Private Views
Client
Artlogic
Role
Lead Product Designer
Type
Product Design, Research, Strategy
Project Overview
Private Views is one of the most-used features on Artlogic, the market-leading SaaS platform for the art world. It lets galleries, artists and collectors create and share artwork presentations with colleagues, customers and prospective buyers.
This work reimagined a classic feature, to add more value and lay the groundwork for its future. I made the case for investing in the feature, used customer research and product data to define the direction, then integrated AI into the process — from exploring ideas and building prototypes to connecting our design system with product development.
What I did
Made the case for investment, backed up by product and billing data
Interviewed high-usage customers to understand workflows
Analysed feedback and product data to identify patterns and opportunities
Defined the direction and principles for the new experience
Worked with PM to define requirements and shape a phased launch
Ran a phase of AI ideation and prototyping, to explore ideas quickly
Created and tested working prototypes with customers
Designed the final experience and worked with engineers to bring it to life, using AI and agentic development connected to design files & components
Team — PM, Engineers, UX Researcher
Research & Discovery
Understanding the Opportunity
200k
<5mins
<10%
I gathered product analytics, customer interviews, competitor audits and feedback from across the business, then presented back standout insights. We looked at these together and discovered a tension. Balancing these became a guiding principle…
01 — Room to grow
Customers wanted more flexibility, while competitors were advancing their own presentation tools. There was an opportunity to make Private Views more capable and competitive.
02 — But don't lose the simplicity
The original experience was successful because it was fast and easy to use. It served a broad range of gallery roles and supported everything from sales and marketing to administration.
We then ran further workshops — co-ideating with stakeholders and journey mapping opportunities. Finally, opportunities were grouped into themes and broken into a phased launch, favouring MVP and real-world feedback.
Ideation & Validation
AI-Assisted Solution Design
To explore ways of improving the feature, I leaned into AI to rapidly conceive and validate ideas.
I was able to transform basic wireframes and ideas into (mostly) working prototypes in hours, rather than days or weeks. Hooked up to design system components, I created flows then worked with our UX Resercher to get them in front of customers and find out what worked and what didn't.
This massively reduced the cycle time between idea and validation.
Build & Launch
Ship in stages & learn as we go
We launched the first version as an invite-only beta, then learned from real usage and feedback as we built towards the wider release. AI helped connect the final designs to the existing design system, while phased delivery let us ship useful functionality early and iterate from there.
