The conversations were dynamic, purposeful, and deeply aligned with GeoPoll’s mission of enabling organizations to access real-time, high-quality data across emerging markets. Below are some of my key reflections.

AI is the New Foundation of Africa’s Digital Transformation

A dominant takeaway from the conference was that AI is no longer a competitive edge, rather it is the foundation of business reinvention. Across industries, leaders demonstrated how AI and IoT are powering smarter agriculture, predictive analytics in fintech, intelligent automation in health and logistics, and data-driven policy design.

In a standout session moderated by Kitso Lemo (BCG), speakers including Mercy Ndegwa (Meta), Jamie Collinson (iSDA Virtual Agronomist), and Kevin Xu (Huawei Technologies) explored how AI is unlocking efficiency and inclusion across African economies.

The message was clear: Africa’s next leap forward depends on localized innovation powered by authentic African data.

Localization and the Data Imperative

Throughout MWC Kigali, participants emphasized the need for contextually relevant datasets to train AI models that reflect Africa’s languages, cultures, and consumer realities. This challenge is precisely where GeoPoll brings unique value.

Through GeoPoll AI Data Streams, we’ve built one of the world’s largest repositories of structured voice data from Africa with over 450,000 hours of verified recordings from more than 1 million individuals, spanning 100+ languages. These datasets are ethically sourced, demographically representative, and purpose-built for training Automatic Speech Recognition (ASR) models, Large Language Models (LLMs), and Generative Voice applications.

Localized datasets like these ensure that future AI systems, from chatbots to digital assistants, truly understand and serve African users.

Mobile-Led Innovation in Fintech, Gaming, and Everyday Life

The conference also spotlighted mobile-first innovation across fintech, entertainment, and gaming. In conversations with leaders from Visa, MTN, and GSMA, it became evident that Africa’s mobile ecosystem continues to drive engagement, commerce, and creativity.

GeoPoll’s own Gaming in Africa Report (2024) revealed that mobile dominates Africa’s gaming landscape, with 92 % of gamers using Google Play and 63 % making in-app purchases, many through mobile-money platforms. These insights reinforce MWC’s broader message: Africa’s digital future is mobile-first, data-driven, and youth-powered.

GeoPoll’s Role in Africa’s Digital Future

At GeoPoll, we sit at the intersection of data, technology, and social impact. Our proprietary solutions, from TuuCho, our always-on consumer-insights platform, to WhatsApp Research Communities (MROCs), and AI-driven Social and Speech Intelligence, empower organizations to understand audiences, test ideas, and monitor sentiment in real time.

Being part of MWC Kigali reaffirmed that Africa’s most transformative innovation will come not just from technology itself but from inclusive data that amplifies Africa’s voice.
That’s where GeoPoll continues to invest, in building the data infrastructure that powers decision-making and fuels AI innovation.

Looking Ahead

As AI, IoT, and mobile connectivity converge, Africa’s digital growth story is entering a bold new phase, one defined by intelligence, inclusion, and innovation at scale.

At GeoPoll, we’re proud to contribute to that story by providing the insights, tools, and data networks that help organizations turn algorithms into action.

John (JP) Murunga is GeoPoll’s Regional Director, Africa.

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Representative AI requires something different. It demands that models hear the breadth of human experience and language variation, not just the loudest or most connected groups. That begins with representative data. For decades, survey science has developed the tools to measure populations accurately through sampling, stratification, and weighting. Unlike scraped web data, which reflects who chooses to publish, survey research ensures inclusion of those who might otherwise be invisible.

This is where GeoPoll’s work is unique. We operate primarily in low-income countries across Africa, Latin America, and Asia. These regions are systematically underrepresented in global datasets. Our surveys reach communities that are often excluded from the digital traces AI relies on. Beyond geography, our sampling design incorporates income and education as core criteria, ensuring that the perspectives of low-income and less-educated populations are captured alongside those of more affluent groups. This intentional inclusion is critical because these voices are most often absent from the data that feeds AI systems.

Representative Survey Research Data for AI

Our approach is grounded in scale and depth. Every year, we conduct hundreds of thousands of telephone-based interviews that extend into rural villages, low-connectivity areas, and places where literacy rates are low and internet access is scarce. These conversations are live and unscripted, capturing how people actually communicate with the slang, cadence, accents, and evolving language that web-based datasets overlook. The result is a corpus of representative audio that reflects the daily realities of underserved populations.

