Artificial Intelligence

I use AI in two distinct ways. As infrastructure for my design work, cutting the time I spend on repetitive tasks and sharpening collaboration with engineers and stakeholders. And as a design material in itself, building products where AI is not a feature but the core of the value proposition. The projects below sit firmly in the second category.

Dual power of AI in design: AI as design infrastructure and design material

How I use AI in my design work

Beyond building AI products, I've restructured how I work as a designer around AI tooling. Not as a novelty, and not selectively. As a genuine layer of how I think and produce.

Optimizing the design workflow

The areas where AI has changed my day-to-day most concretely: research synthesis, turning interview notes and heuristic findings into structured insights in a fraction of the time; copy iteration, generating and stress-testing UX copy variants before they go into a prototype; design system documentation, which used to be the most tedious part of any DS project and now takes a third of the time; and stakeholder communication, preparing alignment materials and translating design decisions into business language faster and with less cognitive overhead.

The result isn't that I work less. It's that I spend more time on the decisions that actually require my judgment and less on the tasks that were just consuming it.

Building AI into the service value chain

The more interesting application, and the one I find myself thinking about most, is where AI belongs inside a product's value chain rather than just inside a designer's workflow.

There are two distinct positions worth distinguishing. AI as an intermediate layer, working between systems or processes to reduce operational friction, automate classification, flag anomalies, or accelerate decisions that currently require human time. The DORA compliance platform is a clear example: the AI doesn't replace the auditor, it compresses the time between a risk signal and a human decision.

AI's most consequential design decision isn't the interface, it's where the human boundary sits

And AI as a user-facing solution, where the intelligence is the product. Contable AI Pro sits here. The user doesn't interact with a tool that has AI inside it. They interact with something that would be impossible without AI at its core.

Knowing which position makes sense for a given product, and designing the right human-AI boundary for each, is what I consider the most interesting design problem of this decade.

Personal projects

Contable AI Pro

Automated personal finance analysis powered by AI

The problem

Personal finance tools ask you to do the work. You categorize, you label, you reconcile. For anyone with years of banking history and hundreds of monthly transactions, that friction is enough to make the whole thing pointless. I wanted to build something where the user does nothing except drag a file.

What I built

A web application that takes raw CSV bank exports and turns them into a structured, categorized financial dashboard in seconds. The core engine is Claude 3.5 Sonnet, which handles semantic categorization of transactions by learning from the user's own consumption patterns over time. Not rule-based sorting. Actual reasoning about what a transaction means in context.

The technical decisions I'm most proud of: implementing Anthropic's prompt caching to reduce token consumption by 90% by reusing historical user context across sessions, and using Zod for strict data validation to ensure AI responses conform to the application schema. That last one matters more than it sounds. An AI that returns malformed data in a financial context isn't just wrong, it's a liability.

Stack

Next.js for frontend and API. Claude 3.5 Sonnet for financial reasoning. Vercel Blob Storage for CSV handling. Private authentication middleware and full search engine exclusion for data privacy.

  • >90%Prompt caching
  • 4,500+Rows ingested
  • 3Types of analysis

The result

4,500 rows of raw financial chaos to an organized dashboard in milliseconds. A zero-friction tool that understands your financial life rather than just reading it.

Portfolio web with Cursor

Design, development, and deployment from the IDE

Overview

I designed and built this portfolio end-to-end in Cursor: information architecture, UI systems, and copy, through a production React stack (Vite, TypeScript) and lightweight serverless endpoints where needed (for example gated downloads). Human decisions on narrative, hierarchy, and craft were paired with AI-assisted iteration for drafting, refactors, and keeping changes consistent across the codebase.

Accessibility was treated as a product requirement, not a checklist at the end: semantic structure and landmarks, skip navigation, visible focus, meaningful link and button labels, external-link affordances, and patterns that hold up with keyboard and screen readers. SEO work included unique titles and meta descriptions per public route, JSON-LD (Person, WebSite, Organization, breadcrumbs), sitemap and robots aligned with what we actually ship, plus indexable public case previews alongside password-protected full studies.

Along the way I ran structured passes on UX copy, navigation clarity, forms and CTAs, motion usage, and performance basics (lazy media, sensible bundles, edge-friendly hosting). The stack stays deliberately mainstream—React Router, component-scoped CSS, Lucide icons, Git-based releases—so the site stays fast to load and cheap to evolve.

  • Unique metaPublic SEO surface
  • 4 typesStructured data
  • Patterns + focusAccessibility