I still remember the first time someone mentioned Chamsmobile to me. It was during a casual conversation about digital payments and identity systems in Nigeria, and a friend said, “Have you checked what Chamsmobile is doing?
They are not loud, but they’re doing real work.” Out of curiosity, I went online to see for myself, and honestly, that small search opened my eyes to a brand that deserves far more attention than it gets.
What I love about Chamsmobile is that they are building solutions for realities we face every day in this country, not fantasies.
Many tech companies chase hype; Chamsmobile builds quietly for people who need technology the most: market women, transport workers, SMEs, civil servants, and communities usually left out of the main conversation.
One thing that really stood out to me is their focus on digital identity and financial inclusion. In a country where a simple thing like verifying someone’s identity can delay jobs, stop access to credit, or complicate business transactions,
Chamsmobile found a way to simplify it with their Kegow platform. It’s not just about transferring money. It’s about helping people prove who they are, opening accounts easily, and accessing financial services without stress.
I also respect how they operate. They don’t try to be everywhere at once; instead, they focus on solving real administrative and financial gaps in Nigeria.
For example, the way they’ve built partnerships with government agencies and private organizations shows that they understand the Nigerian system deeply. They’ve also created room for agents and small business owners to earn through their digital services, and for me, that’s one of the most practical ways a tech brand can empower ordinary people.
Another thing I admire is the trust they’ve built over the years. In a space where many fintechs rise fast and crash even faster, Chamsmobile is consistent.
They don’t make noise, but they deliver. They’ve been part of Nigeria’s digital identity journey long before it became trendy, with the help of their parents’ company, Chams Hold Co, and they’ve stayed committed to it through every challenge.
Chamsmobile makes me proud because they represent what African tech should be: innovation rooted in real problems, solutions created with Nigerians in mind, and a quiet confidence that speaks louder than hype. They may not always trend on social media, but they are impacting lives in a way that truly matters.
For me, that’s the kind of tech brand worth celebrating.
The idea for Trackiose did not start in a boardroom. It started at the front desk of a hotel.
I was working in hospitality at the time, and I saw the same pattern every day. Guests would leave negative reviews about issues that could have been fixed. Slow check-in. Staff not knowing basic room details. Inconsistent service.
One experience stayed with me.
As a guest, I stayed at a supposed five-star hotel that did not have a walk-in shower or a working telephone. These were not complex problems. They were operational gaps.
The frustrating part was not that feedback did not exist. It did. Hotels collected reviews, surveys, and mystery shopping reports. The problem was what happened after.
Most of that feedback never translated into real improvement.
Management would review reports, discuss them, and sometimes plan training. But by the time anything changed, weeks had passed. The issues had already repeated themselves.
At the same time, I had spent years in HR and talent development, working with enterprise systems that large organizations used to improve performance. The contrast was clear.
Large companies had structured systems. Most small and mid-sized hospitality businesses did not.
That gap is what became Trackiose.
A Fragmented System That Does Not Scale
The traditional approach to customer experience in hospitality is fragmented.
A business might use a mystery shopping agency, spend hours manually checking online reviews, and occasionally run surveys. Training, when it happens, is often reactive and generic.
These systems do not connect.
Insights from reviews are not linked to training. – Feedback is delayed. – Decisions are based on incomplete information.
In a fast-moving environment like Lagos or Abuja, this delay has consequences. Poor reviews accumulate. Revenue is lost. Staff disengagement increases.
More importantly, managers cannot answer simple questions like:
What is our biggest service issue right now? Is our training actually working?
The problem is not a lack of data. It is the lack of structure.
From Feedback to Action
The turning point came when I tried to solve the problem manually.
I analysed reviews for a restaurant and noticed a recurring issue. Staff could not confidently answer questions about food allergies. I created a short training video and shared it with the team.
Within days, a customer specifically praised a staff member for their knowledge. That feedback turned into a top review for the restaurant.
That was the insight.
The value is not just in collecting feedback. The value is in turning it into action quickly.
Trackiose was built around that idea.
Building an Integrated System
Trackiose combines three core functions into one system:
Feedback collection from online and on-site sources
Sentiment analysis to identify patterns
Automated training delivered to staff
Instead of waiting weeks, insights can be converted into training within a short time frame.
Staff can access these learning modules on their phones, making it easier to apply knowledge in real time.
The goal is simple. Reduce the gap between feedback and improvement.
Early Traction and Impact
Trackiose is still in its early stage, but the results from pilot customers are encouraging.
The platform currently works with hospitality businesses and financial institutions across Nigeria and Ghana. It has generated hundreds of personalised training modules and built a growing network of mystery shoppers.
