Design systems
One language for type, spacing, color, components. Documented, themable, built to outlast a redesign.
Where the system meets the person. Design, interface, brand, mobile, web — the layer readers and users actually see. We work this stratum so the rest of the system reads as one thing, not a stack of decisions other people made.
Edge work is what the rest of the engagement gets judged by. A perfect backend with a confused interface is still a confused product.
One language for type, spacing, color, components. Documented, themable, built to outlast a redesign.
Web and mobile screens, drawn carefully. Wireframe → high-fidelity → engineered handoff.
Logo, marks, voice, typography, photography direction. The visual side of the practice your audience meets first.
iOS and Android. Native where it matters, cross-platform where it doesn't. Shipped to the store, not stuck in TestFlight.
Figma library, tokens, component docs. The reference your engineers and future designers both use.
Working code in your repo, deployed, signed. Not a prototype.
Logo, marks, type, color, voice guide. Everything a partner agency would need.
A public write-up of how it was made, redacted as needed. Read
Where the work happens. APIs, integrations, databases, infrastructure — the engine room. The middle stratum, doing most of the lifting on every engagement, and the one most likely to be quiet on a good day.
Services work is the bulk of the engagement. It's not glamorous; it's load-bearing. We pick boring technology on purpose and we keep things observable.
REST or GraphQL — versioned, documented, signed at the boundary. One auth surface, not three.
Payment, CRM, ERP, government services, third-party APIs. Make the quiet ones quiet; make the noisy ones legible.
Schemas, migrations, indexes. Designed to grow without breaking; designed to roll back when growth was wrong.
Deployment, observability, cost discipline. Boring on purpose. Signed on every change.
How the system fits together, drawn once, kept current. The map the team owns when we leave.
Deployed, monitored, documented, signed.
Every external system named — who reads, who writes, what fails when.
What to do when things break. Written in plain English; rehearsed before we leave.
Where intelligence lives. AI agents, language model integration, analytics, pipelines. The newest stratum and usually the riskiest. We treat AI as engineering, not as magic — and we say no to it more often than we say yes.
Most AI conversations end with us recommending less AI than the client expected. The ones that go ahead are scoped tightly, evaluated honestly, and built to be turned off without breaking anything.
Custom agents and copilots that fit inside your existing systems. Tools, not chatbots. Audited, evaluated, replaceable.
Add language models to existing flows. The right model for the job, controlled costs, measurable wins, clear failure modes.
Dashboards the business will actually use. Numbers a non-engineer can trust without phoning home.
Move data from where it lives to where decisions get made. Reliable, observable, signed, owned by your team after we leave.
Where AI fits in your stack, where it doesn't, what to do first. The deliverable for the standalone "AI Readiness" engagement shape.
Deployed, evaluated, monitored, in production. With evals you can run before every release.
Tests, metrics, regression detection. Specific to your domain, not generic LLM benchmarks.
Schemas, pipelines, lineage. The truth, written down, signed.
One paragraph is enough. Tell us the shape of the system; we'll reply within two business days.