Enterprise AI company Ageiro today announced $3 million in funding to help organisations cut software development time from months to days by turning business intent directly into production-ready applications.

Ageiro is building “humanity’s last app,” an agentic AI platform that converts high‑level goals into autonomous digital execution, enabling enterprises to launch new capabilities faster, remove traditional software development bottlenecks, free engineering teams to focus on higher-value work and scale AI‑driven digital workforces across the business that significantly lift productivity and outcomes.

Enterprise-grade product innovation with humans at the helm

Enterprises face escalating complexity. Traditional, human‑driven software development cannot keep pace with evolving needs, fragmented tools and rising expectations for speed and intelligence. These pressures are compounded by a global shortage of tech talent. The World Economic Forum estimates that by 2030, Africa will need an additional 23 million STEM graduates to fill key roles.

Ageiro answers the question: “What if we could create an adaptive autonomous layer between software engineers and the enterprise where agentic AI builds, evolves and maintains applications under human oversight?”

“Ageiro is built for creators, founders, software engineers and enterprises that can no longer afford the cost, friction and slow pace of the conventional software development lifecycle,” said Paulo Matos, Chief Executive Officer of Ageiro. “Humanity’s last app represents a fundamental shift in how we view software development. It is one platform that can build and evolve the applications an enterprise needs in days rather than months, while humans retain governance, transparency and control. This is enterprise autonomy with enterprise discipline.

Designed for enterprise-grade product innovation

Designed for governance, auditability and scale, Ageiro enables organizations to direct autonomous digital workforces that operate with reliability and accountability. With humans in the loop, decisions adapt in real time under governed controls, and people set strategy, constraints and oversight. With humanity’s last app, Ageiro helps enterprises accelerate their software development lifecycle using AI‑powered automation. This shift shortens time‑to‑market, improves efficiency and allows highly skilled software teams to focus on governance, security and intelligent design. By leveraging AI‑assisted tools, teams augment their capabilities while maintaining control and transparency over AI‑powered systems, a critical advantage in a fast‑moving and competitive marketplace.

Funding impact

With this investment, Ageiro is strengthening its decision‑intelligence models, compliance and risk frameworks, and connector systems to support autonomy in complex, regulated environments; while also scaling its go‑to‑market presence across sales, partnerships and category-building efforts so more enterprises can adopt autonomous application development and digital workforces as a core part of their software strategy.

Brave Group has emerged as a market leader in the critical pillars of modern communication, technology, and transformation, according to the 2025/2026 Agency Scope South Africa report. The biennial study, conducted by Scopen in partnership with the Independent Agency Search & Selection Company (IAS), ranks Brave Group number one in six distinct categories, including Advanced in Digital, Use of AI, and Black Empowerment.

The Agency Scope study is widely regarded as the benchmark for the communications industry, offering a rigorous, perception-based analysis derived from face-to-face interviews with over 500 professionals, including 311 marketing leaders and decision-makers from South Africa’s top spending companies. The research analysed 474 client-agency relationships across 38 creative agencies between June and September 2025.

“These results validate our strategic bet on technology and transformation,” said Musa Kalenga, CEO of Brave Group. “We’ve deliberately positioned ourselves at the intersection of advanced capability and authentic cultural connection—because we believe the future of marketing demands both algorithmic precision and human insight.”

DUAL DOMINANCE: HIGH-TECH AND HIGH-CULTURE

The report’s findings highlight a rare duality in Brave Group’s market positioning. The agency achieved the #1 ranking in both technological capability and cultural transformation. This combination addresses what the study identifies as the modern marketing paradox: the need for algorithmic precision without sacrificing the human, cultural connection that drives brand affinity.

In the technology sphere, marketers identified Brave Group as the leader in Advanced in Digital and the Use of AI, signalling the agency’s successful pivot toward data-led, future-fit marketing solutions. This positioning proves particularly strategic given that 59.3% of marketers surveyed believe agencies are creating economic or strategic advantages by using AI. In comparison, 77% of agency professionals identified adapting to AI as their primary challenge.