One of the most meaningful outcomes came from a restaurant group in Lagos.
After implementing Trackiose:
Their review rankings improved significantly
Customer ratings increased
Service quality scores rose across locations
Time spent managing customer experience dropped from hours to minutes
But the more important change was internal.
Staff reported feeling more supported, and some progressed into leadership roles. That shift reflected something deeper than metrics.
It showed that structured feedback can improve both customer experience and employee development.
Lessons From the Journey
One of the most important lessons has been to build from lived experience.
Understanding the problem at an operational level makes a difference. It shapes how solutions are designed and how they are adopted.
Another lesson is that speed matters.
In many businesses, the gap between feedback and action is too wide. Closing that gap creates immediate value.
Finally, in emerging markets, context matters.
The challenges, behaviours, and constraints are different. Building with that understanding is not a limitation. It is an advantage.
Looking Ahead
Trackiose is focused on scaling its platform across Nigeria and expanding into new markets.
The goal is to improve automation, reduce manual intervention, and build a system that can operate at scale.
Beyond growth, the broader vision is clear.
To create a system where customer feedback does not just exist, but leads to meaningful change. Where staff are supported with the right information at the right time.
And where small and mid-sized businesses can access tools that were previously limited to large organizations.
Because when feedback turns into action, performance improves.
Right now, that object is AI. Every startup pitch deck mentions it. Every product roadmap promises it. Every conference panel dissects it. And yet, for all the noise about artificial intelligence revolutionizing product development, most organizations are building AI features that nobody asked for, solving problems that don’t exist, and wondering why adoption rates are dismal.
Here’s what they’re missing: technology has never been the bottleneck in product innovation. Understanding human behavior is.
I’ve spent nearly two decades building digital products across Nigerian fintech, capital markets, and UK healthcare, markets where the stakes are high, the margins are thin, and users have zero tolerance for products that waste their time. What I’ve learned is that the most transformative products aren’t the ones with the most sophisticated technology. They’re the ones that deeply understand what people actually need and remove every barrier standing between them and that need.
AI doesn’t change that equation. It amplifies it.
The Empathy Deficit in Product Development
Let me tell you what I see when I look at most “AI-powered” products hitting the market: solutions in search of problems.
A chatbot that forces users through ten questions when they just want a phone number. An AI recommendation engine that suggests things nobody wants because it’s optimized for engagement metrics, not actual usefulness. A “smart” interface that’s so clever it confuses the humans who need to use it.
The problem isn’t the AI. The problem is that the teams building these products never stopped to ask the most important question: What is the actual human problem we’re solving, and is AI the right tool to solve it?
I learned this lesson early, not in a boardroom, but on contact center floors managing customer complaints. You hear what people are actually frustrated about. You see where systems fail them. You understand the gap between what executives think customers want and what customers are actually experiencing.
That ground-level empathy, the ability to see your product through the eyes of someone who doesn’t care about your technology stack, who just wants to accomplish a task and move on with their life, is the foundation of good product development. Always has been. Always will be.
AI should enhance that empathy, not replace it.
What AI Actually Does Well (And What It Doesn’t)
Here’s the uncomfortable truth about AI in product innovation: it’s exceptionally good at scale, pattern recognition, and automation. It’s terrible at understanding context, nuance, and what people actually care about.
AI is able to analyze millions of data points and reveal patterns that no human would ever know existed. That’s a powerful thing. I’ve had experience with big data and predictive analytics to understand that if you have the right data infrastructure in place, AI will deliver insights that change the way you interact with customers.
But this is what AI cannot do: it cannot tell you why the customer is agitated. It cannot understand that because a customer fell off in the middle of your product is not because the interface was confusing, it’s because they got a call from their kid’s school and had to handle an emergency. It can’t differentiate between a user that is struggling seriously and a user who is browsing your product out of curiosity.
Context is everything. And context requires empathy.
The best product teams I’ve worked with use AI to handle the things humans are bad at, processing massive datasets, identifying patterns at scale, automating repetitive tasks, so that humans can focus on the things AI is bad at: understanding what people actually need, designing experiences that feel intuitive, making judgment calls when the data is ambiguous.
Designing for Humans, Not Algorithms
One of the most dangerous trends I see in product design is groups coding for their algorithms instead of for their people.
I’ve seen this play out in financial services, where banks build loan approval systems optimized for risk models that are technically sound but produce outcomes that feel arbitrary and unfair to customers. I’ve seen it in healthcare, where digital platforms optimize for operational efficiency but create experiences that leave patients feeling like case numbers instead of humans.
