A multi-device system to enhance how Alexa users interact with Hunches by making notifications more personalized and less intrusive, while increasing transparency around the impact of user feedback to encourage continued engagement.
As a UX Design Intern, I led the end-to-end research and design of the second iteration of Spaero’s MVP, focusing on improving adoption and usability for scientists. I collaborated with the founder, lead designer, and PM to prioritize feature development, designing two key features from the ground up and achieving a 38% increase in customer satisfaction
Timeline & Status
3 Months, June 2023 - Sep 2023
Team
1 UX lead
1 PM
1 Automation engineer
5 Devs
1 UX intern (me)
Platform
Web application
My Role
UX intern
Contribution
Primary research
Competitor analysis
Concept ideation
Prototyping
High-fidelity designs
Overview
When I joined Spaero, the team had already launched an initial MVP for their lab automation platform. My role was to improve the next version by observing scientists in real lab environments using automated liquid handler setups.
I led the end-to-end redesign of the experiment creation and simulation workflows, identifying usability gaps through research. I proposed workflow improvements, mapped user journeys, and prototyped new features to better align with scientists’ needs.
Following my internship, the features I designed were tested with users, resulting in a ~38% increase in user satisfaction based on usability testing.
Problem context
Lab automation has scaled up experiments while allowing scientists to focus on high-level science
Healthcare and pharmaceuticals have undergone a profound transformation over the years, shifting from traditional approaches to highly advanced and technology-driven systems.
Traditional Life Sciences
Modern Life Sciences
But despite its advantages, lab automation has a high barrier to entry, leading to slower adoption
Steep learning curve
OEM softwares are difficult to learn and are not intuitive enough for first-time users to adopt
Scientists manually run experiments
Scientists prefer to do things by hand which is inefficient and prone to errors
Requires an automation engineer
For high-throughput experiments, automation is a must and needs an additional automation engineer
Gaps in knowledge transfer
Due to differing expertise between scientists and automation engineers, experiments are more error prone due to gaps in knowledge transfer
Caregiving often feels like a second job—one that most aren’t prepared for. Caregivers must juggle multiple roles and responsibilities, often all at once, with little guidance or support.
A desktop web application that streamlines the creation and debugging of lab automation experiments through intuitive workflows, enhanced transparency, and alignment with scientists’ mental models.
Final solution
library of materials
This step organizes reusable experiment components, such as liquids, reagents, and labware, into a structured system. Scientists can define starting contents, volumes, and concentrations for each material, making setup faster and more consistent across experiments.
Pain points addressed:
Reduced barrier to entry
Easier experiment creation
Build experiment step-by-step
A more visual drag-and-drop mode of creating experiments from the previously created library of materials. This flow follows the mental model of the scientist and allows them to easily retrace their steps in case of errors.
Pain points addressed:
Reduced barrier to entry
Easier experiment creation
note-taking
This step bridges the scientist’s real-world benchwork with the digital experiment setup. Scientists can capture notes or upload images at each step, preserving their thought process and ensuring consistency between planning and execution.
Pain points addressed:
Reduced barrier to entry
simulation of experiment
A pre-run simulation screen visualizes the location, volume, and concentration of each liquid at every step. By making each action transparent, it helps scientists spot and correct errors before executing the actual experiment.
Pain points addressed:
Better transparency during runtime
Tracking the experiment from the POV of a single liquid
A focused view that filters the simulation by a single liquid, allowing scientists to trace its movement, volume, and concentration across all locations throughout the experiment. This helps ensure every action involving the selected liquid is clearly tracked from start to finish.
Pain points addressed:
Better transparency during runtime
What type of experiments do scientists typically perform
What is their typical process like
When does lab automation help them vs hurt them
At what points in their process do they face issues
I interviewed and shadowed scientists and researchers in university biochemistry labs to better understand their workflow
Research
My research was fairly open-ended and I aimed to understand the following:
Most scientists followed a typical workflow from designing the experiment manually, to building and execution on the liquid handler robot
How might we help scientists automate lab experiments by making the process more intuitive and transparent
Project scope
I got together with the founder, PM and design lead to decide which user needs to focus on for version 2 of the MVP
Reduced barrier to entry
Users needed the process of automating experiments to match their mental model
Easier experiment creation
Users needed a more intuitive way to reference lab equipment when creating experiments
Better transparency during runtime
Users needed better traceability of liquid and labware movements during runtime
User needs
I performed a competitor analysis to identify common gaps in existing products and refine the focus of the user needs uncovered during research
Choice of competitors
Based on brand popularity
Analysis lenses
Ease of use, barrier to entry, transparency of system status
The competitors I analyzed had common drawbacks that presented me with design opportunities
Complicated labware definition
The process of defining starting liquids, volumes, concentrations, and labware was confusing
Unintuitive experiment creation
Experiment creation didn’t follow the scientist’s mental model
Unintuitive simulations
Simulations lacked key details like volumes and concentrations that were important to the experiment
I had multiple concept ideas, so I created quick prototypes and presented them to my team for feedback
Ideation
Tooltips
This feature provides first-time users with tooltips through each step of the process to provide them with quick, contextual guidance as they navigate the product.
Idea scrapped
Why?
It did not make sense for a minimum viable product. It could be the focus of a more established product.
Real images of labware in library
Using real product images instead of illustrations helps scientists more accurately choose equipment.
Idea scrapped
Why?
It did not make sense for a minimum viable product. Also, product images from different manufacturers were not readily available.
Renaming labware
This feature enables users to rename labware from complex technical terms to simpler ones, making it easier to reference during experiment setup and execution.
Pushed to future iteration
Why?
The team liked the idea but it did not match the vision for the MVP.
I handed off the designs to the design lead and PM to perform user testing on our clients
Testing and impact
Conclusion and Learnings
I thoroughly enjoyed worked on this project. It was my entry into a pretty niche industry which was interesting but also came with a lot of challenges
A highly closeted industry
Getting onboarded into the industry was difficult as there were not many publicly available resources and most major players in life sciences kept their products and services close to the vest. Most companies were B2B.
Limited access to target users
I was working remotely for a smaller startup with limited customers. Therefore, my access to users was limited to those in academia and in my city.
Difficulty in measuring impact
I was working on the next version of the product while the rest of my team worked on the MVP. This made it harder to visualize and measure impact. My work did not end up getting implemented.
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