This data has unique value for AI training. Unlike scripted phrases or synthetic samples, GeoPoll’s representative audio captures natural variation across cultures and regions. When used to train or fine-tune models, it consistently outperforms curated voice datasets because it is drawn from the real world rather than produced in a studio. It gives models the ability to recognize speech patterns as they exist in daily life, not as they appear in filtered or idealized forms.

Contrast this with the risks in today’s AI pipelines. Web-scraped data carries selection bias, temporal bias, and cultural bias. It reflects what gets published, not how people live and speak. Models then amplify those distortions, producing outputs that misinterpret slang, misrecognize dialects, or stereotype entire groups. Left unchecked, these gaps compound and erode trust in AI systems, hindering emerging market adoption widening the divide.

The science of sampling provides the corrective. By embedding representative data into AI pipelines, researchers can fill blind spots and build systems that perform consistently across diverse populations. This approach also provides a benchmark: survey data can test model outputs, reveal where failures occur, and guide targeted fine-tuning. It creates a feedback loop where AI evolves alongside the societies it is meant to serve.

If AI is to be truly global, it must be trained on datasets that reflect the global population. That requires more than volume. It requires representativity. Survey science has perfected the methods to listen to everyone, not just the few. Now it offers AI what it has always lacked: balance, diversity, and authenticity. The companies that focus on the quality and representativeness of their training data will be the ones that meet users where they are. Just as WhatsApp became ubiquitous by working for people everywhere, the companies that build representative AI will gain the most users and will emerge as the clear global leaders.

Nick Becker is GeoPoll’s CEO.

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Our presentation was anchored in findings from our whitepaper that compared results from a traditional CATI survey data with synthetic outputs generated using several large language models (LLMs). The session was a mix of curiosity, concern, and critical thinking, especially when we demonstrated how off-the-mark synthetic data can be in places where cultural context, language, or ground realities are complex and rapidly changing.

We started the presentation by asking everyone to prompt their favourite AI app with some exact questions to model survey results. No two people in the hall got the same answers. Even though the prompt was exactly the same, and many people used the same apps on the same models, issue one.

The experiment

We then presented the findings from our experiments. Starting with a CATI survey of over 1,000 respondents in Kenya, we conducted a 25-minute study on several areas: food consumption, media and technology use, knowledge and attitudes toward AI, and views on humanitarian assistance. We then took the respondents’ demographic information (age, gender, rural-urban setting, education level, and ADM1 location) and created synthetic data respondents (SDRs) that exactly matched those respondents, and administered the same questionnaire across several LLMs and models (even did repeat cycles with newer, more advanced models). The differences were as varied as they were skewed – almost always wrong. Synthetic data failed the one true test of accuracy – the authentic voice of the people.

Many in the room had faced the same tension: global funding cuts, increasing demands for speed, and now, the allure of AI-generated insights that promise “just as good” without ever leaving a desk. But for those of us grounded in the realities of Africa, Asia, and Latin America, the idea of simulating the truth, of replacing real people with probabilistic patterns, doesn’t sit right.

This conversation, and others we had throughout the conference, affirmed a growing truth – AI will undoubtedly shape the future of research, but it must not replace real human input. At least not yet, and not in the parts of the world where truth on the ground doesn’t live in neatly labeled datasets. We cannot model what we’ve never measured.

Why Synthetic Data Can’t Replace Reality – Yet

Synthetic data is exactly what it sounds like: data that hasn’t been collected from real people, but generated algorithmically based on what models think the answers should be. In the research world, this typically involves creating simulated survey responses based on patterns identified from historical data, statistical models, or large language models (LLMs). While synthetic data can serve as a functional testing tool, and we are continually testing its utility in controlled experiments, it still falls short in several critical areas: it lacks ground truth, it missed nuance and context, and therefore it’s hard to trust.

And that’s precisely the problem.

In our side-by-side comparison of real survey responses and synthetic responses generated via LLMs, the differences were not subtle – they were foundational. The models guessed wrong on major indicators like unemployment levels, digital platform usage, and even simple household demographics.

