My role
I designed end-to-end interaction and visual design of the product, from initial concept to MVP.
The machine learning team had an early prototype, and I refined and defined the full interaction model, visual design, and overall experience.
I have also worked closely with our marketing team to define visual language.
Opportunity
Tia helps recruiters surface top candidates faster and with greater confidence
Surfacing answers to questions like “Why is this candidate the best fit?”, improving their efficiency and understanding of their candidate pool.
Market research
Since most recruiters manage candidates in their ATS (Applicant Tracking System), we embedded Tia into our Chrome extension so they can ask questions while browsing candidates.
Research into how AI is being integrated across B2B products have surfaced a common pattern embedding AI natively within the platform, but that approach had limitation for us as a portion of our users review candidates outside of our platform - in external ATSes (Applicant Tracking System).
We already had a Chrome extension in place for interview scheduling. It made sense to integrate Tia directly into it, giving users access to AI assistance while browsing on their ATS.
Brainstorming
Expanding touch points users might need Tia for beyond identifying candidates: Discover, comparing and generate
Mapping out the full hiring journey, we have uncovered opportunities for Tia well beyond candidate search: from drafting offer letters to developing training plan based on their weaknesses.
Working with engineers, we prioritised tasks for MVP, set others aside for later releases based on feedback, and grouped them into 3 query categories:
Discover: Top candidates on the role
Comparing: Between multiple candidates
Generate: Decline and acceptance emails, interview questions or training plans based on candidate data
To prioritise a fast release, the team focused on adoption feedback and deprioritised other ideas in favour of lowest possible effort
Branding
The visual direction was to feel smart and professional while staying true to our brand colours pink and purple
Our marketing designer was responsible for the Tia logo, and I designed the visual UI for the Tia interface.
Drawing on my design expertise and consolidated team feedback, I ultimately landed on a lighter palette using these colours, as it felt cleaner and allowed more focus to the content.
Final design
Simple onboarding screens demonstrating capabilities
With a simple onboarding guide for first time users
Context is everything
When recruiters are viewing a job in their ATS, we incorporate that context into the chat and evaluate candidates based on the role they’re looking at, creating a more relevant and contextual experience.
Sufficient guidance to deliver value
A blank chat input can be confusing, so a prompt library guides users through the hiring process. I also proposed dynamic prompts tailored to the specific job being viewed - scoped out of MVP to revisit after user feedback.
Delightful waiting experiences
We surface Tia's reasoning in real time, keeping users informed and bringing transparency to how top candidates are evaluated.
Crafting responses in a digestible manner
The target users aren't tech-savvy and prioritise speed, so the interface is kept visually clean and responses easy to digest. Core AI tasks were mapped to cards, each with clear follow up actions to keep the workflow moving.
Candidate cards were designed around what talent teams actually need: scoring, email for quick outreach, and a tight 2–3 bullet summary of key highlights.
Measuring success
Explicit and implicit feedback were both monitored by the team
For our MVP phase, adoption is the key metric we want to ramp up.
Explicit feedback are collected through “thumbs up” and “thumbs down” in our responses
Implicit feedback were metrics such as:
- Average duration and adoption of chat
- Whether users were asking the same questions again
- Interaction between the tasks cards designed
This project is currently in the pre launch phase, so its success metrics are not yet available.
Learnings and reflection
As AI products grow more complex, the designer-engineer relationship becomes critical.
Understanding the capabilities and constraints of AI informs how we design experiences that feel intuitive - meeting users where they are, and surfacing the right guidance at the right moment.
Furthermore, understanding what customers might ask are hard to test and assume as there is no linear flow in a non-deterministic chat. Feedback becomes essential as it can reveal how they would naturally talk to AI.