The Name Change Was a Signal

When the co-founders started building, they were solving for Africa.

Post-COVID 19, remote work had created acute demand on the continent for customer support tools that were either too expensive or too complex for African SMEs to access or operate.

Zendesk charges at price points that would destroy the margins of a mid-sized Nairobi fintech.

Dial Africa was the answer to that problem.

Then customers started to arrive from other climes like the United Kingdom, Canada, just to name a few.

The problem turned out to be the same problem but just in a different geography. Overpriced, overcomplicated tools designed for large Western enterprises trying to serve smaller businesses everywhere else.

The founders did not abandon Africa. They expanded the category of who needed what Africa needed first. Then the name Resolv followed.

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The Core Problem

Behind every customer success, there is a human.

The founders return to this observation consistently and it frames the entire product philosophy.

The customer support AI market is currently engaged in a heated debate about automation versus human relationship.

Most of the debate is wrong because it presents a binary that the actual problem does not require.

A customer who places an order does not need a human to confirm it. The task is mechanical, rule-bound, and repetitive. Automation handles it faster, more accurately, and at a fraction of the cost.

The same customer who has a complaint about the order, who needs reassurance, who is on the verge of churning, who represents six months of acquisition cost about to walk out the door, that customer needs a human. Not eventually. Immediately, and with full context.

The problem is that existing tools serve one side of this well and the other poorly or ask businesses to subscribe to multiple platforms and build the integration themselves, or price both layers at enterprise rates that make the economics work only for large businesses.

Resolv’s thesis is that the workspace should contain both layers, that the AI handles what AI handles well, the human handles what humans handle better, and the transition between them should be invisible to the customer and frictionless for the agent.

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The Strategic Decision Layer

When the co-founders decided to commit fully to Resolv, they did not have a revenue base to sustain product development.

They had a software idea and no paying software customers.

Rather than raising external capital immediately or burning personal savings, they set up a business process outsourcing (BPO) operation on the side.

The BPO generated consistent cash flow. That cash flow funded the software development. And the BPO clients became the first test environment for the software itself.

Three things happened simultaneously. Revenue came in. The product was tested in real conditions by real users with real customer support problems. And the feedback from those users directly shaped what the software became.

By the time Resolv was ready for its seed round, the product had not been built in a vacuum and then tested.

It had been built inside an operating business and improved through that process for years.

The founders eventually wound down the BPO as the software business became self-sustaining.

The BPO had served its purpose and adding complexity to the operating structure was no longer worth it.

Resolv integrates with multiple foundation AI models rather than building its own from scratch or committing to a single provider.

The middleware layer selects which model to use based on accuracy and cost for each specific query.

The cost of the selected model is passed to the client at a small markup rather than absorbed into the platform’s margin.

That architecture produces three simultaneous advantages namely:

1. The platform remains model-agnostic, which reduces dependency risk as the AI model landscape continues to shift.
   
2. The client pays based on consumption rather than a flat fee, which aligns cost to value in a way that is easier for SME clients to justify.
   
3. And the cost structure allows the platform to maintain low subscription prices, because the expensive AI inference cost is not embedded in the platform margin but billed separately as usage.

The co-founders deliberately built support for African languages and local linguistic contexts into the platform before expanding.

This includes but not limited to Nigerian Pidgin, Swahili variations, Francophone contexts, and local accents and terminology patterns that generic AI models trained on Western datasets handle poorly.

The observation from the founders that some tools were all foreign and were not making sense in the African context, is the founding insight that drives this differentiation.

Hence, Resolv’s AI infrastructure is built to be locally accurate before it is globally competitive.

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Ecosystem Context

The founders had initially turned down UK-based customer support company. Not because the offer was bad.

Because the purpose was not complete and selling at that stage would have been exchanging a large outcome for a modest one.

That decision, made from a position of genuine need for capital and genuine difficulty building in Africa without enough resources, reflects a specific kind of conviction about long-term value that is rare enough to deserve recognition.

The technical talent retention problem the founders name is one of the most practically specific ecosystem observations.

Google trained Resolv’s engineering team through the Google AI First Accelerator. The team was fully equipped to build the AI platform.

Resolv navigated this through transition into AI even though it took longer and cost more than it should have.

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Observed Patterns

The BPO-to-SaaS trajectory is one of the more disciplined founding stories in the series.

Most founders either bootstrap from personal savings until the money runs out or raise external capital as early as possible.

The Resolv approach, building a service business to fund a product business while using the service business as the product’s test environment, is operationally sophisticated and requires the kind of dual-track execution that most founding teams cannot sustain.

The fact that the BPO was eventually wound down rather than kept running as a parallel revenue stream also suggests founders who were clear about what they were building and disciplined enough not to let the bootstrap mechanism become the business.

Resolv does have a huge investor base. Expert Dojo from San Francisco was an early backer. Made’s Capital from Nigeria. Frontend Ventures from Kenya. Victoria Business Angel Network. Afrinext Ventures operating across Kenya and the US. And Google, through the AI First Accelerator cohort out of Africa.

Google’s participation in particular is a specific and publicly referenceable validation signal.

The AI First Accelerator is a competitive programme. Being selected from an African cohort puts Resolv in a defined category of companies that Google’s own evaluation process considered among the most promising AI applications from the continent.

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Open Variables

Between $2.5 and $3 million is being sought at 20% equity, targeting opening within weeks of this interview.

The stated uses include fuelling the AI platform transition that the Google-trained team was building and accelerating geographic expansion including the South Africa entity.

Whether the round closes at those terms, on the expected timeline, in a fundraising environment that remains challenging for African SaaS companies, will determine the pace of the next phase considerably.

The enterprise segment transition is also worth noting. The majority of the current portfolio falls within SMEs and mid-market.

The founders describe a slow transition toward enterprise as well. Enterprise sales cycles are significantly longer, require different commercial infrastructure, and carry different contract risk profiles than SME subscriptions.

Whether the current commercial structure will follow through with the execution of enterprise acquisition and platform development simultaneously is an operational bandwidth variable worth observing.

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Why This Matters

For founders building SaaS infrastructure in African markets, the Resolv case makes the most direct argument in this series for the strategic value of building a service business alongside a software business at early stage.

The BPO was not a distraction from the product. It was the product’s most important development environment.

Founders who treat services as beneath the ambition of a software company may miss the market intelligence, the revenue, and the testing environment that a well-run service operation provides.

For investors, Resolv represents the category of African SaaS company that most closely mirrors the growth trajectory of comparable companies in the US and Europe, but with the additional evidence that the problem being solved is not just Africa-specific.

Customers in the UK and Canada are adopting the platform because the pain of overpriced, overcomplicated customer support tooling is not an African problem.

It is an SME problem everywhere. The addressable market for a company solving that problem at African price points and with African-market localisation is significantly larger than the continent alone.

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Final Strategic Takeaway

The specific weight of building a technology company from Africa without the resource base that equivalent companies in the US or Europe can access as a baseline can be very challenging.

This is an accurate description of the structural conditions that every founder building in Africa at this level of technical ambition must navigate.

Resolv is building toward acquisition or IPO on its own terms, at a valuation that reflects what the business can become rather than what it was worth when the first offer arrived.

The seed extension is the next step toward that destination.

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This article is drawn from an in-depth founder interview conducted by Afriq IQ with Japheth Dibo CEO and Ben Gavana COO of Resolv. Selected insights and observations are published here.