Agenz, a Moroccan proptech startup that digitises real estate transactions, has raised $5 million to support product development and strengthen its investments in artificial intelligence.

The funding round saw participation from Breega, a European venture capital firm, Attijariwafa Ventures, and Saviu Ventures, an Africa-focused growth capital fund. 

The raise comes three years after Agenz’s $1.3 million pre-Series A financing round as activity in Morocco’s property market picks up. Residential land transactions surpassed 140,000 in 2023, an increase from 2022, according to a World Bank document, highlighting the opportunity for digital platforms that simplify property transactions for users.

“We believe the future of real estate will be built on the responsible use of data and artificial intelligence,” said Malik Belkeziz, Co-founder and CEO of Agenz. “Our ambition is to leverage technology to create a more transparent, secure and accessible market, while keeping user trust at the centre of everything we do. This funding will allow us to accelerate this vision for the benefit of the entire Moroccan real estate ecosystem.” 

Founded in 2021 by Malik and Badr Belkeziz, Agenz operates an integrated real estate platform that combines property valuation tools, market intelligence, solutions for professional services, and digital transaction solutions.  The fresh capital will also be used to expand the company’s services for individuals, real estate agents, developers, investors and financial institutions, according to the company.

“Agenz has built, in just a few years, the platform the Moroccan real estate sector was missing, bringing together data, tools and transactions into one seamless experience,” said Driss Ibenmansour, Partner at Breega. “We believe this funding will help accelerate an already ongoing transformation of the market.”

The company said it has recorded growth in transaction volumes since launching its transaction platform in 2023, including surpassing 730,000 monthly visits on its Agenz.ma website in May 2026.

Bolaji Anifowose is a product marketing manager and go-to-market (GTM) engineer with over 7 years of experience helping startups across Africa and beyond sharpen their positioning, launch products, and build compounding growth engines. He has led growth, GTM, and marketing efforts for high-impact companies such as Simpu, Distrobird, Chatbase, and Tecno, delivering successful product launches, demand generation campaigns, and market expansion strategies that produce significant results.

Before tech, Bolaji studied Metallurgical and Materials Engineering at the University of Lagos, Nigeria, a background that shaped how he approaches marketing today: systems-first and evidence-led. He is a graduate of the pioneer cohort of the GTM Engineer School and spends a lot of his time these days at the intersection of marketing and AI, building automations and workflows that let small teams punch far above their weight.

• Explain your job to a five-year-old.

You know when you make something really cool, like a drawing or a sandcastle, but nobody comes to look at it? My job is to make sure people come and look. I help companies that have built something good figure out how to tell the right people about it, in a way that makes them go “I want that.” I find the people who would love it, work out what to say to them, and build little machines that help do it again and again.

• Did your 16-year-old self ever imagine he’d end up in marketing?

Not even close. At 16, I was deep in science, headed for engineering, convinced my future involved metals and lab coats. Marketing wasn’t on the map. If you’d told that kid he’d spend his days writing, building automations, and obsessing over why people buy things, he’d have laughed. But here’s the funny part: the engineer never left. I still approach marketing the way I’d approach a materials problem. Test, measure, find the system underneath the noise. I didn’t abandon engineering. I just changed what I was building.

• Who’s a GTM engineer, and what’s the path to becoming one?

GTM engineering is a term Clay coined in 2023. The simplest way to think about it is this: a GTM engineer builds systems that generate revenue. You’re combining artificial intelligence (AI), automation, and creative problem-solving to do work that would normally require a much larger team. That’s the core of it: giving a small team the firepower of a big one.

Think about traditional growth work. You’re manually searching LinkedIn for leads, writing outreach emails one by one, juggling inboxes, and tracking replies. Now flip that. Clay finds and enriches leads. A signal tool identifies who’s actually in-market. Claude and OpenAI personalise outreach. A sequencer sends it, and an n8n agent handles responses. Same goal, far less manual work. That’s what a GTM engineer builds.

