I help enterprises design and deploy AI systems that deliver real value.
AI is powerful, but most teams struggle with where it actually fits. I work with enterprise customers to figure that out, from early discovery through evaluation and into production. My focus is on turning real business problems into systems that deliver measurable value, whether that involves LLMs, traditional machine learning, or the right level of automation.
Core Expertise
Bridging frontier model capabilities with real-world enterprise architectures.
Architecture
I work with teams to design generation systems that actually hold up in production, from RAG and prompt workflows to multimodal pipelines. A big part of the job is helping people understand how these models behave and where they tend to break.
Evaluation
Most teams underestimate evaluation until it starts creating real issues in production. I help define what “good” actually looks like, build evals around that, and make sure systems improve over time instead of drifting.
Automation
Designing the right level of automation. Some workflows can be fully automated, others need human oversight. The key is knowing the difference and building systems that reflect it.
Discovery, Evaluation, Deployment
The enterprise journey to production-ready AI systems.
1. Discovery & Shaping
Identifying high-leverage use cases that are worth building. We define the technical approach for both text and media generation, understand specific model capabilities, and map them directly to business impact before code is written.
2. Evaluation & Testing Loops
Guiding customers on prompt design and system tradeoffs. Together, we co-create testing strategies and quantitative evals that capture reliability across cost, latency, and response quality thresholds.
3. Deployment & Integration
Advising engineering teams on the final mile. Designing for observability, embedding appropriate human-in-the-loop oversight, and navigating the integration of AI into the enterprise stack to accelerate real-world workflows.
Applied Advisory Outcomes
Guiding customers through critical decisions for high-volume, multimodal deployments.
Intelligent Key Art Resizing
The Challenge: Scaling key art across formats, channels, and global markets
while preserving the focal subject and overall composition.
The
Guidance: Worked with the team to define a system that could automatically
identify and preserve key subjects—such as actors—within source images. Helped shape an
approach where layouts could be dynamically adapted across formats without losing visual
integrity. Partnered across product and engineering to translate this into a scalable
capability, which ultimately informed new features within Adobe’s product ecosystem.
Personalized E-Commerce Asset Generation
The Challenge: Producing high-volume e-commerce assets used in site
navigation, where relevance and freshness directly impact customer engagement and
revenue.
The Guidance: Partnered with product and engineering teams
to define how generative systems could support asset creation at scale. Helped structure a
pipeline that enabled on-demand generation of storefront imagery and copy, making it
possible to personalize assets based on user behavior. Worked with stakeholders to balance
automation with brand control, ensuring the system could scale reliably across a
high-traffic retail environment.
Composable Ad Asset System
The Challenge: Creating high-volume ad assets across multiple markets, with
frequent creative changes, without forcing designers into a rigid
template.
The Guidance: Recommended against a fully generative
template approach and instead helped shape a composable system built from reusable ad
components and formats. This gave designers the flexibility to assemble assets from approved
building blocks while still enabling fast generation for different content types, markets,
and audience behaviors. The result was a more adaptable workflow that balanced creative
freedom, brand consistency, and production speed.
How I think about AI systems
Most enterprise teams don't fail because the technology is not powerful enough. They fail because they invest in the wrong problems, or apply AI where it doesn't meaningfully improve outcomes.
My approach starts by shaping the problem itself. Before thinking about models or architectures, I work with teams to identify where AI changes the economics of a workflow, where it meaningfully improves speed, quality, or scale.
From there, the focus shifts to reducing risk. That means making systems observable, defining clear evaluation criteria, and understanding where human oversight is required. The goal is not just to make something work, but to make it predictable and trustworthy in production.
I think of this role as helping teams make better decisions early, so they build fewer, better systems that actually hold up in the real world.
"The hardest part of AI isn't building the system, it's deciding what's worth building."
Working on something in AI?
If you are trying to figure out what to build, how to evaluate it, or how to get something into production, I am always open to a conversation.