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  • Writer's pictureBeehive AI

Exploring the Frontier of AI with Alexander Visheratin, Head of AI at Beehive AI

As pioneers in the rapidly-evolving field of artificial intelligence, our team at Beehive AI seeks to turn the complexities of loosely-connected data into meaningful insights. To this end, we had a conversation with our Head of Artificial Intelligence, Alexander Visheratin, in which we delved into the unique problems we are solving, our distinct approach to data, and our models’ differentiation from others on the market, such as GPT-4.

As an industry and academic expert with years of experience in artificial intelligence and distributed systems, Alexander has a unique perspective not only on cutting-edge innovations but also tradeoffs in real-life scenarios.

Note: This conversation has been lightly edited for clarity.

What technical problem is Beehive AI solving?

Beehive AI enables companies to extract knowledge from unstructured data on any scale. Our focus rests on the unstructured data companies have both about and from their target audience, including users, customers, stakeholders, and more. This data may come from surveys (with open-ended questions), customer feedback programs, support notes, internal CRMs, and so on.

Why do I consider such data “unstructured”? Because this information neither has a predefined data model nor is organized in a predefined manner. To extract value from this data, you need to ingest it, perform complex exploratory analysis, and figure out how to get useful information from completely unstructured open-ended data. This is a very hard and resource-consuming process that can take many months for a team of data analysts.

What can you tell us about Beehive AI's technical approach for solving the problem? How is it different from other solutions?

There are three main pillars of our technical solution:

  1. Dashboards. To understand the data, you need to be able to explore it. Our dashboards are packed with all the functionality needed to wrangle the data - super complex filters, statistical weighting, segments, breakdowns, and many others. You can dive as deeply as you want, and our system supports you on your journey.

  2. AI. The core of our solution! Our base LLM and the tens of thousands of bespoke models that our platform continuously trains on top of it with enterprise-specific data are used to extract meaningful concepts from qualitative data. This problem is naturally challenging because people can answer the same question in countless ways and many of these different responses mean the same thing. Apart from precision, the main strength of our models is adaptability. No matter the amount of the data or how it changes over time, our AI will be able to learn from it and provide useful insights.

  3. Rich integrations and transformations. “All benchmark datasets are alike; each real dataset is broken in its own way.” Data processing is an art of its own, but people often underestimate how hard it is to prepare the data in the right way to make it usable. It is even harder to do automatically and dynamically. Our system processes tens of thousands of responses by applying multiple complex transformations. After this initial processing, users can further customize their dashboards by setting up numerous dynamic transformations.

How did recent events (e.g. explosion of LLMs) impact Beehive AI (technical aspects, not business)?

We gained access to more robust open-source models, which we have tuned and incorporated into our system. Obviously, we use some of the latest advances to power our own models. However, as many people say, data is the new oil. Without the right data to fine-tune or prompt, even the best models will fail to deliver. Luckily, we have enough domain-specific data to train smaller models and obtain superior performance. We continuously assess new models as they are released and explore novel ways how we can improve the overall product.

How does Beehive AI differ from GPT-4? How is it better? what does it not do (by design)?

The main difference is the purpose. GPT-4 is a system built around a very powerful language model to provide answers to any question (within reasonable limits). In contrast, our system is specifically engineered to derive actionable insights from data, focusing on the user’s unique informational needs. Of course, you can try to come up with a smart prompt and show some examples to GPT-4, but any complex analysis you get from it you need to take with a bag of salt. Furthermore, Beehive AI is a complete analysis platform that includes quantitative data and complex calculations, an area that GPT-4 performs poorly.

How are you tackling the issue of hallucinations?

First of all, the primary cause of “hallucinations” is the very general purpose of the system. When the model ingests the entire Internet, it is quite understandable that during the generation of the response, it can “wander” to the wrong corner of its “memory.” Our models are restricted in scope by design, which significantly reduces the likelihood of generating outputs not represented in the original data. Plus, we have a team of trained linguistic experts who help to steer the models during the training and continuously fact-check our models.

How do you handle very niche domains and/or small datasets?

Having been trained on a large dataset from many diverse domains, our models handle new domains rather well. But when we face very new/niche domains for which we don’t yet have a lot of data, our linguistic team gets involved and shows the model the right way of extracting pertinent concepts from the data.


Exploring the frontier of artificial intelligence requires an unwavering commitment to innovation, adaptability, and resilience. Under the guidance of Alexander Visheratin, our team at Beehive AI continuously pushes boundaries, developing technology for deep data exploration.

Our efforts in artificial intelligence go beyond simply responding to changes - we aim to lead the way, unlocking the full potential of AI and transforming the future of both technology and the world.



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