I don’t believe this is just a statistical issue. It’s a context issue. In regions such as Africa, Asia, and Latin America, ground realities change rapidly. Behaviors, opinions, and access to services are highly local and deeply tied to culture, infrastructure, and lived experience. These are not things a language model trained predominantly on Western internet content can intuit.

Synthetic data can, indeed, be used

Synthetic data isn’t inherently bad. Lest you think we are anti-tech (which we can never be accused of), at GeoPoll, we do use synthetic data, just not as a replacement of real research. We use it to test survey logic and optimize scripts before fieldwork, simulate potential outcomes and spot logical contradictions in surveys, and experiment with framing by running parallel simulations before data collection.

And yes, we could generate synthetic datasets from scratch. With more than 50 million completed surveys across emerging markets, our dataset is arguably one of the most representative foundations for localized modeling.

However, we’ve also tested its limits, and the findings are clear: synthetic data cannot replace real, human-sourced insights in low-data environments. We don’t believe it’s ethical or accurate to replace fieldwork with simulations, especially when decisions about policy, investment, or aid are at stake. Synthetic data has its place. But in our view, it is not, and should not be, a shortcut for understanding real people in underrepresented regions. It’s a tool to augment research, not a replacement for it.

Data Equity Starts with Inclusion – GeoPoll AI Data Streams

There’s a significant reason this matters. While some are racing to build the next large language model (LLM), few are asking: What data are these models trained on? And who gets represented in those datasets?

GeoPoll is in this space, too. We now work with tech companies and research institutions to provide high-quality, consented data from underrepresented languages and regions, data used to train and fine-tune LLMs. GeoPoll AI Data Streams is designed to fill the gaps where global datasets fall short – to help build more inclusive, representative, and accurate LLMs that understand the contexts they seek to serve.

Because if AI is going to be truly global, it needs to learn from the entire globe, not just guess. We must ensure that the voices of real people, especially in emerging markets, shape both decisions and the technologies of tomorrow.

Contact us to learn more about GeoPoll AI Data Streams and how we use AI to power research.

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For over a decade, GeoPoll has supported development agencies, governments, researchers, media houses, and commercial clients with timely, high-quality data from some of the world’s most difficult-to-reach regions. Our work has always been rooted in real human insight, collected at scale through mobile-based methods, and made accessible through flexible, remote research infrastructure.

That hasn’t changed.

What has changed, and continues to evolve, is how we use technology to make this work faster, more scalable, and more efficient. We pioneered mobile-based research in emerging markets when connectivity was a challenge and few others were investing in the space. We built custom platforms that made it possible to collect data in hard-to-reach areas, quickly and affordably. And we’ve continued to innovate ever since.

At GeoPoll, no research project is ever identical to the last. Our teams are constantly solving for context, adapting methodologies, building tools in real time, and finding technical solutions that work in places where standard platforms fall short. It’s this practical, hands-on innovation that defines how we operate.

The Direction We Are Taking with AI

Artificial Intelligence is changing how work gets done across every sector, and research is no exception.

As a tech-first research company, we have spent the last several years building, testing, and refining a range of AI tools across our data pipeline. Today, we are pleased to share that AI is now fully integrated into how GeoPoll delivers research, not to tick trend boxes, but as a way to improve the speed, accuracy, and consistency of data collection in the field.

We have embedded AI into the real systems we already use, including but not limited to:

• AI helps validate survey logic and translations before fieldwork begins
• AI checks live data for inconsistencies or anomalies during collection
• AI reviews call transcripts to ensure enumerators follow protocol
• NLP models help code and categorize open-ended responses
• Predictive tools support sample targeting and flagging during tracking studies
• We have built AI models and tools to help with faster data analysis and reporting

Importantly, AI doesn’t replace our teams; it superpowers them. Every GeoPoll project is still led by experienced researchers and supported by local teams. AI allows them to focus on analysis and insight, rather than manual review or error handling.

Simply, we are an AI-enhanced, human-led research organization. Our approach is grounded in the same fundamentals: direct data collection from people on the ground, strong field oversight, and practical, decision-focused reporting. But we’ve embedded AI tools and processes across the research lifecycle to improve efficiency, scale, and quality.

In short, we are still powered by people, but now enhanced by AI.

Where We Stand on Synthetic Data

With growing industry interest in synthetic data and fully simulated insights to replace direct data collection entirely, we have been asked where we stand. The answer is simple:

GeoPoll remains committed to real data from real people.

