Evai — AI-Driven Agent
An advanced AI-powered agent framework designed to optimize enterprise workflows — and to outperform current market competitors on the moments that matter.
UX research & design
+ follow-on research
Competitive analysis
Product strategy
Enterprise teams spend a large share of their day stitching together context across tools — pulling a number from one system, a status from another, a policy from a third. AI agents promise to close that gap, but most on the market are generic chat layered over an API: impressive in a demo, frustrating in a real workflow.
The opportunity for Evai was to design an agent that understands enterprise workflows specifically — one that earns trust by being accurate, transparent, and bounded, rather than broadly conversational.
I ran discovery interviews with enterprise users to understand where their workflows actually break, then synthesized the patterns into the themes that drove the concept. The goal was to validate a real need before designing a single screen.
I mapped the journey of a typical task to find the moments where the agent could add the most value — and the moments where it had to step back and hand control to the person.
I benchmarked the leading agents against the dimensions users actually cared about. The gap was clear: competitors led on open-ended conversation, but fell short on transparency and workflow fit — exactly where enterprise trust is won.
A working prototype and high-fidelity Figma frames brought the concept to life — the agent embedded in the workflow, the reasoning made visible, and the human confirmation step.
Live screens from the Evai product design — the AI concierge embedded across package selection, signing, and the client deal flow.
The research turned a hackathon idea into a defensible product strategy: an enterprise agent that wins on trust, not novelty. The journey map and competitive framework gave the concept a clear point of differentiation and a sequence for what to build first.
What I took away: with AI products, the design problem is rarely the model — it's the interface of trust around it. Transparency, boundaries, and a human checkpoint weren't features bolted on at the end; they were the concept.