The technology works. The algorithms are accurate. But the products fail because nobody stopped to ask: What does this feel like from the other side?
Enterprise Design Thinking, which I’m certified in, teaches you to start with the user’s needs and work backward to the technology. Not the other way around. It’s a simple principle, but it’s surprising how often organizations ignore it when they’re excited about new tech like AI.
Here is the test that I use: Can you tell me what your product does and why it matters without talking about the tech? If you can’t, then you are building a feature, not solving a problem.
When I led transformation projects, whether it was optimizing customer engagement strategies at scale or redesigning digital health experiences, the question was never “What can the technology do?” It was “What do people need, and what’s getting in their way?”
Once you answer that, the technology choices become obvious.
The Products That Win: Invisible Intelligence
The best AI-powered products are the ones where users don’t even realize AI is involved.
Take the things you’re using day-to-day that seem effortless. That anticipate what you need before you ask for it. That removes friction without making you think about how they’re doing it. That’s not magic, that’s intelligent design backed by smart technology.
I saw this principle at work during a digital transformation project I led that drove over 400% subscription growth during the pandemic. The product wasn’t technically complex in terms of AI, but it succeeded because we obsessed over the user journey.
We identified every point where someone might drop off, get confused, or lose trust. We eliminated those points one by one. The technology served the experience, not the other way around.
Contrast that with products where the AI is front and center, “Look at our machine learning! Look at our algorithm!”, and users are left confused about what they’re supposed to do with it. That’s a product intended for a tech conference demo, not real humans trying to solve real problems.
The magic of excellent product design is making complex things simple. AI should be the engine in the under-the-hood, not the dashboard that you get users to drive.
Where Empathy and AI Actually Intersect
So, where does AI genuinely enhance empathy-driven product innovation? Three places:
1. Personalization at Scale
Empathy in product design used to mean “understand your customer segment.” Now it means “understand each customer individually.” AI makes that possible.
I’ve worked on customer engagement data models that used analytics to identify patterns in behavior, who’s about to churn, who’s ready for an upsell, who needs support before they even ask for it. When done right, this doesn’t feel creepy or invasive. It feels like the product gets you.
But here’s the critical part: the AI identifies the pattern; humans design the response. You don’t automate empathy. You use intelligence to know when empathy is needed and empower humans, or very carefully designed experiences, to deliver it.
2. Reducing Cognitive Load
Good AI removes decisions users shouldn’t have to make.
When I was streamlining customer onboarding, the goal was always to remove unnecessary steps. AI can do that quicker by pre-filling data, offering next actions, or taking users to where they need to go without asking them to wade through complex menus.
But you must be intelligent. The wrong kind of automation is frustrating, like chatbots that trap you in loops when all you want to do is talk to a human being. The right kind of automation is when the product appears to be doing things for you instead of pushing you down a scripted journey.
3. Surfacing Insights That Drive Better Decisions
One of the most powerful uses of AI in product development is helping teams see what they’re missing.
I’ve led discovery initiatives where data analysis revealed gaps in processes that were costing organizations millions, problems that were invisible because systems weren’t designed to surface them. AI can process massive amounts of operational data and flag anomalies, trends, or opportunities that human analysts would never catch.
But again: AI surfaces the insight. Humans decide what to do about it. The combination is powerful. AI alone is just interesting data. Human judgment alone misses patterns. Together, they drive transformation.
What This Means for Product Leaders
If you’re leading product development in 2025, here’s what you need to internalize:
Stop asking “How can we use AI?” Start asking “What problems are our users actually facing, and would AI help solve them better than other approaches?”
Invest in understanding human behaviour before you invest in algorithms. Customer journey mapping, user research, data flow analysis, voice of customer programs, these aren’t nice-to-haves. They’re the foundation. If you don’t understand what people need, your AI will optimize for the wrong things.
Design for outcomes, not features. Folks don’t care that you’re doing machine learning. They care if your product makes them get what they want faster, easier, or better than someone else can. If your AI isn’t moving those needles, it’s decoration.
Remember, empathy does not scale through automation. AI can help you observe patterns and make experiences more intimate, but you cannot replace human judgment, creativity, or the ability to perceive context. The best products use AI to augment human capabilities and not replace them.
Be willing to say no to AI if it’s not the right solution. In certain situations, the best solution is fewer complexity points of infrastructure, better process design, or simply removing unneeded complexity. Don’t add AI features just because they’re cool. Add them because they produce more authentic solutions, better than simpler solutions.