In the markets we serve, where lived realities often diverge from global assumptions, it is critical to ground decisions in firsthand, current input. We believe that AI can and should enhance this work, but not replace it.

Through internal testing, including findings published in our Whitepaper on Synthetic Data, we have seen firsthand how research based solely on synthetic data can miss the nuance, variability, and context that only direct engagement can provide. It can surface assumptions, but it cannot substitute for experience.

We have the capability to generate synthetic datasets, powered by our technology and one of the largest archives of primary research data in emerging markets, based on over 50 million completed surveys. And we do use synthetic data where it makes sense: to test scripts, simulate responses, and explore hypotheses before live deployment.

But we draw the line at using synthetic data as the final source of truth.

GeoPoll’s position is clear: credible, contextual insight must be built on real people and real voices, especially in regions where reliable data is scarce, outdated, or absent altogether. Our view is that AI should improve how we gather and interpret that data, not bypass it entirely.

Supporting Responsible AI Development with GeoPoll AI Data Streams

Our commitment to real human data also extends to how AI itself is trained.

As large language models (LLMs) and other generative AI tools become more embedded in global systems, one of the most pressing challenges is the lack of representation from underrepresented languages, dialects, and regions, particularly across Africa, Asia, and Latin America. These gaps can result in systems that are biased, less accurate, or outright unusable for a large portion of the world’s population.

GeoPoll’s AI Data Streams was developed to help solve this problem.

In addition to our core research work, we now support a growing number of organizations, including technology companies and academic institutions, in training and fine-tuning AI models using ethically sourced, real-world data from the communities we’ve served for over a decade. This includes primarily voice data, open-ended text responses, and labeled datasets collected in local languages and dialects. These data inputs are collected with consent, anonymized, and processed through workflows designed for AI training quality.

We are contributing real, high-quality data from underrepresented regions to help build more inclusive, context-aware AI systems. We see this work as a natural extension of our mission, supporting decisions, technologies, and tools that are grounded in the realities of the people they aim to serve.

Learn more about GeoPoll AI Data Streams here.

What This Means Going Forward

If you’ve worked with GeoPoll before, you’ll continue to see the same responsiveness, reach, flexibility, and reliability you are used to. The difference now is in how quickly and intelligently we can operate and deliver, while maintaining the human focus that’s always defined our work.

If you haven’t worked with us before, we invite you to experience how authentic human data, enhanced by the right tools, can strengthen your decision-making and reduce the time it takes to get there. Reach out to learn more about how we combine AI innovation with practical, on-the-ground execution.

The post From our CEO: How GeoPoll is Using AI to Strengthen Real-World Research appeared first on GeoPoll.

In this article, we look at how, from our own experience over the last few years, LLMs are being used to improve two critical stages of the survey lifecycle: design and data collection.

Why Survey Data Quality Still Needs Work

Even with digital tools, survey research continues to face familiar quality issues that can compromise results if left unchecked. The problems are often subtle but widespread, and fixing them manually is time-consuming and hard to scale.

• Poor question design leads to confusion – When questions are long, unclear, or use unfamiliar terms, respondents may misunderstand them. This results in unreliable or inconsistent answers, especially in surveys where literacy or education levels vary.
• Enumerator variation introduces bias – In CAPI and CATI modes, enumerators can inadvertently paraphrase questions, skip standard probes, or interpret responses differently. Even small variations can affect how questions are understood and answered.
• Respondent fatigue reduces engagement – When surveys are too long or repetitive, respondents lose focus. This often leads to rushed answers, skipped questions, or dropout, especially in mobile-based surveys where attention spans are limited.
• Translation gaps distort meaning – In multi-country surveys, even well-translated questions can carry unintended meanings. Cultural nuances and phrasing differences can cause respondents to interpret the same question in different ways.

These issues can’t be fully eliminated, but they can be better managed. LLMs offer new ways to automate early detection and correction, thereby improving quality without overburdening research teams.

