When an AI initiative becomes
a liability, I make it
an asset.
For 17 years I watched enterprises create unnecessary risk the same way — moving raw data to distribute value. Dashboards, spreadsheets, vendor feeds, AI pipelines. Every one a potential liability. I built Spartera to solve that permanently.
But sometimes the damage is already in motion. The vendor can't deliver. The board wants answers. The initiative that was supposed to transform the business is now a line item on the risk register. I diagnose what went wrong, stabilize the architecture, and give leadership a defensible path forward — before the problem becomes public.
I take on a limited number of advisory engagements at a time.
Select Organizations
Trusted by leadership teams navigating complex AI, data, and analytics challenges in regulated and high-stakes environments.
Google · Adobe · Bank of America · American Express · Pfizer · Kroger · Albertsons · Wells Fargo · Televisa Univision · Mercado Libre · Toyota · Priceline · TripAdvisor · Mars · Macy's · TD Bank · Iron Mountain · 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. Marketing, product, finance, IT, operations. 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. The AI team couldn't retrain without breaking downstream consumers. Innovation had stalled at the point of success.
This problem — seen across multiple engagements — is what led to Spartera's secure inference layer. The model never moves. Business teams access predictions through a governed interface. The AI team retrains freely. If this pattern sounds familiar, it's solvable today.
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. This engagement directly informed the architecture behind Spartera.
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. Spartera talks to your model's API endpoint and returns the inference. 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.
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 · 14,000+ analytics products · Secure model inference
Explore Spartera →Provenance
Seventeen years 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.
Editor of Data Science on the Google Cloud Platform (O'Reilly Media) — a practitioner's guide to building production-grade AI and ML pipelines at enterprise scale.
Engagement
I work on a retained advisory basis with a small number of organizations at a time. If the situation is urgent, say so — I prioritize accordingly.
Initial Conversation
A confidential call to understand the situation, assess severity, and determine fit.
Diagnostic Assessment
A structured evaluation of the technical architecture, vendor landscape, and organizational exposure.
Retained Advisory
Ongoing strategic counsel, architecture oversight, and direct access for decisions that can't wait for a meeting.