Eltropy Fintech SAAS platform
Increase adoption of AI Assistants to improve efficiency of credit union staff.
MY ROLE:
As the primary designer and product owner, I was responsible for:
UX strategy and project plan
Research plan and recruiting
User interviews and synthesis
Secondary and competitive research
Customer feedback facilitation
Review of Interaction design
Review of UI design
ABOUT THE COMPANY:
Eltropy's conversations platform helps credit unions and community banks talk to their members but the AI Assistants and Agents built into it were going largely unused. I led design and UX strategy to change that: rethinking how the feature was surfaced, understood, and trusted by frontline teams, and building the visual system now used across the rest of the product.
ABOUT THE TEAM:
I joined Eltropy in November 2024 to lead design on Assistants and Agents adoption, working with 1 junior designer and a cross-functional team of 3 product managers, 1 engineering manager, and 2 additional designers. Over an 18-month engagement, the mandate expanded from a single feature push into a broader design system overhaul — and into growing the design org itself.
MY STRATEGY:
Eltropy had entered the AI space early, but end-user adoption of the new technology remained low. When I joined in November 2024, the product organization was in a "growth-at-all-costs" phase — the team had successfully broken into the market, but design and product processes hadn't scaled to match.
My role was to stabilize the product through quick wins while architecting a longer-term strategy for scale. I followed the below steps:
Get Users voice in the product:
I started with research: talking to stakeholders and end users, studying competitors, and running design sprint workshops. With no usage analytics available, I introduced a problem-and-solution discovery sprint cycle and mined support tickets to surface real usage patterns,
Translate findings to create design mockups
The design team worked to translate those findings into a scalable, modular interface validating with qualitative feedback post-release in the absence of quantitative data.
The bigger shift was moving the team from feature-driven releases to outcome-driven discovery, bringing user feedback directly into the product roadmap. This required a complete shift in how the product organisation functioned.
The design team extensively leveraged AI to adopt the double diamond process to bring discovery at the heart of release without losing speed.
After building the roadmap, I opened 2 openings so that design could effectively support the business strategy.
Core business problem statement:
How might we reduce the time it takes a credit union to launch a new AI Assistant/ AI agent? How might we empower end users to update an AI Assistant/Agent themselves? How might we help credit union staff to measure the value an assistant is delivering?
Old experience of creating AI Assistants:
THE SOLUTION:
STEP 1: A new information architecture to create an AI Assistant
This helped reduce time to create a new assistant and dependency on the implementation manager. Used the principle of progressive disclosure to display frequently used settings upfront and hide the less used settings to reduce cognitive load.
Empty state of AI assitants
All knowledge assistants filtered by category
New Information architecture
Different types of fallback to solve for edge cases
Centralised knowledge system to reduce the number of updates for a user
Used progressive disclosure to group less used settings at the end
Step 2: Integrated AI Assistant within Analytics & Reporting
The Ai Assistant helped make sense of complex data instantly. The user would be able to create custom dashboard, metrics and understand metric definitions to take data driven business decisions.
Onboarding to create a personalised experience
Metric definitions to build trust on data
A new analytics homepage with relevant data upfrontMetric definitions to build trust on data
AI Assistant integrated within Analytics to provide quick insights
Ability to dive deep using AI Assistant to understand the details of a metric
Custom dashboards with clear hierarchy
IMPACT:
1. Ready to test, launch and learn
The new settings experience was released for all 200 customers but the new analytics and reporting experience was launched in alpha stage with 5 customers.After doing an initial analyses on usage, the number of users increased by 75%. This change in how we built Assistants and utilise it within the product bought in more modularity which would then be translated to AI voice and chat agents as well.
Questions for next round of testing of Analytics and reporting:
How do we scale analytics and reporting to make sure that Eltropy becomes the source of truth across all communication channels
How do we handle AI hallucinations when it comes to data problems
2. Insights from customer research informed the go-to-market strategy and other teams’ work
Including users voice early empowered the product team to take more confident decisions and help iterate on the designs quickly.
3. Improved visual design system
Since these changes required re-building the entire UI, this gave us an opportunity to update the 4 year old Eltropy's design system. This reduced feature time-to-development by 21% and was a crucial step to reduce design debt as the design team increased in number.