“AI isn’t a novelty for us, it’s embedded in how we solve business problems,” Kalenga explained. “Our clients are facing unprecedented pressure to demonstrate ROI and business growth. AI and data analytics give us the tools to deliver measurable impact, not just creative awards.”

The agency also demonstrated strong performance in Analytics and Measurement, a critical requirement for CMOs facing increasing pressure to demonstrate ROI. According to the research, 34.7% of marketers are most willing to pay premium fees for results, ROI, and efficiency, validating Brave Group’s investment in measurement capabilities.

Simultaneously, the agency retained its leadership in transformation, ranking #1 in Black Empowerment and #1 in Diversity. This underscores Brave Group’s ability to offer deep cultural relevance and authenticity, a quality clients in the study described as “deep cultural relevance” and “local authenticity” without arrogance.

“Transformation and diversity isn’t a compliance exercise for us, it’s our DNA and our competitive advantage,” said Kalenga. “In a market as diverse and complex as South Africa, the ability to authentically connect across cultures, languages, and communities is what separates good marketing from great marketing. Our leadership in Black Empowerment and Diversity reflects our lived reality, not an aspiration. We know that scientific research reveals that diverse companies deliver the best outcomes to clients, the team and the market.”

OPERATIONAL EXCELLENCE AND LEADERSHIP

Beyond capabilities, the study points to superior operational performance. Brave Group was ranked #1 for Management Involvement, validating a service model where senior leadership remains accessible and deeply invested in a client’s business. The agency also secured the #1 spot for Earned Media, distinguishing its ability to generate organic traction and conversation for brands in a saturated media landscape.

These operational strengths contributed to Brave Group climbing four positions in the Overall Perception Ranking to #16, with marked improvements in spontaneous awareness and new business activity, ranking #8 among all creative agencies surveyed.

CLIENT SENTIMENT AND MARKET VALIDATION

Qualitative feedback from the study reinforces the quantitative rankings. Clients interviewed praised the agency for being “quick to embrace AI and data-led marketing,” noting that its work across a broad portfolio creates cross-learning benefits. Another client highlighted the agency’s “can-do attitude” and production quality. At the same time, a third emphasised its collaborative approach: “Open to integrated thinking across disciplines, gladly collaborate with other agencies, and work well with them. Happy to lead or follow on a campaign.”

The research also revealed that Brave Group clients value the agency’s innovation and forward-thinking approach, with one noting: “Innovative, forthcoming with ideas, attentive, responds quickly, and good with comms.”

INDUSTRY CONTEXT AND STRATEGIC IMPLICATIONS

The Agency Scope research indicates that South African marketers are navigating significant industry shifts. Digital advertising spending in South Africa reached 17.7 billion ZAR in 2024, representing a 14.2% increase over two years, according to the IAB South African Digital Ad Spend Report cited in the research. Marketing professionals now work with an average of 14.6 different partners, with digital platforms leading collaboration frequency.

The study identified three priority areas where marketers seek agency expertise: creativity and innovation (mentioned by 63% of respondents), agility and time optimisation (69.6%), and demonstrating ROI and effectiveness (27.4%). Brave Group’s performance across these dimensions positions the agency strategically to address evolving client needs.

Notably, 61.1% of South African marketers currently work with integrated agencies, with 57.6% expressing preference for this model in future partnerships—the highest rate among comparable markets globally. Brave Group’s strong performance in integrated services aligns with this market preference.

“The research confirms what we’re hearing from our clients, that the market is moving toward integrated partners who can combine technology, strategy, and cultural insight,” Kalenga noted. “We’ve been fortunate to build capabilities in these areas, but the real test is whether we can consistently deliver on that promise for every client, every day.”