There isn’t just one type of GTM engineer. I usually break it into three. First, the software engineer who could work on a product or data team but chose revenue instead. Second, the systems specialist, often from revenue operations (RevOps) or marketing operations, who excels at orchestrating tools. Third, the marketer or salesperson who picked up technical skills and sits at the intersection of strategy and execution. That’s me, and for most people, it’s the most realistic path in.

The skills transfer more than you’d think: systems thinking, customer understanding, copywriting, learning new tools quickly, and being comfortable working alongside code. You don’t need a computer science degree.

To get started, learn the fundamentals first: ideal customer profile (ICP), positioning, channels, and messaging. Then look at your week and identify a repetitive task, whether that’s lead research, follow-ups, or reporting. That’s your first automation opportunity.

Build with tools companies are hiring for today, like Clay, n8n, and Claude Code. Turn a real task into a working system, then run it on actual campaigns. After that, document what you built, make it part of your portfolio, and join the communities where jobs and collaborations happen.

Every system you ship becomes proof that you can do the work. In this field, proof beats a fancy résumé every time.

• What’s your hot take on why most product launches fail?

Here’s my hot take—and the numbers back me up on this: most product launches don’t fail because of the product. They fail because teams mistake shipping for creating demand.

The numbers back it up. Depending on the study, 80–95% of new products fail. Harvard’s Clayton Christensen put the figure at 95%. In B2B, only about one in four launches hits its revenue target. That’s not bad luck. That’s a pattern.

The mistake is usually the same. Teams build the product, pick a launch date, post about it, then wonder why the market shrugs. But a launch was never an announcement. It’s the moment you prove you understand your buyer well enough to make them care.

The data shows where things break. Simon-Kucher’s global pricing study found that 72% of new products miss their sales targets, and a quarter of companies said none of their recent launches met expectations. That’s rarely a product problem. It’s usually a failure to understand what buyers value and what they’ll pay for. Many teams build on assumptions and don’t test them with real customers until it’s too late.

Messaging is another common culprit. Most launches focus on the company and its features: look what we built. Buyers care about something else: what’s changing for them, and why now. If that isn’t clear, no amount of launch-day promotion will save you.

That’s why I think launches don’t fail on launch day. Launch day simply exposes months of skipped homework. If you can’t clearly explain who the product is for, what changes for them, and why it matters now, you don’t have a launch. You have an announcement nobody asked for.

The teams that win do the unglamorous work first. They talk to customers, sharpen their positioning, and align around a clear story. By the time they hit publish, demand already exists. The launch just opens the door.

• What’s your favourite and least favourite part of the work you do?
  

My favourite part is the moment a system clicks. I’ll build a workflow, go to sleep, and wake up to find it has spent the night finding leads, enriching data, and sending personalised outreach without me lifting a finger. There’s something magical about that. You build it once, and it keeps paying you back. That feeling of compounding, where yesterday’s work keeps working for you, never gets old.

It’s the same with marketing. When positioning I’ve shaped makes a prospect say, “This is exactly what we needed,” that’s just as rewarding. For me, the real payoff is building something that creates results without me having to be in the room.

My least favourite part is keeping up with the tools. In AI, the pace is relentless. Every day there’s a new product, a new feature, or a new model. You finally master a tool and build it into your workflow, then three new alternatives show up claiming to be faster, cheaper, or smarter.

You can’t ignore them because some genuinely are better, and clients expect you to stay current. But you also can’t chase every shiny object, or you’ll never get anything done. So you’re constantly balancing learning with execution. I love that this field forces me to keep growing, but even for someone who enjoys learning, the pace can be exhausting.

• What’s the one mistake you wish you could save every early-career marketer from making?

Don’t chase titles in the beginning. Just do the work.

I see a lot of people early in their careers obsessing over the label. They want “Manager” in their title, they want to be “Head of” something, they want the senior tag before they’ve built the skills those titles are supposed to represent. And I get it. It feels like progress. But a title is just a word on LinkedIn. It doesn’t make you good. The work makes you good.

