Intelligent Software

I design and ship pipelines and warehousing that keep product teams moving — scheduled jobs, clear ownership, and data you can trust without babysitting the stack. From ingestion through transformation to delivery, the goal is boring reliability: fewer fire drills, faster answers, and systems ops can actually run.

The idea is to have a simple way understand the pipeline, the data and have easy ways to observe the transformations and the data flow.

Software for the people keeping sites on schedule, monitoring solar farms, drone and field inspections, construction progress, and general O&M reporting that fits how asset teams actually work. Less spreadsheet chase, clearer compliance trails, and fewer surprises when deadlines land.

I have worked hard to understand the needs of the asset and the O&M teams in order to design software which conforms to their workflows and helps them be more efficient.

My latest work with Small Language Models has been focused on building such workflows and create tools that more private, secure and less expensive to run.

I shape platforms where the domain is messy and the release path has to stay simple — one deploy lane, feature flags, and boundaries teams can reason about. Less tribal knowledge, more systems that survive handoffs and keep shipping under pressure.

The idea behind a good platform design is to have a foolproof pipeline for the product from end-to-end. This way, no matter how complex the product is, and how many changes to the team are made, we can always, reliably deliver the product to the users.

Other critical aspects involve: visibility, observability, traceability, and ease of use.

Hands-on with modern models where they earn their keep — cloud APIs, rented capacity, and local LLMs wired into real operational workflows, not slide-deck demos. Agentic loops, review gates, and tooling that cut grunt work without inventing new risk.

The most important technical shift in the last couple of years has been the introduction of Intelligent Agents and the ability to build workflows that are more autonomous and efficient.

This has been made possible by the introduction of Small Language Models (LLMs) and the ability to build workflows that are more autonomous and efficient.

I believe this is the most likely path to the future of software development and automation.

Ownership means someone is answerable for the stack and architecture choices, the delivery path, and the operational reality after launch. I take that end-to-end accountability so product and ops are not left guessing who owns the next decision. All of this while at the same time keeping the tech department with a single and simple roadmap and multiple responsible heads

I like to think of myself as the following:

• CTO level thinkng
• Own technical strategy
• Setup the AI stack
• Real hands on experience with Claude Code
• Knows their way around openClaw

Automation that pays for itself — platform choices that cut cloud spend and tooling that erased 1000+ hours of manual grind. Fewer handoffs, fewer copy-paste rituals, more time on work that actually moves the needle.