The Next Generation of Product Innovation
The products that will win in the next decade won’t be the ones with the most sophisticated AI. They’ll be the ones that use technology, (AI included) to genuinely understand and serve human needs at scale.
That requires empathy: the ability to see your product through your users’ eyes, to understand their frustrations and goals, to remove barriers instead of adding features.
And it requires intelligence: the ability to process complex data, identify patterns, personalize experiences, and make smart predictions about what people need before they articulate it.
The intersection of empathy and AI isn’t about making technology more human. It’s about making human-centered design more powerful.
I’ve seen transformation happen when organizations get this right, when they stop chasing technology trends and start obsessing over user outcomes. When they use data and analytics to understand behavior, not just measure it. When they design experiences that feel effortless because the complexity is handled invisibly.
That’s the future of product innovation. Not AI for AI’s sake. Not empathy as a buzzword in your mission statement.
Real empathy. Real intelligence. Real products that solve real problems.
Everything else is just noise.
Kehinde Ejukorlem is a digital transformation and product innovation leader with over 15 years of experience delivering technology-enabled solutions across financial services, healthcare, and emerging digital ecosystems. She has led digital innovation initiatives at organisations including AXA Health UK, Crowdyvest, Avon HMO, InvestNaija, and Diamond Bank (now Access Bank), where she has driven the adoption of digital platforms, data-driven decision-making, and customer-centric product design.
She played a key role in delivering the MTN Nigeria Public Offer digital investment platform, a landmark initiative that enabled millions of retail investors to participate in capital markets through a fully digital channel, advancing financial inclusion across Africa.
Currently contributing to digital innovation within the UK technology ecosystem, Kehinde specialises in digital strategy, product development, artificial intelligence, and enterprise design thinking. She also mentors emerging professionals and advises startups on product-led growth and digital transformation, while advocating for increased female representation in technology leadership.
Reevar was born from a simple but powerful question:
What if expertise didn’t depend on physical presence?
In a world increasingly driven by artificial intelligence and digital connectivity, knowledge still faces an old constraint, time. The most brilliant professionals can only be available for so many meetings, consultations, classrooms, or advisory sessions. Their thinking has depth.
Their insights have value. But their availability limits their impact.
Behind Reevar is a forward-thinking team of technologists, strategists, and product builders who saw this limitation not as inevitable, but as solvable.
The Gap They Chose to Solve
Before building Reevar, the team observed a critical paradox in the digital economy:
Professionals were building massive audiences. They were creating content libraries, hosting webinars and publishing thought leadership. Yet their true value, how they think, remains locked behind one-to-one access.
Content scales visibility. But thinking does not scale easily.
The team realized that what makes an expert valuable is not just information, it’s reasoning patterns, frameworks, decision-making logic, and contextual judgment. And none of that had been truly operationalized. So they built Reevar.
Turning Expertise into Deployable Intelligence
Reevar enables professionals to create AI-powered Digital Minds, structured, interactive representations of how they think, reason, and communicate within their domain.
These are not generic AI assistants. They are domain-specific, personality-aligned, reasoning-informed digital extensions of professional expertise.
Digital Minds on Reevar can:
Be discovered by individuals and organizations
Engage users in real-time conversations
Be embedded into websites, dashboards, and platforms
Function as scalable advisory or knowledge systems
In effect, Reevar transforms expertise into an employable, deployable, and scalable digital asset.
This introduces a new category of professional presence, one that is persistent, interactive, and not limited by geography or schedule.
The Team’s Philosophy
At its core, Reevar is built on a clear belief:
AI should not replace human intelligence. It should amplify it.
The team approached the platform not from a place of automation, but from augmentation. Their goal was not to create another tool for content generation, but to design infrastructure that multiplies professional impact.
With strong foundations in:
Artificial intelligence
Product architecture
Digital systems design
Human-centered experience
Strategic thinking
The team engineered Reevar to sit at the intersection of technology and human cognition.
Every feature reflects that philosophy, preserving authenticity while enabling scalability.
More Than a Product: A New Professional Layer
Reevar is not positioning itself as just another AI startup. It is building what could become a new layer of the internet, where expertise is interactive, deployable, and accessible in real time.
If social media scaled voice, and cloud computing scaled software, Reevar aims to scale human reasoning. The team understands that the future of work will be defined not just by automation but by cognitive productivity. By how effectively expertise can be accessed, distributed, and embedded into digital systems.
Reevar represents the beginning of that shift. And the team behind it is not simply launching a product. They are building the infrastructure for scalable human intelligence.