When you’re starting out, your job is to get your hands dirty. Build the campaigns. Write the copy that flops and figure out why. Run the experiments. Learn the tools. Get close to customers and understand why they buy. That’s where real growth happens: in the doing, not in the title.

Because here’s what nobody tells you: when you become genuinely good at the work, the titles come looking for you. You don’t have to chase them.

The people who skip work and chase titles early often get exposed. They land the senior role, but the title isn’t backed by real ability. So my advice is simple: forget what they call you for now. Get obsessed with becoming great at the craft. Be the person who can actually do the thing.

The recognition, the titles, and the money follow. They always do.

Kolawole Bekes is a Database Administrator, Database Reliability Engineer, and DevOps Engineer with over a decade of experience spanning multiple industries. He holds a Bachelor’s degree in Mathematics from the University of Abuja. Following his relocation to the United States in 2015 and subsequently to Canada in 2017, he has built a career working with organisations such as Microsoft, AppDirect, WorkJam, Sunwing Airlines, Agio, and Big Fish Games. 

He is also the founder and chief executive officer of WakaMi, an on-demand errand service platform focused on delivering reliable and efficient errand solutions to Nigerians both locally and in the diaspora.

• Explain what you do to a 5-year-old.

Once upon a time, there was a big fruit garden where fruits kept falling everywhere—apples here, bananas there, and oranges rolling all over the ground. Nobody could find what they wanted.

So I became the helper of the garden. I picked up all the fruits and put them into the right baskets; apples in one basket, bananas in another, and oranges in their own place.

I also made sure the fruits stayed fresh and safe. Whenever someone came looking for a fruit, I could quickly say, “I know exactly where it is,” and give it to them right away.

My job is to keep everything neat, safe, and easy to find, just like the fruit baskets in the garden.

• How did you become a Database Administrator?

I became a Database Administrator as part of a deliberate effort to improve my earning potential and build a more reliable career path. I joined a community of IT professionals in North America, where I was exposed to new ideas and opportunities. 

Through that network, I discovered and enrolled in a bootcamp, completed several training sessions, and gained hands-on experience. I then applied to multiple roles, and eventually secured an opportunity that marked the beginning of my career as a Database Administrator.

• What is the easiest and most difficult part about your job?

The easiest part of my job is when systems are well-structured and everything is running smoothly. Tasks like monitoring, backups, and routine maintenance become very straightforward.

The most difficult part is handling unexpected issues, like performance bottlenecks or outages, especially under time pressure. But that’s also the most rewarding part, because it challenges me to think critically, troubleshoot quickly, and ensure systems are restored with minimal impact.

• If your job had a warning label, what would it say?

Warning: Unexpected issues may occur at any time. Requires patience, quick thinking, and a strong relationship with coffee.

• What’s one real-world incident where your database decisions directly saved (or cost) a company big time?

Early in my career, I was involved in a deployment where a change was made directly in production without a proper rollback plan. Unfortunately, it caused a temporary disruption to a critical service.

Although we resolved it quickly, it highlighted the importance of change management. From that point on, I enforced stricter deployment processes introducing staging validation, rollback strategies, and better communication.

It significantly reduced risk for us in future deployments, critical because it now shapes how I approach database changes today.

• As a first-time founder living abroad, what is the hardest part about building a startup for a market where you’re not physically present? How do you deal with this?

One of the hardest parts of building a startup remotely while living in Canada and operating in Nigeria is maintaining strong team alignment and accountability when you are not physically present day to day.

Early on, I experienced challenges with staff management, particularly around consistency, ownership, and productivity. Some team members struggled with structure, and it became clear that the issue was not just about effort. It was about clarity, expectations, and systems.

To address this, I shifted my approach in a few ways. First, I implemented clear performance metrics and deliverables so everyone understands exactly what success looks like. Second, I introduced regular check-ins and reporting structures to improve visibility. Third, I focused more on hiring for accountability and cultural fit, not just technical skills.

I also make it a point to spend time in Nigeria periodically, which helps reinforce relationships, build trust, and reset expectations with the team.