LLM Powered Survey Design

Designing a good questionnaire is both an art and a science. Poorly structured surveys can compromise insights from the outset. LLMs support this process by improving clarity, consistency, and localization—quickly and at scale. Here’s how:

• Simplifying complex questions – LLMs can rephrase technical, wordy, or abstract questions into simpler, more accessible language. This is especially useful when surveying populations with diverse education levels or limited familiarity with certain terminology.
• Flagging confusing or biased phrasing – Models can identify double-barreled questions (“How satisfied are you with the product and the service?”), overly leading language, or ambiguity – issues that often go unnoticed until field testing.
• Standardizing question structure and tone – When surveys are built collaboratively, inconsistencies can creep in. Well-trained LLMs can help harmonize formatting, style, and tone across sections and ensure the questionnaire feels coherent from start to finish.
• Generating answer options – Based on the intent of a question, LLMs can suggest logical and mutually exclusive answer choices. From our experience at GeoPoll, this is particularly helpful when creating closed-ended questions for new topics or markets.
• Localizing and validating translations – In multi-country surveys, LLMs can compare translated questions against the source text to identify tone shifts or meaning drift. They can also suggest culturally appropriate alternatives when direct translation fails.
• Testing for logical flow and respondent fatigue –This is one area where researchers, rightly, spend a lot of time, yet it is too subjective – analyzing the overall structure to optimize the survey for respondents. LLMs can help by highlighting sections that may feel repetitive or too long, helping improve the flow and reducing dropout risk.

As a disclaimer, this doesn’t replace expert input, but acts as an intelligent first layer of review, to allow researchers to iterate faster and avoid common design pitfalls. The future of survey research lies not in replacing human expertise with AI, but in creating synergies between technological capabilities and research experience to deliver insights of unprecedented quality and depth.

Supporting Enumerators and Real-time Quality Checks during Data Collection

In interviewer-led surveys, data quality depends on how faithfully enumerators follow scripts and protocols. Here, too, LLMs can make a difference.

They can generate tailored training content based on the questionnaire, explaining the purpose of each question and how to handle common respondent reactions. Instead of relying on static manuals, training can become more interactive and responsive.

LLMs can also simulate interviews. Enumerators can practice with AI-generated respondent personas that offer varied and realistic answers, building confidence before going into the field.

And during data collection, LLM-powered assistants can offer on-demand support. If an enumerator is unsure how to handle a tricky response or apply skip logic, they can get instant clarification and minimize downtime and inconsistency in the process.

Once data collection begins, LLMs can help maintain quality by monitoring incoming responses and identifying red flags.

They can detect issues such as:

• Straight-lining or repeated patterns in answer choices
• Contradictions between responses in different parts of the survey
• Suspicious durations, such as surveys completed too quickly to be valid

Instead of waiting for manual audits, research teams can be alerted in real time. This enables quick corrective action, like pausing specific enumerators, reviewing flagged records, or adjusting quotas.

These automated checks help enforce quality at scale, even in large, multi-country projects where human oversight is limited.

The Limitations of Using LLMs—Especially in Emerging Markets

While LLMs offer substantial benefits, their application in survey research, particularly in emerging markets, also comes with challenges:

• Limited language coverage and dialect handling
  Many LLMs perform best in English and struggle with less common languages, dialects, or localized expressions, which are critical for engaging diverse populations across Africa, Asia, or Latin America.
• Internet and device accessibility
  Real-time LLM features often require connectivity or device capabilities that aren’t available to all enumerators or respondents, especially in rural or under-resourced regions.
• Cultural nuance and bias
  LLMs are trained on global data, which may not reflect local realities. Without oversight, this can lead to inappropriate phrasings, cultural misunderstandings, or even biased interpretations, especially when local context is key.
• Data privacy and ethical concerns
  Automating parts of the survey process with AI introduces questions around consent, transparency, and data handling, particularly where regulations are still evolving.

These limitations are a pointer to the importance of hybrid approaches. Tools like LLMs should complement, not replace, human expertise, local knowledge, and robust quality controls. At GeoPoll, we’re integrating LLMs into our systems with these constraints in mind, ensuring our solutions are grounded in context and aligned with the realities of remote data collection across the globe.

The Bottom Line

LLMs aren’t magic, but when applied thoughtfully, they can meaningfully improve how surveys are designed and delivered. At GeoPoll, we have been developing our AI models, and the impact has been better efficiency, better quality, and better work, which translates to faster, quality data for our clients, especially at scale.

Our learning: As survey demands grow more complex, the opportunity is clear: pair the best of AI with human expertise for higher quality, more actionable insights—anywhere in the world.