Financial institutions increasingly use 2-Factor Authentication (2FA) in their mobile apps to protect customer transactions. Traditionally, 2FA combines two different types of verification. This is usually something you know, such as a transaction PIN or one-time password, and something you have, such as your registered phone or a token.
In many modern transaction flows, after a user enters a transaction PIN or token, the app then requests biometric authentication. This could be Face ID or a fingerprint, depending on the user’s device. While this improves convenience, it raises an important question. Should device-level biometrics really count as a valid second factor for transaction authentication when the verification does not happen within the financialinstitution’s systems?
How Biometrics Work in Financial Apps
When a financial app uses Face ID or fingerprint authentication:
● The app does not see or transmit the raw biometric data. Instead, it asks the phone whether the biometric presented matches one of the biometrics already enrolled on the device
● The biometric data itself is stored securely on the phone, within protected hardware designed for this purpose. The app simply receives a yes or no response from the device. The financial institution never receives the actual fingerprint or facial data.
This means the institution is relying on the phone’s confirmation rather than directly verifying the user’s biometric identity.
Can Biometrics Count as a “Second Factor”?
Traditionally, 2FA is based on using two different categories of authentication:
1. Something you know, such as PIN or password
2. Something you have, such as a phone or token
3. Something you are, such as a fingerprint or face
On the surface, combining a transaction PIN with Face ID or a fingerprint appears to meet the definition of 2FA. However, there is an important distinction.
Because the biometric check is performed entirely on the device, the financial institution cannot independently confirm who was authenticated. For this reason, device biometrics are often best viewed as a strong convenience layer rather than a fully independent second factor on their own. They work best when combined with other institution-verified controls, especially for high-risk transactions.
Security Risks and Edge Cases
1. Device Access by Another Person
If another person is able to register their fingerprint or face on a phone, whether intentionally or under pressure, financial apps that rely on biometrics may treat that person as a legitimate user.
2. Device Trust Over User Identity
Because the biometric decision comes from the phone, anyone who can unlock the device and add their biometric may be approved during transactions. The financial institution has no visibility into changes made at the device level.
3. Spoofing and Advanced Attacks
While modern phones include strong protections, biometric systems are not immune to spoofing attempts such as fake fingerprints or manipulated facial images, especially on older or less secure devices.
4. Biometrics Cannot Be Changed
Unlike a PIN or password, biometrics are permanent. If they are compromised, they cannot simply be reset. This makes them riskier as a primary security control over time.
Is It Safe to Use Device Biometrics as Part of 2FA?
Benefits
● Convenience. Transactions are faster and easier for users.
● Device-level protection. Biometric data is stored securely on the phone and is difficult for remote attackers to extract.
Limitations
● Dependence on the device. If a phone is lost, stolen, or compromised, risk increases.
● No direct verification. The financial institution cannot confirm who passed the biometric check.
● Accuracy limits. Biometric systems can sometimes fail or incorrectly approve access.
Regulatory and Legal Considerations
● Most financial institutions clearly state in their terms that enabling biometric login means any biometric registered on the device may be able to access the account.
● Device manufacturers aren’t legally liable for financial fraud from financial app transactions authenticated via device biometrics. Responsibility typically rests with the financial institution’s policies and local financial regulations.
● In many regions, financial institutions must still comply with privacy and data protection laws related to biometric use, even when the data is stored only on the device. However, this does not automatically transfer liability to device providers.
In conclusion, device biometrics add meaningful value by improving convenience and strengthening security at the device level. However, they are not a perfect or fully independent second factor when used alone for transaction authentication. For users, it is important to maintain strong device security, protect phone access codes, and understand how biometric authentication works.
For financial institutions, biometrics are most effective when used as part of a layered 2FA approach rather than as the sole line of defense.
It started during COVID, when movement was restricted, and many of us finally had time to think deeply about the structural problems facing African businesses.
There were four of us at the beginning. We met roughly once a month, not to rush into building a startup, but to talk through patterns we were seeing in the market. One issue kept resurfacing. Most businesses in Nigeria were operating with very little data and almost no modern tools to guide day-to-day decision making.
At the time, many startups were focused almost entirely on payment collection. That mattered, but it felt incomplete. Collecting money is only one part of running a business. Inventory, expenses, reporting, and performance visibility were still largely unmanaged. Businesses were working hard, but without clarity.
Speaking directly with business owners shaped our conviction. Again and again, we saw that once payments were collected, owners were largely left on their own. Many relied on foreign software that was not built for Nigeria. Others stitched together fragmented local tools that never worked as a single system.
The result was predictable. Decisions were made based on instinct rather than insight. Owners put in effort without knowing what was truly working. That disconnect between effort and clarity pushed us into this space.
