Ascenda
Designing for trust
Industry
SaaS
Enviroment
Web
Background
Nigeria's economy pulses with the energy of over 40 million small and medium-sized enterprises (SMEs). These businesses are the lifeblood of the country, contributing significantly to GDP and employment. Yet, these businesses struggle to scale because they lack access to the critical services provided by corporate service providers (CSPs).
The core challenge? Trust
Background
CSPs, including banks, insurers, and venture capitalists, hesitate to engage SMEs because of insufficient Know Your Customer (KYC) data. These gaps burden them with high investigation and verification costs, operational inefficiencies, and risks that CSPs aren’t always willing to bear. Conversely, SMEs lack the resources or knowledge to meet the rigorous compliance standards required to unlock these services. As a result, SMEs remain underserved, and CSPs miss out on untapped opportunities. The system perpetuates inefficiencies, with both parties relying on manual processes, word-of-mouth referrals, and lengthy paperwork that stifles progress.
Innefficient workarounds
The first step was understanding the intricacies of SME-CSP relationships. Through rounds of interviews, surveys, and focus groups, two themes emerged consistently:
CSPs needed a way to filter and connect with SMEs seamlessly, without unnecessary friction.
SMEs needed guidance and support to complete rigorous KYC processes.
Research findings revealed that SMEs generally resort to informal financing options, risking high interest rates and unreliable terms. Others attempt to engage CSPs but are often turned away due to incomplete or disorganised documentation.
CSP teams manually vet SMEs—a process that can take weeks or even months. This involves chasing down documents, conducting background checks, and multiple rounds of verification—all of which increase operational costs and delay decision-making.
% of people who struggle with macros dining out
68%
Source: The Times
Another insight that stuck with me? Decision fatigue. The more choices a person has, the harder it is to make a confident decision, especially when nutritional uncertainty is in play.
A smarter way to track nutrition
FoodNoms has always stood out to me because it does nutrition tracking differently. Where other apps feel cluttered, slow, and packed with ads, FoodNoms is clean, privacy-conscious, deeply integrated with Apple’s ecosystem, and automate food tracking through small design choices that make the task feel less like a chore.
But even the best nutrition trackers hit a wall when it comes to restaurants. Unless a restaurant provides nutritional information (which most don’t), users are left estimating. And if they’re serious about their fitness goals, “estimating” isn’t good enough.
I wanted to create a feature that feels as intuitive as the rest of FoodNoms, and the first design decision was input. Should users manually type in menu items? Maybe not, because that might feel like too much work, especially when dining out is meant to be social and enjoyable. Instead, I leaned on computer vision.
AI processes the menu, using OCR (Optical Character Recognition) to extract text, then applies natural language processing to recognise dish names and ingredients. It cross-references this data with a nutrition database (like USDA and FoodNoms' own dataset) to estimate macros, using machine learning to refine accuracy over time.
5 parties collaborate to analyse and present nutritional information
A key finding from my research into AI-assisted food tracking was that users are more likely to trust an AI-generated macro breakdown if they can verify and edit it. This shaped the next part of the experience: instead of assuming the AI would always get it right, I designed confirmation functions where users can adjust portion sizes or correct any mismatches. Providing a balance between automation and control.
Next came accuracy. Not all menus describe their food in detail, which meant AI needed to do more than just read text. It had to interpret it. For example, if a menu lists “Grilled Chicken Salad,” it should recognise that as a high-protein, low-carb option, even if the menu doesn’t explicitly mention macros.
This requires a smart database matching system, where FoodNoms cross-references menu items with existing nutritional data, adjusting estimates based on portion sizes and cooking methods.
Research into food tracking adherence also influenced my approach. Studies show that users are more likely to consistently track meals if they can do so within 10-15 seconds. This meant designing a minimal but powerful interface. A simple camera overlay for capturing menus, a clear and digestible (pun intended) macro breakdown, and an effortless way to log meals.
Designed to guide, not dictate
Another challenge I had to consider was decision-making in real time. Beyond tracking their meals, people want guidance on what to order. This led to the creation of “Suggested Picks”, a subtle feature that highlights meals aligning with a user’s preset dietary preferences, without forcing a decision. So the app helps you make the best choices clear at a glance.
Feedback and learnings
Most homes are dynamic spaces with a collection of artefacts that require care. The app seamlessly integrates maintenance tracking, with timely reminders for servicing appliances, replacing filters, or checking on valuable electronics. These reminders offer helpful insights, some with links to verified DIY practices.
This is another area where automation makes a difference. Instead of users having to remember when their washing machine needs servicing or when to change the air filters, the app predicts maintenance schedules based on manufacturer recommendations and usage patterns.
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Last updated December 2024.