IMPLICATIONS FOR MARKETERS

For South African CMOs and brand leaders, Brave Group’s performance in the 2025/26 Agency Scope suggests a partner capability that solves the modern marketing paradox: the need for advanced, algorithmic precision (AI/Digital) without sacrificing the human, cultural connection (Diversity/Empowerment) that drives brand love.

As marketers face mounting pressure to demonstrate business impact, with 65.6% preferring output-based remuneration systems tied to measurable results, Brave Group’s leadership in analytics, measurement, and AI deployment offers a data-driven approach to accountability and performance.

Cloud has become the backbone of modern business, but its true value only fully emerges when paired with data and artificial intelligence (AI). As Jaap Scholten, Head: Group Hybrid ICT Strategy at Datacentrix and COO at eNetworks, a wholly owned Datacentrix company and ISP, explains, the cloud is often the unsung hero – quietly enabling data, the raw material from which intelligence is forged.

From cloud adoption to intelligent transformation

Too often, Scholten explains, cloud adoption is seen as a tick-box exercise, focused on moving applications from on-premises to the cloud. “But the real opportunity here lies in transformation and at the heart of this is data. If cloud is the behind-the-scenes champion, then data is the golden thread weaving intelligence throughout the enterprise.

“At the end of the day, AI is only as effective as the data it consumes, and the cloud is essentially the ecosystem where this data converges. By unifying structured and unstructured data from across the enterprise, organisations unlock richer insights.”

Cloud powers AI at scale

For AI to deliver enterprise-grade value, it needs infrastructure that can handle unpredictable workloads, vast data volumes and complex integrations. “Unfortunately traditional IT infrastructures can limit the speed, volume and flexibility required to make AI impactful,” Scholten notes.

“What is required is a cloud-native architecture able to provide the elasticity, connectivity and continuous data flows that allow AI to operate seamlessly across the business, detecting anomalies, trends and opportunities as they happen.”

While technology provides the foundation, the true measure of cloud-enabled AI lies not in the tools themselves but in the tangible business outcomes they enable, he adds.

Turning potential into performance

Yet, while the combination of cloud and AI unlocks enormous potential, success is not a given.

Organisations must navigate a set of practical and strategic challenges to realise meaningful value. These include reliable connectivity, safeguarding data, addressing cultural shifts and proving clear return on investment (ROI).

“These hurdles require deliberate planning and execution,” Scholten continues. “Only by tackling them head-on can businesses turn cloud-native AI into a sustainable competitive advantage.”

By aligning scalable cloud infrastructure, intelligent AI capabilities and a strong data-driven culture, businesses can unlock new levels of agility, innovation and resilience.

“Many companies might wonder where to start,” says Scholten. “The first step is a strong data strategy. Piloting AI use cases early and embedding governance throughout the cloud are also essential. Addressing challenges upfront ensures the transformation remains both secure and sustainable.

“The message is clear: organisations that embrace cloud-native AI today will define the competitive edge of tomorrow.”

For more information on Datacentrix’s cloud services and Hybrid IT offering, please visit https://www.datacentrix.co.za/cloud-services.html

AI’s potential to reshape industries is no longer theoretical, it’s unfolding in real time. Yet in South Africa, where energy is both finite and fragile, the rise of large language models (LLMs) presents a challenge.

Unlike jurisdictions such as the United States or European Union, South Africa does not yet have legislation specifically targeting data centre energy consumption. However, the National Data and Cloud Policy (2024) signals a shift in direction, proposing that data centre operators adopt independent backup power and cooling systems to improve operational reliability and resource efficiency. While these proposals are not yet mandated, they reflect a growing policy focus on boosting the efficiency and resilience of IT infrastructure.

For programmers, software developers, and business leaders, the message is clear: innovation must be reimagined for a resource-constrained world. This means embracing alternative approaches that prioritise IT, energy and general resource efficiency as a strategic imperative.

Fortunately, innovation in AI is proving that models don’t need to endlessly scale to be effective. While ballooning LLMs have been the rockstars dazzling global audiences, AI teams are turning to Small Language Models (SLMs) as small yet mighty alternatives that can be better suited for specific enterprise use cases.