Reach out to the GeoPoll team to learn how we’re integrating LLMs into multi-country studies, mobile-based surveys, and rapid data collection at scale.

The post Improving Survey Data Quality with LLMs: Design & Data Collection appeared first on GeoPoll.

One of the most valuable yet underutilized datasets for fine-tuning speech AI models comes from survey interview recordings collected through CATI (Computer-Assisted Telephone Interviewing). These real-world, natural language conversations capture regional accents, speech patterns, socio-economic terminology, and sentiment variations—making them a goldmine for improving AI-driven speech recognition and analytics.

The Importance of Fine-Tuning in Audio-Based AI

Pre-trained AI models serve as generalized speech recognition systems built on large datasets primarily sourced from media transcripts, scripted dialogues, and high-quality recordings. However, real-world applications—such as call centers, telephonic surveys, market research, and opinion polling—demand models that can:

• Recognize diverse speech patterns from non-native English speakers or local dialects.
• Handle spontaneous, unscripted conversations, which often differ from media or studio recordings.
• Differentiate similar-sounding words in regional accents.
• Capture sentiments and emotions beyond just transcribing words.

Fine-tuning allows AI models to adjust their weights, phoneme recognition, and contextual understanding to perform better in these real-world conditions.

Why CATI Survey Interviews are a Game-Changer in AI

CATI survey recordings offer several unique advantages that make them ideal for AI fine-tuning:

1. Massive, Real-World Data Volume
   • Research organizations like GeoPoll conduct millions of CATI surveys annually across Africa, Asia, and Latin America, generating vast, diverse, and naturally occurring speech data.
2. Diverse Linguistic and Socio-Economic Contexts
   • Unlike scripted datasets, survey interviews capture real conversations across urban and rural populations, spanning various socio-economic classes, education levels, and speech idiosyncrasies.
3. Regional Accents and Code-Switching
   • Many multilingual populations switch between languages (code-switching) within a conversation (e.g., English-Swahili, Spanish-Quechua). This is hard for standard AI models to process, but fine-tuning with survey interviews helps.
4. Background Noise and Real-World Conditions
   • Unlike clean, studio-recorded speech datasets, CATI survey calls contain natural background noise, making AI models more resilient to real-world deployment scenarios.
5. Emotion and Sentiment Recognition
   • Market research and polling surveys often gauge public sentiment. Fine-tuning models with survey data enables AI to detect tone, hesitation, and sentiment shifts, improving emotion-aware analytics.

How to Fine-Tune Speech AI Models with Audio Survey Interview Data

Organizations seeking to improve speech recognition, transcription accuracy, sentiment analysis, or voice-based AI applications can fine-tune their models using real-world survey interview recordings. Whether it’s a tech company creating and improving voice assistants, a transcription service improving accuracy, or a research firm analyzing sentiment at scale – anyone, the process generally is:

1. Collect and Organize the Data

• Use authentic spoken language datasets from surveys, call centers, customer service interactions, or voice-based interviews.
• Ensure data diversity by incorporating different languages, dialects, accents, and conversational tones.
• Organize datasets into structured categories, such as demographic groups, topic areas, and call conditions (e.g., background noise, speaker emotion levels).
• Verify compliance with privacy regulations by anonymizing sensitive data before processing.

2. Convert Audio Data into a Machine-Readable Format

• If your AI model processes text, convert raw audio recordings into transcripts using automatic or human-assisted transcription.
• Include timestamps, speaker identifiers, and linguistic markers (such as pauses, intonations, or hesitations). This enriches the model’s understanding of natural speech.
• Label speech characteristics such as emotion (e.g., frustration, enthusiasm), background noise levels, or interruptions for models that analyze sentiment or conversational flow.

3. Train Your Model with the Right Adjustments

• If using a pre-trained model, fine-tune it by feeding domain-specific audio data. This helps it to adapt to regional speech patterns, industry-specific terms, and unscripted conversations.
• If developing a custom AI model, incorporate real-world survey recordings into your training pipeline to build a more resilient and adaptable system.
• Consider applying active learning techniques, where the model learns from newly collected, high-quality data over time to maintain accuracy.

4. Test and Evaluate for Real-World Performance

• Assess word error rate (WER) and sentence accuracy to ensure the model correctly understands speech.
• Validate the model on diverse demographic groups and audio conditions to confirm that it performs well across all use cases.
• Compare results with existing benchmarks to measure improvements in speech recognition, transcription, or sentiment analysis.

