"We want a state-of-the-art AI model that self-trains every night..."
Model discovery
Working with you to uncover and assess what exact model needs you have, short, near, and long term.
Model deployment
Installation, configuration, and deployment of the AI model(s).
Model training / tuning
Tailored AI training and tuning systems that align directly with your organization's unique data, technological, and strategic requirements & constraints.
Model leverage
Developer-first and hands-on, I work directly with dev teams to facilitate the consumption and throttling of the model into products.
Self-improving AI systems that learn and optimize
Recently, I built and deployed a game-changing self-tuning AI model platform for a content creation platform. The project required designing an end-to-end architecture and workflow that married optimal model performance with specific business intent — transforming the AI models in place from a set of isolated models and silos of python code into an organized system that continuously learns from scored outcomes and dynamic data.
Adaptive AI architecture
I worked with the platform and developers to define, select, configure, and deploy the appropriate AI model in the Azure AI Foundry ecosystem, according to specific needs and constraints.
Intelligent model evolution
I architected, developed, deployed, and supported the creation of an automated model tuning system that pushed customer-specific model tuning data into the Azure AI Foundry model data training system on a nightly basis.
Continuous learning frameworks
The python and C# developers on the team were able to immediately gain enormous value and lift by this seamless deployment and configuration. They were not inhibited or constrained throughout the rollout and delivery.
A typical AI model fine-tuning journey
Challenge: This project was for a young company who needed to transform their static and disconnected AI model and decentralized collections of python scripts into centralized, self-improving system that learns, adapts, and self-optimizes over time.
Solution: I designed and deployed an automated AI model pipeline within Azure AI Foundry for a large-scale content creation platform. I led the process of model selection, configuration, and deployment, aligning the architecture with both business outcomes and developer workflows.
For this client, I created an automated nightly self-optimizing and self-tuning framework that injected customer-specific and precisely formatted tuning / training data directly into the model as an automated retraining cycle. The result was a continuously improving ecosystem — one where existing Python and C# developers on the team were able to continue their work freely while the AI models behind the scenes refined themselves.
This kind of system design demands both precision and empathy — precision for the algorithms, empathy for the humans who depend on them. It’s not enough to automate intelligence; you have to respect the realities of the team, the codebase, and the business model. The pipeline I built didn’t just optimize models — it optimized collaboration. Developers didn’t need to become data scientists overnight. They simply pushed code, and the models quietly learned from the outcomes, integrating performance feedback, content analytics, and human interactions into a closed feedback loop.
At its core, the goal was continuity — a self-sustaining loop of improvement where insight compounds. Each night, the models absorbed new patterns, retrained against fresh context, and redeployed automatically with traceable version control. The system effectively created an “AI metabolism,” one that metabolized new data into performance gains without manual intervention. This freed leadership to focus on strategy rather than firefighting, and it gave developers the confidence that every release was a little smarter than the last.
I’ve come to see that real AI transformation isn’t about installing tools — it’s about re-architecting habits. When you design feedback, logging, and training as first-class citizens within a platform, you shift the organization’s entire center of gravity toward learning. That’s what I build: not isolated models, but adaptive ecosystems that evolve alongside the business.
These architectures create a new kind of competitive advantage — one that compounds. Every interaction, every customer click, every moderation event feeds back into the intelligence layer. The result is a continuously learning company — one where AI becomes infrastructure, not an experiment. And that’s where the real leverage begins: when the models aren’t just outputs of innovation, but engines that drive it forward.
Creating the matrix of every possible AI opportunity.
One of the key deliverables in the discovery phase of the process is the AI opportunity matrix, which provides a detailed list of each key AI insertion opportunity that exists and its ultimate impact on the financial and operational bottom line.
- Area, focus, and impact
- Financial implications
- Integrations and technical ramifications
- Ownership, accountability, and expectations
"Anyone can deploy a model; the real work is aligning it with human intent, business value, and measurable improvement. My role is to architect systems that don’t just run, but learn — that tune themselves, refine themselves, and deliver more tomorrow than they did today."
"I have worked with Shawn and I know his talent and leadership can have an enormous impact in a consulting engagement. I have seen it first-hand."
