The model, called Sahara v2, supports 24 African languages and recognises more than 500 African English accents. The company said the system was trained using more than 14 million audio clips collected from over 40,000 speakers across Africa and the diaspora.

For many users on the continent, voice technology usually has challenges with everyday phrases and names. Common expressions can be misheard or completely distorted, making digital assistants unreliable for basic tasks.

Developers say the problem lies in how most global systems were built. Many were trained mainly on Western speech patterns and do not align with the tonal nature, accent variety and frequent language mixing common across African countries.

With Sahara v2, Intron says it wants to close that gap by building technology that listens to how people actually speak. The recordings used to train the system were gathered across environments, including clinics, courtrooms, call centres, streets and offices.

The new model covers languages such as Hausa, Swahili, Yoruba, Igbo, Zulu, Twi, Kinyarwanda and Xhosa. In total, Intron says its systems now support 57 languages.

One of the additions is a bilingual speech recognition system that switches between English and Swahili. Intron developed the model with Kenya-based health provider Penda Health to better match how people naturally move between both languages in conversation.

The company also released a Hausa text-to-speech system designed to power local language voice assistants that can run continuously for services such as customer support.

Intron said the new system can also operate offline, allowing organisations to run voice tools locally where privacy or data security is a concern.

According to the company, Sahara v2 performs better on African speech compared with several widely used global models. These include systems developed by Google, OpenAI, Amazon Web Services and Microsoft.

Testing carried out by the company showed stronger accuracy when recognising African names, locations, numbers and sector-specific terms used in areas such as finance, healthcare and telecommunications.

Several organisations have already begun using the system in their services. These include voice banking platforms, medical documentation tools, courtroom transcription systems and automated call centre software.

Ayo Oluleye, head of Data and Insights at ARM Investments, said the model improved the accuracy of automated transcription.

“Using Intron AI models, we’ve seen significant improvement in transcription and summaries compared to models we previously explored. Their systems capture context and nuance better, leading to more accurate results.”

Sarah Morris, chief product officer at Audere, said the system also performed well during testing. “In our testing, accuracy was excellent on several Southern African accents and APIs were robust with 99%+ success rates.”

Alongside the launch, Intron also released its first Africa Voice AI report for 2026, examining how voice technology is being developed and used across the continent.

The report aims to guide governments, businesses, investors and researchers working to expand digital services that rely on speech technology.

Tobi Olatunji, chief executive of Intron, noted that the project shows what happens when technology is designed with local languages in mind.

“Sahara v2 proves that when technology is built with deep cultural and linguistic understanding, amazing things can happen, and we’re just getting started.”

This makes Deepgram IBM’s first voice partner, providing real-time transcription and voice features for enterprise clients.

The integration is designed to improve how companies handle complex audio environments, including background noise, accents, and natural conversation.

It also supports a wide range of languages and regional dialects, including multiple Arabic and Indian variants. Users will gain access to real-time captioning, natural-sounding voices, and options for custom tuning.

These tools can be applied across sectors such as healthcare and finance, supporting automated customer care, call analysis, and voice-driven data entry.

Scott Stephenson, Deepgram CEO and co-founder, said, “Voice is rapidly becoming the default interface between humans and technology, and enterprise deployments require a real-time platform that is accurate, low latency, and reliable at scale. 

“By embedding Deepgram inside watsonx Orchestrate Agent Builder, IBM clients can build voice agents and voice-enabled workflows on top of a real-time foundation that has been developed and refined over more than a decade.”

Nick Holda, vice president of AI Technology Partnerships at IBM, added, “Our watsonx Orchestrate integration powered by Deepgram APIs introduces new speech recognition and transcription capabilities to IBM clients, refining and modernizing their operations. 

“This collaboration aims to help enterprise organizations accelerate their AI initiatives and reinforces IBM’s open ecosystem, bringing choice and cutting-edge voice technology to partners and customers.”