5. Deploy and Continuously Improve

• Implement the fine-tuned model into your AI applications, whether for transcription, speech analytics, or customer insights.
• Collect new, high-quality audio data over time to refine accuracy and adapt to evolving speech trends.
• Use feedback loops, where human reviewers correct errors, helping the AI model to learn and self-correct in future updates.

GeoPoll AI Data Streams: High-Quality Audio Training Data

The future of speech AI in multilingual, diverse markets depends on its ability to accurately interpret, transcribe, and analyze spoken data from all demographics—not just those dominant in global AI training datasets. Fine-tuning AI with survey interview recordings from CATI research can improve speech models to be more accurate, adaptable, and representative of global populations.

GeoPoll’s AI Data Streams provide a structured pipeline for accessing diverse, real-world survey recordings, making them invaluable for organizations developing LLM models that are based on voice or underserved languages.

With over 350,000 hours of voice recordings from over a million individuals in 100 languages spanning Africa, Asia, and Latin America, GeoPoll provides rich, unbiased datasets to AI developers looking to bridge the gap between global AI technology and localized speech recognition.

Contact GeoPoll to learn more about our LLM training datasets.

The post Fine-Tuning Audio-Based AI Models with Survey Recordings appeared first on GeoPoll.

For years, artificial intelligence has suffered from a critical blindspot: its narrow, often homogeneous view of the world. Traditional AI development has been like looking through a keyhole, capturing only a tiny, limited perspective of human experience. Most machine learning models have been trained primarily on data from North America and Europe, creating systems that fundamentally misunderstand the vast majority of global human communication and context.

Consider language, the most nuanced form of human expression. Current AI systems excel in English and a handful of European languages but struggle dramatically with the linguistic diversity of regions home to billions of people. A conversational AI trained solely on American English will flounder when confronted with the dialects of Nigeria, the coded slang of Indonesian youth, or the linguistic variations of rural Panama communities.

Being representative of global populations is essential. Emerging markets, in particular, offer a wealth of untapped, high-quality information that can drive innovation and significantly improve AI models. But they also present unique challenges that require innovative data collection and processing solutions.

The Importance of Data Diversity in AI Development

For AI models to perform accurately across different demographics, they must be trained on datasets that represent the diversity of the world’s population.

AI systems learn and evolve based on the data they consume. Just as a well-rounded education requires diverse and comprehensive knowledge, robust AI models depend on high-quality AI data. The benefits of utilizing quality data include:

• Improved Accuracy: When models are trained on reliable and representative data, they can make more precise predictions and decisions.
• Reduced Bias: Diverse datasets help mitigate biases that often arise when models are trained on homogenous data sources.
• Enhanced Generalization: Exposure to a variety of scenarios and languages enables AI systems to perform better in real-world applications.
• Innovation Catalyst: Fresh perspectives and novel data points from different regions can inspire innovative applications and use cases.

However, much of the current AI training paradigm relies on data from well-established markets, which can limit the scope and adaptability of AI solutions on a global scale. the result has been biases that limit AI’s effectiveness in emerging economies. There has been a struggle to interpret accents, dialects, and cultural nuances in regions such as Africa, Asia, and Latin America.

The Potential of Emerging Markets

Emerging markets are rapidly evolving digital landscapes brimming with potential. They present a unique opportunity to enrich AI training datasets with insights that reflect a more diverse array of cultural, linguistic, and socioeconomic backgrounds. Here’s why these markets are so promising:

• Diverse Linguistic Data – Emerging markets are home to hundreds of languages and dialects. Integrating these into your AI models ensures better language understanding and processing. This is particularly critical for natural language processing (NLP) applications, where nuances in local language can make or break the effectiveness of a model.
• Cultural Nuance and Context – Data from emerging markets bring in cultural nuances that are often missing from datasets sourced predominantly from developed regions. This diversity can help reduce cultural bias, enabling AI to better understand and serve global communities.
• Real-World Relevance – The challenges and scenarios prevalent in emerging markets often differ significantly from those in more established regions. By incorporating these unique data points, AI systems can be trained to address a broader range of problems, making them more adaptable and effective in diverse environments.
• Economic and Social Impact – Investing in AI datasets from emerging markets doesn’t just improve technology—it also supports local innovation ecosystems. By acknowledging and utilizing local data, companies can contribute to economic growth and social progress in these regions.

