multi-touch
A step past last click, and still a correlation. Blind offline. Signal loss has taken an estimated 30–40% of conversions that used to be trackable.
A note for the measurement practice. I will not out-model the people who do this every day. I do want the arguments to be inspectable. Bayesian versus frequency. Saturation and diminishing returns. Fallout and creative fatigue. When a recommendation moves because one of those moves, a client should be able to see why.
The two systems
One platform that measures. One layer that acts.
EMRge
The asset. A data foundation, measurement, and the surfaces our teams and our clients use to plan and grow. Databricks underneath. I read the public product as six capabilities already in market: predictive planning, holistic reporting, modern MMM+, automated operations, EMR activation, and precision testing. The work is to make these scale, stay enterprise-grade, and stay pointed at incremental revenue, a healthier customer file, and profit.
AI OS
The loop. An orchestration layer across Claude, Glean, ClickUp, Slack, Outlook, and the next tool that earns a place. A meeting becomes intelligence. Intelligence becomes an action. The action writes back into the system where the work already lives. Knowledge is still findable the week after. IT and Security are in the first design, because this reaches across the company. Every workflow gets an owner, an evaluation, and a cost.
Buy where the market is already excellent. Build where the workflow is Ovative’s: the measurement logic, the write-back into how this firm actually works, the golden path our own teams will live on. Models are a portfolio. Keep the right to leave.
How I run it
A high bar, and a short path to the customer.
01
Accelerate the people here
Ovative’s advantage is the talent already in the building. Technology should multiply that. Clear careers, a bar we can describe at every level, and managers who know what great looks like.
02
Metric before build
Write down what success is. Test. If it moves the number, scale it quickly. If it does not, stop it where everyone can see, and keep the lesson.
03
Engineers near customers
Keep the people who build close enough to hear what is actually needed. Give them room to step outside a lane when the client problem asks for it. That proximity is how a firm this size stays able to pivot.
04
Build the 5%
Get the core right, once. Then build the thin layer each customer actually uses, and make that layer theirs. In the age of AI, that is what finished looks like. Specific, on top of something solid.
05
Golden paths
Care in delivery comes from a standard way to start and a standard shape of output. A path people trust, and permission to leave it when they should.
06
Hands on
I can sit with a stakeholder who does not write code, and I can still read the diff. Name the blocker early. Name the success early, and protect it. AI carries an idea further. Judgment stays with the people.
The work, briefly
Twenty years, still in the code.
The pattern is the one I would bring here. Get close to the user. Build the smallest thing that teaches you something. Scale it when the metric moves.
2015 — now
Google
Tech lead, manager, and staff engineer. Marketing platforms.
SACA. Machine learning over search-term performance, to see what was resonating. It led to YACA, the same idea pointed at YouTube creatives. Themes, not just terms.
Video creative performance. A patent-pending model for how a video will land.
SDF, from Bulkdozer. A sheet, into DV360.
Data lake designer, for Publicis. Ads, maps, and social, through Fivetran, into one channel view.
Anomaly detection. ARIMA, seasonal decomposition, an alert when the pattern breaks.
In the open. The Agent Development Kit, and MCP servers for Google Ads.
2012 — 2015
DevMynd
Director. We grew the consultancy from 8 people to 32.
Local dealers. Automotive bidding, built for the way a storefront actually buys media.
Farmland. A chemical-buying tool drawn on geo maps. The user was never an engineer. Adoption was the product.
2011 — 2012
Lightbank and oBaz
CTO-in-residence. The idea that held was oBaz.
Group buying, gamified. Deals like Sperry and Ray-Ban. From the idea through to an acquisition into Groupon Goods.
2006 — 2011
Tribune
Steve Gable got us the MacBooks. I started the Tribune Interactive group.
newspaper.com. A CMS for the sites.
The archive. Watermark a photo, then license it on to iStock.
Past ninety
The year is the point.
My job as CTO is to make that measurement system more scalable, more AI-augmented, and more tightly closed-loop into planning and activation — while keeping the human expertise that turns numbers into decisions.
Get the core right
Data lake, insights, planning, and protection. The foundation has to be boring before the ambition gets loud.
Close the loop
Measurement that feeds planning and activation, with a person still making the call. One new surface I would pilot, and not promise: read organic video the way SACA taught us to read search. Themes that resonate, not only media we paid to run.
Run AI like a product
Governed, evaluated, costed, and small enough to change. The AI OS earns a second team only after the first one can show its work.
Grow without the machinery
Doubling the business in three years works if engineers can still hear a customer. Hiring follows the year. The bar stays high. The path between the build and the client stays short.