We did not want to build another narrow solution. We wanted to help businesses actually run better.
Starting With People and Patience
We started with a technically strong founding team, most of us with engineering backgrounds. Several of us had worked in structured retail and manufacturing environments like KraftHeinz and Pepsi, as well as tech startups serving small and medium businesses. Those experiences shaped how we thought about systems, efficiency, and scale.
On the capital side, we raised a small friends and family round. It allowed us to build an early product, test with real merchants, and iterate quickly without external pressure. Family and close friends were critical, not just financially, but emotionally. Their belief gave us the patience to build deliberately and stay focused on real customer problems.
Trust, Pricing, and Market Reality
Early challenges were unavoidable. The first was trust. Convincing business owners to move critical operations onto a new platform takes time. The second was hiring, especially in an environment where experienced talent often had safer or better-paying options.
Pricing was another constraint. Nigerian businesses are highly price sensitive, and we could not charge what similar tools cost in Europe or North America, even though development costs were comparable. Inflation and currency instability made long-term planning difficult.
Interestingly, recent tax reforms shifted behaviour. More businesses began to understand the importance of proper record keeping and structured systems. That shift aligned closely with what we were building.
Technology as the Foundation
Lumi was technology-first from day one. We were not a traditional business that later adopted tech. Technology was the lever for scale, consistency, and insight.
We built an all-in-one platform combining sales, inventory management, payments, expenses, customer management, analytics, and integrations with financial partners. Internally, strong data infrastructure and scalable cloud systems allowed us to support hundreds of merchants across multiple locations.
Once customers got past the learning curve, many became deeply reliant on the visibility and control the platform provided. Over time, Lumi shifted from being a nice-to-have to being operationally essential.
Growth, Impact, and What Endures
At our peak, we were processing over 400,000 sales transactions monthly and more than ₦11 billion in transaction value across hundreds of merchants. Beyond scale, we are proud that we built a profitable, sustainable business solving real operational problems.
As customers matured, their questions changed. They began asking about margins, trends, and optimisation. Access to data was reshaping how they thought about their businesses.
Through it all, certain principles remained non-negotiable. Treat the team fairly. Reward hard work. Champion the customer. Build patiently.
Looking ahead, the focus is on scaling through aligned partnerships and ensuring technology remains digestible for business owners used to manual systems. As AI reshapes competitive advantage, structured data will be the entry point. That is where Lumi’s foundation matters most.
Most fintech apps in Nigeria follow the same script.
Build a shiny interface, pack in features, then fight to convince people to download one more app they’ll barely open. Xara walked away from that playbook.
Sulaiman Adewale, Xara’s founder, built a financial tool that runs inside WhatsApp. No extra app. No onboarding stress.
You open the platform you already use every day and handle your transactions from there. With 95% of most Nigerian social media users already active on WhatsApp, this offers an explicit opportunity to leverage.
What stands out is how smoothly Xara fits into everyday conversation. You can send a voice note in pidgin, drop a photo of an account number, or type: “Send ₦10,000 to Seun for breakfast.” And the system seamlessly understands.
The AI was trained on Nigerian expressions, so it responds to the way people actually speak. And that’s really important.
Many banking apps still overwhelm users with interfaces that feel stiff or confusing. Xara removes that friction completely.
It also works on simple phones with unstable internet connections. You don’t need a fancy device or steady data. You don’t need to switch between apps or search for account numbers. Everything happens on directly on WhatsApp.
And the way people easily adopted it shows it works. Within two weeks of launch, Xara pulled in 10,000 users and processed over ₦135 million in transactions. These aren’t just people downloading for the sake of it—It works, and they’re using it.
Xara is worth paying attention to for more than convenience. It’s proof that African tech doesn’t have to copy Western products to succeed. Sometimes the answer is meeting people where they already are, using the tools they already trust. Adewale saw that and built around it.
There’s still work ahead—building trust, scaling, and regulation. But if Xara grows, it signals something bigger. Financial inclusion doesn’t always hinge on building complex products.
Sometimes the breakthrough is as simple as moving banking to the space people open every morning.
In a market that has shut out millions with complexity, Xara’s strength is its simplicity. And that’s why it stands out for me and several other Nigerians.
One of us went to buy a basic food item in Lagos during the Salah period and was quoted a price that felt unusually high. Later that same day, the exact item was found selling for significantly less in another city.
That experience made something clear.
Many Nigerians are not overpaying because they are careless. They are overpaying because they do not have access to reliable price information.
