• It offers one comprehensive, intuitive interface to guide you step by step through your project
• It allows you to create and reuse large libraries of GenAI twins and workflows
• Inputs can be easily changed and workflows updated without restarting entire conversations or overwhelming a model’s context memory
• It uses LLM capabilities to develop the most effective prompts for each task. So there is no trial and error in getting to the right embeddings / answers

GenAI twins, also called synthetic users or virtual customers are virtual representations of real-world personas. They can be used to understand customer needs, obtain industry expertise or test and validate product ideas. They work by combining data modeling techniques with behavioral algorithms to create realistic user simulations. They typically leverage machine learning models (e.g. LLMs) trained on anonymized real user data and can be tailored to represent specific customer segments.

Several studies have shown that GenAI twins are able to mirror real customer behavior with high accuracy. Check out two papers discussing the use of virtual personas in market research. Often, the most effective way to use GenAI twins is to complement synthetic insights with real, targeted customer conversations

There is a growing body of academic research that proves that the behavior of synthetic personas closely approximates that of real people. One paper we have relied on was produced by D^3 at HBS, entitled “Using ChatGPT for market research“ by Ayelet Israel et al.; another paper entitled “Out of one, many“ published by researchers at Cornell. The field keeps evolving and we continue to monitor the latest developments, and if anything, the accuracy keeps improving.

Our deployments confirm the growing body of academic research that proves their reliability. In all our experiments and deployments we have found that the insights generated are consistently 90% aligned with traditional methods.

• First, our platform allows you to deploy whichever LLM is best equipped to serve your project. E.g. if there are concerns regarding some specific LLM, we can exclude those models.
• Second, we create fenced environments for you to deploy AlgoVerde in a secure private instance.
• Third, inputs loaded into AlgoVerde are never used to train the underlying LLMs and there are no data leakages.

After a first introductory meeting, we offer an extended 2-4hr bootcamp to provide you with hands-on experience with the platform, build your first customized GenAI Twins and detail the challenge to be tackled. At the end of the bootcamp you will have a project proposal with clearly defined outcomes and a view on the workflows to be built.