Challenges of AI Training Data in Emerging Markets

Despite the need for diverse data and the huge potential, collecting high-quality training data in emerging markets comes with distinct challenges:

• Language and Dialect Complexity – Many regions have multiple languages and dialects that are not well-documented or digitized.
• Limited Digital Infrastructure – In areas with low internet penetration, mobile-first or offline data collection methods are essential.
• Privacy and Ethical Concerns – Compliance with local data regulations and ethical AI principles must be prioritized.
• Data Labeling and Annotation – High-quality AI models require accurate data labeling, which can be difficult to achieve at scale in emerging markets.

GeoPoll’s Solution: AI Data Streams

As AI applications expand globally, ensuring that training data reflects the voices and realities of people in emerging markets is critical. Companies looking to scale AI solutions must prioritize ethically sourced, high-quality datasets from these regions to build more inclusive and effective AI systems.

At GeoPoll, we are uniquely positioned to transform the landscape of AI training with our innovative approach to data collection—AI Data Streams. Our platform has amassed over 350,000 hours of diverse, representative, and high-quality voice recordings from 1 million+ individuals across Africa, Asia, and Latin America, structured and ready for LLM training​. This treasure trove of audio data is more than just a record of conversations; it is a dynamic resource poised to revolutionize how large language models (LLMs) are trained.

The voice recordings, collected ethically and with respondent consent, capture the natural flow of language—intonations, accents, and conversational nuances that are often lost in text-only datasets. The diversity inherent in our recordings from emerging markets ensures that AI systems can learn from a wide range of linguistic inputs. This is especially critical for LLMs, which require vast amounts of high-quality AI data to understand and generate human-like language. With this rich, multilingual audio data, LLMs can become more adept at recognizing and processing a variety of dialects and accents, ultimately leading to more inclusive and culturally sensitive AI applications.

GeoPoll’s AI Data Streams bridges this gap by providing reliable, high-volume training data from Africa, Asia, and Latin America. By partnering with GeoPoll, organizations can drive AI innovation while supporting local data ecosystems and contributing to the responsible development of artificial intelligence.

To learn more about how GeoPoll can support your AI training data needs for emerging nations, contact us today.

The post Building Better AI with High-Quality Training Data from Emerging Markets appeared first on GeoPoll.

As artificial intelligence continues to reshape the way researchers gather and analyze data, synthetic datasets generated by large language models (LLMs) like ChatGPT and Llama are emerging as promising tools. They’re fast, inexpensive, and easy to deploy. But how accurate are they?

GeoPoll’s latest whitepaper explores this question through a groundbreaking study. We compared AI-generated synthetic data with real-world responses from a Computer-Assisted Telephone Interviewing (CATI) survey in Kenya to assess how well LLMs replicate demographic patterns, media usage, food consumption habits, and more in a low-resource setting.

Inside the Whitepaper

Some of the areas we covered in this 20+ page report include:

• How closely synthetic datasets reflect demographic characteristics

• Where and why LLMs diverge from real-world data (e.g., overestimating internet access via laptops)

• The influence of prompting language (English vs. Swahili) on data quality

• Whether known correlations—such as between education and food insecurity—are preserved in synthetic outputs

• The implications for researchers, policymakers, and data scientists seeking cost-effective data solutions

• How different LLMs compare

The findings are nuanced: while LLMs sometimes capture surface-level demographic traits, they often fail to reproduce behavioral patterns, cultural context, and complex social dynamics. The conclusion? Synthetic data can be useful, but it’s not a replacement for grounded, contextual data collection—particularly in underrepresented regions.

Why This Research Matters

More and more researchers and institutions are increasingly considering synthetic data a low-cost alternative to survey research, but the risk of drawing flawed conclusions increases—especially in places where context is everything. This paper offers a clear-eyed analysis of:

• Systemic underrepresentation in training data

• The limitations of prompt engineering

• Safeguards and validation protocols for responsible synthetic data use

• Adaptations to improve AI performance in local contexts (including GeoPoll’s role)

For researchers, these findings offer essential guidance for using synthetic data, responsibly.

Download the Whitepaper

For more information, please contact us here.

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