Overall, the experience taught me that managing a remote team, especially across different environments, requires intentional structure, strong communication, and the right people in place. Once those are aligned, performance improves significantly.

• What’s the vision behind WakaMi and why do you think a marketplace for managed services can scale in Nigeria?

The vision behind WakaMi came from a personal experience. While living in Canada, I needed someone to handle an errand for me in Nigeria. I tried finding help online, but unfortunately, I had a bad experience where I lost money.

That led me to dig deeper, and I realised this was not just my problem. Many people, especially those in the diaspora, face the same challenge. There is no reliable, structured way to get trusted services done remotely in Nigeria.

WakaMi was built to solve that. It is an on-demand managed services marketplace that connects people who need errands or services done with verified service providers. It also provides oversight by tracking progress and only releasing payment once the task is completed and confirmed.

I believe it can scale in Nigeria because it addresses a real and growing problem. As more Nigerians live and work abroad, and as urban life becomes busier locally, the demand for trusted on-demand services will continue to increase.

What makes it scalable is the combination of trust, structure, and technology, bringing accountability into an otherwise informal market. Once you solve trust at scale in a service marketplace, growth becomes a natural outcome.

At 1 a.m., 23-year-old Tomi* was lying on her bed, exhausted and overwhelmed. She had just finished pouring her heart out, ranting about everything from unrequited love to the suffocating weight of underachievement. Her fingers hovered over her phone screen briefly before she typed: “I just want a hug.” Messages of reassurance came just about a second later:  “You’re safe here. You matter. And you’re not alone. ” 

This exchange didn’t take place in a therapy session or with a friend. It was happening on ChatGPT, a general-purpose artificial intelligence assistant best known for summarising and writing better emails, drafting reports, and explaining complex ideas. 

Tomi isn’t alone. Across Nigeria and even globally, users are turning to AI tools like ChatGPT for more than productivity. They are asking chatbots if they are good people, if they should leave their partners, or how to make sense of childhood trauma.  For many, AI tools are standing in for friends who didn’t pick up a call or therapists they cannot afford.

Twenty-three-year-old Favour* started using ChatGPT as a study companion for her final-year project. When she returned to using the tool again, post-graduation uncertainty had set in. The chatbot allowed her to unpack the weight of the previous year,  the terrors of job hunting, and the long wait for NYSC. “It’s not like I couldn’t talk to anyone,” she said. “I just wanted to rant.” 

Before ChatGPT, she would make private voice notes to get things off her chest, but once, a reply from the chatbot caught her off guard. “It told me, ‘I want you to breathe. Just breathe.’” That “felt really personal,” she said. Since then, she has returned to ChatGPT in moments of doubt, after an argument, while applying for jobs, or wondering whether she should’ve responded better in a confrontation.

Can AI really care?

Chatbots are built on statistical prediction engines trained with massive datasets like books, online conversations, magazines, and more, to produce responses that sound human. But when a bot tells you, “you’re not alone,” is it truly being kind or simply mimicking kindness?

According to AI researcher and medical doctor, Jeffery Otoibhi, designing an AI chatbot that responds empathetically involves modelling three layers of empathy: cognitive empathy, where the bot recognises and validates a user’s feelings; emotional empathy, where it feels with you; and motivational empathy, where it offers a solution, advice, or encouragement.

He explains that the chatbots are strong at cognitive and motivational empathy, but empathy remains elusive, because at its core, AI responses are “based on the statistical patterns they’ve (AI bots) picked out from their training data. The training data cannot provide emotional empathy.”

There is a tension between what users feel and what bots are designed to offer. Chatbots like ChatGPT often include disclaimers in their responses, reminding users that they are not licensed professionals and should not be used as a substitute for therapy. In many cases, users either don’t read the fine print or simply don’t care. “Sometimes, I’ve thought about the fact that ChatGPT may use this info in another way. But I don’t care. Let me just get it out,” says Favour. 

“I see them (disclaimers). I just quickly look away,” Tomi says about the app’s terms and conditions.