Operating in Nigeria means constantly dealing with price changes. Inflation is not abstract. It shows up in everyday purchases. Prices shift quickly, and most people are left to guess what is fair.
Over time, people develop their own systems. They call friends. They ask traders. They compare notes informally. It works, but only within small circles. Beyond that, there is no structure.
We were not trying to change how people buy. We were trying to give them better visibility before they spend.
Building Around Existing Behaviour
From the beginning, the insight was simple. People are already sharing price information. It just does not scale.
Technology became the way to organize that behaviour. Instead of building something complex, we focused on simplicity. A web-based platform where people can easily contribute prices and find information quickly.
The goal was not to replace the market culture. It was to structure it.
As the platform grew, the model shifted. Instead of chasing data ourselves, the community became the source. The platform became the system, and the users became the engine.
Earning Trust Before Scale
One of the biggest challenges was trust.
Price is sensitive. People are naturally skeptical, especially in a market where information is often unreliable. We had to prove that Bango existed to empower users, not to take advantage of them.
Growth came without paid advertising. In just over a month, we crossed 1,000 users organically. More importantly, users were contributing data on their own. That was the real signal.
Trust was forming.
From Guesswork to Data
As more people began using the platform, behavior started to shift.
Instead of relying purely on instinct, users began checking prices before buying. There was a gradual move from guesswork to more informed decisions.
Technology also removed location barriers. Someone could now access price insights beyond their immediate environment. What used to be limited to a small network could now be shared more widely.
Building a Shared System
What Bango is building is not just a tool. It is a shared system.
A way for people to look out for one another before spending their money. A way to make pricing more transparent, not just for buyers, but for sellers as well.
Looking ahead, the focus is on improving data accuracy, expanding commodity coverage, and building smarter tools that help users act confidently.
The vision is simple.
To create a trusted network at scale, where Nigerians are better informed, more confident, and less exposed to inconsistent pricing.
Because sometimes, the most valuable information is knowing what something should actually cost.
In 2016, the idea that would eventually become PlyVerd did not look like a tech startup.
It looked like frustration.
Nkwachikwere Amadi was running a travel agency while pitching software solutions when he helped a student apply to study abroad using the traditional manual method. The process was fragmented, paperwork-heavy, and ultimately unsuccessful. Schools in Lagos and Abuja rejected partnership attempts. The system felt opaque and inefficient.
But one question stayed with him:
What if this could be rebuilt as technology?
He carried that thought for years. In 2022, he finally acted on it.
With just $100, Google Forms, Airtable, and a basic WordPress site, he launched DemeraldStudys. There were no investors. No safety net. Just the determination to solve a problem he had seen derail lives.
Still, something felt incomplete.
At Lagos Startup Week in 2023, Amadi had a defining realization. He was not supposed to digitize the old immigration model. He needed to rebuild it entirely.
The core insight behind PlyVerd is simple but powerful.
Talent is borderless. Opportunity is not.
Across Africa, highly skilled professionals face invisible walls not because they lack ability, but because the migration process is often chaotic, opaque, and riddled with misinformation. Families liquidate savings. Professionals trust unverified “agents.” Rejections become financially and emotionally devastating.
PlyVerd was built to replace guesswork with structure.
Instead of acting as a traditional travel agency or document processor, the platform positions itself as an operating system for legal migration. It consolidates a user’s entire immigration history, audits eligibility across countries, and tracks application progress in a centralized digital vault.
Migration, in this model, becomes a managed lifecycle rather than a one-time gamble.
Bootstrapping in a Low-Trust Market
Starting in Nigeria’s immigration services market meant confronting a credibility crisis.
The industry has long been associated with scams, broken promises, and exploitation. Convincing users that PlyVerd was infrastructure, not another scheme, was one of the company’s steepest challenges.
The early phase between 2022 and 2023 relied on simple tools. Google Forms, Airtable, and WhatsApp communities were used to validate demand and prove that transparency itself had value. Over 1,800 community members engaged during this period.
But the scale required more than spreadsheets.
Immigration policies change constantly, with hundreds of updates globally each year. Manual tracking could not keep up. In 2024 and early 2025, the team rebuilt everything into a proprietary AI-driven compliance engine that automates eligibility assessments, identifies documentation gaps, and flags risk factors before submission.
In July 2025, PlyVerd officially launched its WebApp.
Data as Dignity
One of the most transformative shifts for users has been psychological.
Before PlyVerd, applicants depended on agents for updates. Documents were scattered. Past applications were difficult to track. The process felt opaque.
With PlyVerd’s Digital Vault, users consolidate visas, credentials, and documentation into one secure system they control. They can audit their profile against major pathways, including the UK, US, Canada, and Australia, simultaneously.
