We build systems that make people more capable.
Skowak is an AI transformation boutique, built on twenty years of designing and shipping software for domains that resist it insurance underwriting, federal healthcare data, solar engineering, developer infrastructure.
The practice
The through-line
Every engagement is a version of the same problem: a domain too complicated for the people inside it to see whole, and software that made it worse rather than better.
The method hasn't changed much. Model the domain before designing the interface. Find where the real constraint lives it's almost never where the complaint is. Build the thing, watch people use it, and measure whether they can do the work better.
That last part matters more than it probably should. We don't measure success by engagement or time-on-task. We measure it by whether someone can do their job better than they could before.
Applied AI
What's different about AI work
Applied AI hasn't changed the method; it's raised the stakes on getting it right. A traditional system fails visibly. An AI system fails plausibly it produces something confident and wrong, and unless you've built the instrumentation to catch it, you find out from a customer.
So the discipline is the same discipline, applied harder: understand the domain, define precisely what correct means, build the measurement before you build the trust.
Range
Range
Insurance (Fidelity Life), federal healthcare data (Bitscopic / VA), renewable energy (SunPower), developer tooling and DevOps automation (StackStorm workflow authoring 40–60% faster), cloud infrastructure (Engine Yard), education (Colearn parent admin effort down 30–50%), and consulting delivery (ThoughtWorks, Microsoft).
Startups from garage to Series A, and enterprise organisations with thousands of employees. The problems rhyme more than people expect.
Own systems
What we've built with AI
We build AI systems for our clients. We also build them for ourselves. These are live systems in daily use not demos and they're where much of the applied-AI practice comes from. The evaluation frameworks, agent orchestration patterns, and human-in-the-loop designs used in client work were developed here first.
- Craftal
- A research-based multi-agent product-planning system: a custom DSL and RAG pipeline behind a deterministic verification core, turning product context into structured, verifiable planning workflows with human review built in.
- Finetunio
- Model development, evaluation, and control as a single connected flow.
- CareDash
- A multi-agent healthcare workflow and decision-support system focused on care coordination with human-in-the-loop review.
- Contextus
- A model-agnostic AI workspace for portable context and user-owned memory.
The trade
Why work with a principal
There's a real trade here, and we'd rather name it than sell around it.
What you give up: capacity, 24/7 coverage, a bench, and the institutional comfort of a large firm's name on the invoice.
What you get: the person who runs discovery is the person who builds the system. No translation loss, no staffing surprise, no junior team learning your domain on your budget. Twenty years of judgment about what breaks in production, applied directly.
For a large multi-year program, hire a firm. For a hard problem that needs one senior person to design and build it, that's what Skowak is for.
Teaching
Teaching
Skowak's practice grew out of a teaching instinct: design and front-end engineering courses, product-team mentorship, and developer evangelism reaching thousands. It's the same goal as the rest of the work that people can do the thing themselves afterwards. It's also why engagements are structured around handoff rather than dependency.
Start here
What are you trying to build?
If you're evaluating where AI fits in your operation or you have a project stalled between demo and production, that's the conversation Skowak is built for.