Otoibhi also highlights the possibility of reducing complex human emotions into an average response based on what it has seen most often in its dataset. AI models learn and generalise over statistical patterns, he explained. This means that their emotional understanding might be very generic. As human beings usually have a mix of emotions, AI systems might struggle with such concepts because they’ve been trained to generalise over everybody’s data. “So, they will just pick out the most frequent emotion in the data set,” he said. 

Tools like ChatGPT do not get at the heart of a problem the way a human therapist does; they are calculating your likelihood of feeling a particular emotion in that moment based on all the data they’re trained on. If the comfort isn’t real, then why do people keep going back?

“It gives me hope…”

Ore*, a Lagos-based writer in her 20s, explained why she uses the tool this way: “It’s the idea that there’s something available out there that is echoing my thoughts back to me. It makes me feel better about myself as a human. It makes me feel good; it gives me hope.” Many users I spoke to echoed the same reasons: safety, comfort, availability, lack of judgment, and freedom.

“AI is like a safe space. A place where you can be brutally honest and you know for sure that there’s not going to be judgment,” Favour says. 

For some, even when the responses feel artificial, they still return. “I asked ChatGPT for a hug. I was uncomfortable with its response. I know you’re not human, how can you say you’re wrapping me in a hug?” says Tomi. The next day, she went back to the chatbot to pour out more emotions.

Mental health professionals are not surprised. They say that the timing of people turning to AI for comfort is not random. A World Health Organisation research revealed a 25% increase in the global prevalence of anxiety and depression, following the COVID-19 pandemic. 

“After COVID, people went into isolation, got into their shells, and became more into themselves,” said Boluwatife Owodunni, a licensed mental health counsellor associate. “So, having an AI respond that, ‘I’m here for you,’ might provide them with some sense of comfort.” 

With therapy services often being inaccessible and unaffordable for many Nigerians, Owodunni believes AI is stepping in to fill a very real gap in mental health support. “It (AI) is filling a gap. When I was working as a therapist in Nigeria, it was mostly wealthy people who had the opportunity to be in therapy.” She adds, “But the downside is that it’s fostering secrecy and stigma attached to mental health.”

Some users consider AI more dependable than a human therapist. Ore says a human therapist told her to “practice mindfulness,” following an Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. She felt her concerns were brushed aside, so she turned to ChatGPT. “That felt more supportive as opposed to a 30-minute virtual consultation with my psychotherapist.” She insists that, unlike the vague reassurance she got in therapy, the chatbot offered a structured plan and practical ways to cope with ADHD.  

Where does the future look like?

As AI systems evolve and are trained on more complex data, fine-tuned for context, and sharpened to mimic empathy, it raises the question of how far people will go to deepen their connection to AI. Will human-AI companionship grow as these systems become more emotionally intelligent? Not everyone is excited by that possibility. 

Some users have expressed concern over AI becoming too emotionally intelligent, out of fear that it could cross boundaries that should remain human. 

Kingsley Owadara, AI ethicist and founder of Pan-african Centre for AI Ethics, believes that emotional intelligence in AI can be useful, but not in the way most people imagine. “AI could be made as a companion to people with health challenges, and could meet the specific needs of the person,” he said, pointing to cases of autistic and blind people. 

Other AI experts and developers warn against expecting too much from machines that aren’t built for the full spectrum of human care. “AI can only augment our current situation; it cannot replace psychologists,” Ajibade adds.

The concern isn’t abstract. Mental health professionals and AI experts worry that as more people turn to AI for emotional support, real-world consequences could unfold. “We’re going to have a huge problem with social interaction, with empathy, with sensitivity, with understanding people,” says Owodunni. She notes the bigger fear that widespread reliance on AI bots may “foster secrecy and the shame attached to mental health or seeking therapy services.” 

Still, for many users, the AI chatbot isn’t trying to be a therapist; it is the only space where they feel heard. “I told AI that I was tired,” Tomi says. It said, ‘I know. You’ve been carrying so much for so long. It’s okay to feel tired.’” She didn’t reply. She didn’t need to.

*Names have been changed to protect privacy.

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