This transparency shifts migration from desperation to strategy.
To date, the platform has completed over 900 eligibility assessments and maintains a 100 percent client satisfaction rate, all achieved without paid advertising. The business has grown sustainably through customer revenue.
As users mature on the platform, their behavior evolves. The question shifts from “How fast can I leave?” to “How do I optimize my profile across multiple countries?”
That change signals deeper product-market alignment.
Defining Migration as a Service
PlyVerd is positioning itself within a new category: Migration as a Service.
Instead of selling visa outcomes, it productizes the process itself. The value lies in clarity, compliance, and lifetime profile management.
Amadi believes the future of migration is digital-first and AI-driven. Physical paperwork is fading. E-visas are becoming standard. Borderless identity management will define the next era.
PlyVerd’s long-term vision is ambitious: audit and secure 600,000 African profiles within three years and become the trusted operating system for African mobility.
The company is also exploring interoperability with embassy systems for one-click verification and building toward a future where digital mobility profiles function much like financial credit scores.
Building With Discipline
Bootstrapping from $100 shaped PlyVerd’s philosophy.
Constraints forced focus. Every feature had to solve a real problem. Customer revenue validated demand more reliably than investor capital.
If funding comes, the priority remains infrastructure and access. Strengthen the digital identity backbone. Expand language support. Improve mobile-first accessibility. Move faster without sacrificing sustainability.
The Mission
At its core, PlyVerd is about agency.
It envisions a future where an African professional can open a dashboard and know with certainty where their skills are competitive globally. Where migration decisions are informed by data, not desperation.
Growing up, I battled confidence issues because of my dentition.
Approaching women in person felt intimidating, sometimes impossible.
But I noticed something about myself early on: when I could express affection, intention, and personality through words first, I felt more understood. I could show who I was before being judged by how I looked.
That experience stayed with me. And over time, it raised a bigger question.
What if connection didn’t have to start with physical appearance?
That question eventually became Woomeout.
Building Connection Before Attraction
Woomeout is a matchmaking platform designed around one core belief: emotional connection should come before physical connection. It’s for people who want intention, clarity, and meaning, not endless swiping or surface-level interactions.
The idea didn’t begin as an app. It began on Instagram.
By observing how people from different backgrounds interacted online, we launched a simple matchmaking page and started connecting people intentionally.
We listened. We introduced. We watched conversations turn into real interest. What began as casual matchmaking slowly revealed a deeper need for structure, safety, and purpose in modern dating.
That was when Woomeout evolved from an idea into a product.
Bootstrapping a Dating Platform With Purpose
Woomeout was fully self-funded. The Instagram matchmaking service was paid, and the revenue, combined with personal savings, funded the first version of the app. There were no investors at the start, just belief and iteration.
Because Woomeout doesn’t follow the traditional dating app structure, early users faced a learning curve.
Some struggled to navigate features like sending date requests, video calls, or participating in challenges.
Many expected endless chatting, while Woomeout encourages intentional actions such as requesting or scheduling dates.
What we learned quickly is that building a dating platform isn’t just about technology. It’s about changing behaviour.
Dating culture has trained people to swipe endlessly and delay intention. Woomeout challenges that and that takes education, reassurance, and trust.
From Online Matches to Offline Moments
One of our proudest milestones is turning a deeply personal idea into a growing, community-driven platform.
Seeing people move from online conversations to curated offline events and real relationships has been especially magical.
Those events matter. They remind us that technology should enable connection, not replace it.
Technology allowed us to remove fear and distance. People who might never approach someone in real life now have a safe space to express interest, curiosity, and intention. Across cities and backgrounds, Woomeout creates access to connection beyond physical boundaries.
What Comes Next for Woomeout
Our focus isn’t rapid expansion.
We’re prioritising better matchmaking quality, expanding curated offline events across more cities, and building features that reward intentional dating over endless interaction.
Growth will be community-led. We’ll only enter new markets where organic demand already exists.
If funding or partnerships come in, the priorities are clear: product quality, community trust, and safety.
That means improving user experience, strengthening moderation, and partnering with venues, event hosts, and community collaborators who support real-world connection.
More Than a Dating App
What excites me most about Woomeout’s future is that it’s becoming more than a dating platform. It’s a space where people feel seen, intentional, and confident about how they meet others.
In a world optimised for speed and surface-level attraction, choosing to slow down and lead with emotional connection feels radical.
And sometimes, the most meaningful tech doesn’t help people swipe faster. It helps them feel understood first.