SLMs typically have fewer than 10 billion parameters; tiny compared to LLMs, which can have hundreds of billions or even trillions. This makes SLMs a more sensible alternative when computing power is limited or when speed and low latency are critical. Their compact design allows them to run efficiently on less powerful hardware, using only a fraction of the energy larger models require. They’re also easy to deploy locally, which reduces the need for heavy infrastructure. This is especially important in areas of the country with limited digital resources. By keeping resource requirements modest, SLMs make AI more accessible to a wider range of users, enabling more inclusive innovation in key sectors.

Newer generations of GenAI take this even further by automatically switching to smaller models for simple queries and reserving larger models for complex tasks. In this way, various models can provide accurate, yet efficient, answers to more queries.

The argument for SLMs

Some organisations had dismissed SLMs, assuming they were unable to deliver enterprise-grade performance, would be hard to scale, and would have reduced general knowledge due to narrower training data. Many worried that these models would turn up short when it came to complex reasoning, multilingual capabilities, and effectively handling nuance and ambiguity. However, many of the misperceptions about SLMs have been disproven over the last eighteen months.

Underpinning this pivot toward SLMs is a growing recognition that data quality – not quantity – is the key to model performance. LLMs are often burdened by vast pools of raw, unfiltered data, much of which can be duplicate or entirely irrelevant. SLMs instead rely on so-called ‘data efficiency’ principles, where datasets are meticulously curated with precision and relevance in mind.

In fact, the targeted approach to purpose-built SLMs trained on curated, high-quality datasets has improved accuracy for domain-specific tasks while reducing the inefficiencies burdening larger model behemoths. Smaller models stand out for their ability to be fine-tuned more quickly and updated more often, making them flexible and ideal for dynamic settings. Because SLMs are typically easier to deploy and manage, they are often appealing to enterprises. They are also preferred in edge environments, such as in smart factory settings, where energy efficiency is essential.

Pilot programmes to integrate SLMs have already launched in legal, medical, and financial services, where these models deliver faster inference times, lower latency, and smaller hardware footprints.

Higher education is a key example of an industry in which SLMs can have a transformative impact in South Africa. When trained on subject- or task-specific datasets and equipped with customised algorithms, these models can support teaching, learning, and research activities. And in agriculture, developers can start building applications using globally trained models and adapt them with local context, such as crop varieties or regional climate patterns. By tapping into foundational models, local teams can focus their resources on adding the contextual intelligence that delivers real, distinctive value.

Open-weight models offer another alternative

Along with the rise of SLMs, open-weight models are also revealing themselves to be a viable energy efficient alternative to LLMs. Those models use a Mixture of Experts (MoE) architectures that are attractive to energy-conscious AI developers because they only activate a small subset of their parameters during inference, drastically reducing the compute and energy required for each task. A model with one hundred billion parameters might only need to use five billion at a time, for example. And, because open-weight models can be deployed locally and fine-tuned for specific use cases, they also relieve the energy burden on cloud infrastructure, making them well-suited for edge environments and enterprise applications where energy efficiency is important. The learnings of the last two years have demonstrated that SLMs can be just as effective, cost less, and do well at tasks that don’t demand the extensive knowledge base of an LLM.

As the next phase of AI development unfolds, the focus must shift to efficiency. South Africa’s evolving regulatory landscape, including the National AI Policy and a potential AI Act, will play a critical role in fostering innovation while guiding the responsible development of SLMs. These frameworks can help ensure that models are not only impactful but also aligned with societal needs.

For organisations leveraging AI, it will be necessary to embed efficiency as a foundational operating principle and consider the implications at every stage of the AI lifecycle – from data curation to model architecture design to deployment. By applying holistic, lifecycle thinking to IT challenges, developers will be able to refine their techniques and spark renewed creativity in the next wave of AI’s evolution.