My Role
I led the design of InfyU labs Potato Farm app across iOS, Android, Handheld devices since the outset of the project in June 2023. Up until March 2024, I led efforts to evolve the experience and address customer pain‐points related to the app’s features and discovery experience.
Customer Insights & Ideation
I partnered with three project managers and one tech lead to uncover insights and translate concepts into features that address customer behaviours and motivations.
Experience Strategy & Vision
I created frameworks and prototypes to share the vision, design principles and content strategy. This helped to evangelise ideas, gain alignment and drive decision making.
Planning & Scope Definition
I defined the product with my project manager partners. I evangelised customer goals and balanced business goals. I prioritised and negotiated features for launch and beyond.
Oversight & Coordination
I designed across with stakeholders , founders and PMs to translate product features for each platform context.
Design Execution & Validation
I designed down on Excalidraw, Adobe illustrator, & Figma. I executed journeys, wireframes, prototypes and design specs.
Leadership
I designed up and presented works to gain buy‐in from executives, inverstors and many other InfyU teams throughout the project lifecycle.
The Challenge
Create Deeper Relationships
with Customers
Since 2015, Agri. tech has seen a rise in tech innovation in the world, and now a sizeable proportion of the Indian agriculture industry’s new-age collaboration is made up of digital services. For InfyU Labs, this signaled a rapid change in day-to-day farm-to-trade activities.
Our challenge was to evolve with customers and enter the less tech-savvy competitive market where InfyU Labs would be instrumental in bridging the gap between advanced technology and traditional practices. InfyU Labs would offer a limited selection of trade services and farmer directories exclusive to their registered users.
With this new benefit, we hoped to create deeper relationships with InfyU Labs' customers.
The Approach
Good Fast Cheap
To speed up the time to market, I was tasked with designing and building the app within the existing Trade, directory, and other provided services architecture. This approach was seen as advantageous and the least risky.
The idea was straightforward: we already knew a set number of customers visit the InfyU app every day. By extending the familiar, Buy, Sell, and Explore model and using the existing infrastructure, we could get to market faster and at a lower cost. This early architectural decision significantly impacted the quality of the customer experience I could both create and reconcile.
Chasing Waterfalls
Feature design and development were broken into parallel work streams for all the Services, newly added Community section, and Directory. I led the design for all aspects related to all stated above.
Each feature phase of the project was serialised, starting with the design and development. Once each feature was designed and approved, the engineering team began the implementation.
I followed by working with backend developers to translate product features for their platform’s context. Concurrently, I would design the next feature in the pipeline, whilst also working with my own platform engineering teams to execute the current feature through to completion.
“The combination of a fixed launch date and aggressive scope created an intense environment with many coordination and time challenges.”
Working backwards from a fixed launch date, meant that design was subsumed into an engineering‐driven process. Sign‐off milestones were driven by engineering estimates and time to create the right design was the time left over. The combination of a fixed launch date and aggressive scope created an intense environment with many coordination and time challenges.
The Discovery
We conducted customer and market research to drive our planning phase. These are the key insights that defined the launch version of the product:
Optimised Crop Management
The primary segments are customers who value real-time data and actionable insights for managing their potato crops. Features like weather forecasts and community support help them make timely decisions, improving yield and reducing losses.
Access to Market Trends
Farmers are keen on understanding current market prices and trends for potatoes. Providing up-to-date market data and price forecasts allows them to make informed decisions on when and where to sell their produce for the best returns.
Resource Management
Users are interested in tools that help them manage resources like water, fertilizers, and pesticides more effectively. Cost tracking and optimization features can help them reduce expenses and increase profitability by identifying areas where they can cut costs or improve efficiency.
Community and Support
Farmers appreciate being part of a community where they can share experiences, seek advice, and get support. Features that enable interaction with other farmers, access to expert advice, and solutions to common farming challenges enhance their experience and foster a sense of connection.
How We Got There
The biggest challenge I faced throughout this project was balancing moving forward with designs, whilst collaborating with the wider team. Since this project touched every part of the business I needed to coordinate and get buy‐in from many teams that were both co‐located and distributed. This was hard.
Managing feedback was even more challenging and felt like a swinging pendulum of viewpoints.
I observed this pattern early enough in the project and invested time into creating documentation to help alleviate the data crutch and better articulate and distribute design rationale. Doing this upfront was quite time consuming, but saved a lot of back‐and‐forth as the project progressed.
Design principles and the content prioritisation framework plus I.A. helped to create visibility into my decision‐making process and galvanise the team to share in the vision.
Communicating Design
The sheer size of this project and structured waterfall approach meant that I needed to have everything figured out before teams would commit to moving forward with the work. Many teams involved in the project needed to see it in a tangible document. This risk averse mindset meant I created a lot of reference documentation that was widely distributed and a high overhead to maintain.
My process involved sketching and white‐boarding concepts and flows with stakeholders and then translating these directly into hi‐fidelity design comps. Since I was working with a few existing design patterns, This too became an additional task with a very tight timeline, making it relatively straightforward to transition directly into high-fidelity designs.
Once sketching was dine and approved from all stakeholders. My next step involved slicing the comps and piecing them together with Figma into a prototype. In the early stages I focussed only on representing the highest risk areas of the design. Later phases allowed me to focus on micro‐interactions, which I created again in Figma.
I created sets of documentation during this project to communicate requirements to the engineering team and support our quality assurance teams in writing test cases. This documentation required the most rework during the project and was the highest overhead to maintain.
The Execution
Bringing it all to life
The prototype shows some of the app designs for InfyU labs app.
Testing Our Assumptions
Once the team had a prototype ready for use, we knew we needed to put it in the hands of our customers.
2 months before launch we doubled‐down on validating our wildest convictions. We held an extended beta and conducted Guerrilla user testing which highlighted the top risks in the product to be
Performance and stability
Wayfinding
The complexities of placing & tracking bids and the overall trade section.
Interestingly, the themes we discovered during this time were the same issues we discovered after launch.
The Launch : Pulling The Trigger
The launch went off without a hitch—an amazing achievement considering the scale and complexity of the deployment.
