AI
Experience
Agent workflows, LLM integrations, and AI-augmented systems — built around how people actually work, not how the model works.
Most "AI integrations" are ChatGPT with a company logo on it. A chat window. A button that summarizes documents. Those aren't products — they're demos that someone productized before they understood the problem.
The work here is different: designing the full human+AI operating layer. Where does the AI make the decision and where does a person? What happens when the model is uncertain? How does the handoff happen so the human doesn't lose context? What does the person using the tool actually need to trust it?
These are UX and systems design questions as much as they are AI questions. The organizations getting real ROI from AI aren't the ones who deployed a model — they're the ones who redesigned the workflow around what the model makes possible, while keeping humans where humans belong.
Deployed. Not just demoed.
Agent Workflows
Multi-step AI agents that handle intake, routing, triage, or document processing — operating inside your existing systems, not alongside them.
LLM Integrations
Language model integrations that are actually scoped correctly — prompt engineering, context management, fallback behavior, cost control.
Human-in-the-loop Design
Systems where AI and humans collaborate — the AI handles what it's good at, escalates what it isn't, and the handoff doesn't lose the thread.
AI Strategy & Policy
For organizations navigating what to build vs. buy, what guardrails to set, and how to introduce AI without breaking what already works.
Real systems for real workflows.
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LLM-routed tutor escalation system with real-time confidence-gap detection. When the AI isn't certain enough, it hands off to a human TA with full context — no awkward handoff, no lost thread. Designed around how tutors and students actually communicate, not how the model works.
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AI-augmented internal workflows for organizations spending too much time on tasks that shouldn't need a human decision. Intake routing, document processing, scheduling intelligence. Deployed into existing systems, not alongside them.
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Advisory work on AI policy and implementation strategy. What guardrails actually change behavior versus what checks a box. Which integrations are worth building versus buying. The decisions organizations keep getting wrong because the vendor answer and the right answer aren't the same.
Let's design
your AI layer.
Most projects start with a short conversation — no brief required. Typical scope runs $5–15K and ships in weeks.