The partnership is expected to strengthen IBM’s ability to provide flexible voice solutions to enterprise clients while expanding Deepgram’s reach to new customers through a trusted platform.

Deepgram provides real-time speech-to-text, text-to-speech, and full speech-to-speech features through cloud or on-premises APIs.

It has processed over 50,000 years of audio and transcribed more than one trillion words. IBM, on the other hand, provides hybrid cloud, AI, and consulting solutions to clients in over 175 countries.

The company, which transitioned from a general AI solutions provider to a voice-first call centre automation platform just six months ago, is now placing itself as a key backend partner for banks, fintechs, insurers, and ISPs across the continent.

Before voice AI, there was data analytics, and before that, hospitality. Each pivot was a response to market challenges, but the voice AI play is different, revenue is climbing, and the business case is obvious. 

With nearly $1 million in monthly revenue and projections of $10 million in ARR by the end of 2025, Caantin is betting that banks and financial service firms can’t afford to ignore voice-based automation.

The startup’s infrastructure now supports over one million calls per day. Clients include names like Carbon and Fairmoney. In fact, Carbon’s CEO, Chijioke Dozie, is also an investor in Caantin. And it’s not just about performance metrics; it’s about cost-cutting at scale.

Customer service, especially for loan recovery, is one of the most expensive and high-stakes operations in banking. “If these banks stop calling borrowers, they lose money,” said Njawa Mutambo, Caantin’s CEO. “But managing that operation is expensive and fragile. AI is not a nice-to-have. It’s essential for scale.”

Caantin’s pricing is aggressive. In Nigeria, it charges ₦185 per minute (about 12 cents)—which is nine times higher than local telecom operator rates. Yet, for financial institutions bleeding cash on sprawling customer service teams, the ROI more than justifies the premium. 

One of its clients, Nigerian fintech Cowrywise, reportedly managed 100,000 customer calls in just three months using only one human staff, something that would normally require around 30 agents.

Caantin estimates a 933% return on investment and a 1.3-month payback period for businesses switching from human agents to voice AI.

Mutambo had previously raised $2.16 million for TopUp Mama, a procurement platform for restaurants across Kenya and Nigeria. That experience, deeply embedded in B2B logistics, local operations, and scaling infrastructure, appears to be impacting how Caantin is built.

Now, the company is planning to go beyond Africa. Its next major push is Latin America, where the cost of labour is higher, but the challenges of customer engagement are nearly identical. “In Brazil, the cost of call centre labour is around $2 per hour. In Nigeria, it’s closer to 25 cents,” Mutambo said. “So the ROI for AI is even stronger in LATAM.”

Brazil’s $262 minimum wage compared to Nigeria’s $46 only stresses the economic gap Caantin is looking to exploit. And in markets where businesses are desperate to improve margins, automation is now a means for sustainability.

Other companies like YC-backed Bland AI and Observe.AI are also building voice-first platforms. But few are adapting their systems to support African languages or integrate directly with regional fintech infrastructure like Paystack or Flutterwave. That’s where Caantin sees its edge.

“We are a telecoms business tailored to financial services. Their growth becomes our growth,” Mutambo explained. “By serving banks and fintechs, we are effectively hedged within a high-yield vertical.”

The company is developing advanced analytics to pull insights from voice data, turning it into a decision-making asset for clients. This positions Caantin as more than a call automation tool; it’s aiming to become a strategic enterprise layer across industries.

In a continent where under 1% of global AI research is produced, but where infrastructure inefficiencies are common, Caantin’s push for voice-first AI is timely and necessary. High illiteracy, low smartphone usage, and poor connectivity make voice far more inclusive than apps or chatbots.

The global call centre AI market is projected to grow from $1.6 billion in 2022 to over $7.5 billion by 2030. Caantin is angling for a piece of that, starting from Africa but with its eyes clearly fixed on bigger, more lucrative terrain.