Before you fund the AI roadmap,
find out which of it is real.
Enterprise AI programs rarely fail on the model. They fail underneath it, on the system nobody owns, the two customer tables that never reconciled, the field that has been null since 2019. That surfaces in month seven, long after the budget is committed and the roadmap is public, and by then the conversation is about blame rather than architecture.
I have been inside enterprise data environments since 2007, including eight years as a Senior AI Architect at Google Cloud, across retail, media, grocery, financial services, pharma and automotive. Give me two weeks and I will tell you which of your use cases your data can actually support, which cannot be built as scoped, and what the rest would cost to make real. I also built Spartera to solve the structural version permanently — distributing data and model value without ever moving the underlying asset.
I take on a limited number of engagements at a time.
Or start with a 20 minute conversation →
Organizations Served
Organizations served through prior firms — Google Cloud, Adobe, Omnicom, and PwC — across financial services, healthcare, retail, and media, on initiatives where failure creates regulatory exposure or reputational damage.
Google · Adobe · Bank of America · American Express · Pfizer · Kroger · Albertsons · Wells Fargo · Televisa Univision · Mercado Libre · Toyota · Priceline · TripAdvisor · Mars · Macy's · TD Bank · LIV Golf · Highmark Health
Situations Resolved
The pattern is consistent: an organization invests heavily in AI, the initiative stalls or misfires, and the question shifts from "how do we innovate" to "how do we contain the damage." That's where I come in.
Stalled Pipeline
A global cloud platform's AI deployment pipeline had stalled across enterprise accounts. Vendors were missing milestones. Customers were escalating. Internal confidence was eroding.
$50M+ in contracted revenue was at risk. Leadership needed a defensible technical position before the next board review.
Re-architected the delivery model, stabilized the pipeline, and recovered the at-risk revenue base. Customer deployment time dropped 85%.
Platform Failure
The world's largest media agency's global analytics platform was bleeding cost and delivering insight months too late to influence decisions.
Architecture debt had compounded to the point where incremental fixes weren't viable. The platform was a credibility risk, not just a cost center.
Rebuilt from the data layer up. Operating costs cut over 30%. Months of insight delivery compressed into days.
Model Distribution Problem
At Google, I watched a large grocery chain build a powerful customer purchase prediction model. It worked — which meant every team wanted it. The model had become the most valuable thing in the building, and there was no safe way to share it.
Distributing the model meant either handing over weights and training data, or building bespoke API access for each team. Both paths exposed proprietary IP. Innovation had stalled at the point of success.
A governed inference layer rather than distribution of the asset itself. The model never moves; business teams access predictions through a controlled interface, and the owner retains the weights and training data.
Data Governance Crisis
A financial services firm had AI agents and internal teams accessing and distributing raw customer data across systems without governance controls. The architecture had scaled faster than the oversight.
Direct regulatory exposure and material breach risk. Leadership needed both a clear diagnosis and a remediation plan defensible to the board.
Architected a zero-data-movement governance layer — analytics distributed without raw data exposure. Queries execute where the data lives; only the governed result returns.
How I Engage
The AI Feasibility Read
Two weeks. Every use case on your roadmap scored against the things that actually kill them: data availability, data quality, system access and integration cost.
You get a go or no-go on each one, and for every no, what it would take to turn it into a yes. Run it before the budget commits, not after.
- Fixed fee, fixed scope, two weeks
- Scored per use case, not a narrative
- Starting at $18,000
Strategic Advisory & Fractional CAO
A small number of ongoing engagements, working directly with C-suite leaders as the outside voice on Data and AI strategy, architecture, and the decisions that are too expensive to get wrong.
This is not a retainer for access. It is for leaders who want someone who will push back, name the blind spot, and stay engaged long enough to be accountable for the answer.
- Architecture oversight on live programs
- Recovery of stalled or failing initiatives
- Federal agencies & systems integrators
Diligence & Expert Witness
An independent read for the person who has to decide. For investors, whether a target's AI capability is real, defensible and economically viable at scale, or a wrapper and a pitch deck.
For counsel, whether a system did what it was represented to do, and what that means in terms a court can follow.
- Pre-term-sheet read — days, not weeks
- Full technical diligence & architecture review
- Failed implementations & vendor disputes
Keynotes & Executive Sessions
I speak to executive and technical audiences on why enterprise AI programs fail, what the recurring patterns look like from the inside, and what recovery actually costs.
Workshops are built around your organization's specific problem, not a curriculum that could have been delivered by anyone.
- Why Your AI Strategy Is Already Failing
- The Hidden Cost of Bad Data Foundations
- AI Governance Without the Theater
The Infrastructure
The advisory identifies the problem.
Spartera is the architecture built to
ensure it doesn't happen again.
Every engagement traced back to the same root cause: someone moved raw data when they only needed to move the answer. I built Spartera around a single conviction — the data never has to move. Only the insight does. That architecture doesn't just reduce risk. It unlocks monetization that was never possible when sharing meant exposing.
Share analytics and AI outputs across your organization without creating data exposure risk. Queries execute where your data lives. Only results return. The compliance team stops losing sleep.
Deploy AI model predictions across your organization — or to external customers — without ever exposing the underlying model. The model weights, training data, and architecture never leave your infrastructure. Retrain freely. Consumers see no disruption.
Turn proprietary data into revenue without surrendering custody. Your data stays in your environment. Buyers get the insights they need. You keep full control of what moves — and what doesn't.
Zero data movement · 17+ data platforms · 18,000+ analytics products · Secure model inference
Explore Spartera →Provenance
Practicing since 2007, stabilizing AI and data systems in environments where failure creates regulatory exposure, reputational damage, or both. My work sits at the intersection of deep technical architecture and boardroom-level risk strategy.
Previously Senior AI Architect at Google Cloud, where I worked across financial services, healthcare, and regulated enterprise accounts. Before that, architecture and strategy roles at Adobe and Omnicom serving Fortune 500 data organizations.
Provided technical review feedback on a chapter of Data Science on the Google Cloud Platform (O'Reilly Media, 2018), a practitioner's guide to building production-grade AI and ML pipelines at enterprise scale.
Project manager and architect of AutoMLOps, an open-source production pipeline framework released by Google Cloud.
Founder of NoVAI, a practitioner-led AI community based in Northern Virginia, and Spartera, a zero-data-movement analytics marketplace built on GCP and BigQuery.
Engagement
Most engagements start with a Feasibility Read, because it is bounded, fixed fee, and answers the question you actually have. Ongoing advisory follows when the work warrants it, with a small number of organizations at a time. If the situation is urgent, say so — I prioritize accordingly.
A confidential call to understand the situation, assess severity, and determine fit.
Every use case scored on data availability, data quality, system access and integration cost, with a go or no-go on each. Fixed fee from $18,000, delivered in two weeks.
Ongoing strategic counsel, architecture oversight, and direct access for decisions that can't wait for a meeting.
Or start with a 20 minute conversation →