Dr. Zohar Bronfman is thge Co-founder & CEO of Pecan AI. With deep expertise in computational psychology and data science, Zohar applied his inherent entrepreneurial spirit to Co-Found Pecan, right out of graduate school. Zohar holds two PhDs from Tel Aviv University – one in computational cognitive neuroscience and one other within the history and philosophy of science and technology. He also holds a BA in economics from the Open University of Israel.
Founded in 2018, Pecan AI is a predictive analytics platform that leverages its pioneering Predictive GenAI to remove barriers to AI adoption, making predictive modeling accessible to all data and business teams. Guided by generative AI, firms can obtain precise predictions across various business domains without the necessity for specialised personnel. Predictive GenAI enables rapid model definition and training, while automated processes speed up AI implementation. With Pecan’s fusion of predictive and generative AI, realizing the business impact of AI is now far faster and easier.
What was the journey like in founding Pecan AI and what are a number of the key milestones achieved along the best way?
Starting Pecan AI was quite the rollercoaster. All of it kicked off when my co-founder and I joined a global data science competition. We created a data-preparation automation that became Pecan’s prototype, but we missed the deadline and lost. As a substitute of moving on, we decided to show our prototype into something impactful. Just two months after ending our doctorates in 2018, we rented a small room at Tel Aviv University and commenced hustling. With limited business experience, we pitched our idea to enterprise capitalists. Thankfully, Haim Sadger and Aya Peterburg from S Capital saw potential and invested $4 million, giving us the boost we wanted.
One major milestone was raising $66 million in a Series C round led by Insight Partners, with backing from GV (formerly Google Ventures) and others. This funding allowed us to expand globally and speed up our development efforts.
How does your background in computational cognitive neuroscience influence your approach to developing AI solutions?
My background in computational cognitive neuroscience, together with my PhD in history and philosophy of science, plays a giant role in how I develop AI solutions. These fields help me understand each the technical and philosophical points of technology. This dual perspective is incredibly beneficial in today’s rapidly changing tech landscape. It allows me to create AI products that will not be just technically advanced but additionally ethically sound and user-friendly.
Are you able to explain the concept of Predictive GenAI and the way it integrates generative AI with predictive machine learning?
Sure thing. Predictive GenAI is all about merging Generative AI with Predictive Machine Learning. Generative AI lets users interact with data through natural language, making it easy to ask questions and guide the AI. Nevertheless, its predictive abilities are limited. That’s where Predictive Machine Learning is available in, because it processes data to make accurate future predictions. By combining these two technologies, Predictive GenAI allows even those with little data science experience to construct predictive models and use them seamlessly, like chatting with ChatGPT.
How does Predictive GenAI simplify the technique of creating and deploying predictive models for businesses?
Predictive GenAI simplifies things with features like Predictive Chat and Predictive Notebook. Predictive Chat acts like an AI sidekick, guiding users through the modeling process using natural language. It formulates predictive questions based on the user’s business concerns and generates a Predictive Notebook with ready-made SQL queries and sample data. This implies users don’t need to begin from scratch or have deep technical knowledge to get accurate predictions.
Could you elaborate on the case study involving the CAA Club Group and the way Pecan AI optimized their roadside assistance services?
Absolutely. The CAA Club Group used to spend per week manually forecasting roadside assistance, which was time-consuming and limited. After implementing Pecan AI, their data science team developed over 30 models to generate short-term demand forecasts twice per week. These forecasts predict call volumes and repair types hourly, ensuring efficient staffing and quick responses, especially during harsh winter conditions. Pecan’s platform also allows continuous improvement of those models, enhancing service efficiency.
How did Credit Pros profit from using Pecan AI for client churn prediction and what specific challenges did it solve for them?
The Credit Pros faced significant challenges with client churn prediction, which was a fancy and time-consuming process. Implementing Pecan AI reduced the model development time from three months to only weeks, enabling proactive retention strategies. This streamlined process allowed TCP to accurately predict client churn and devise effective strategies to retain clients, ultimately increasing their revenue.
How do the Predictive Chat and Predictive Notebook tools enhance user experience and make predictive analytics accessible to non-technical users?
Predictive Chat uses GenAI to create custom notebooks based on the user’s business questions and data. Users can interact with the chat in natural language, answering questions and following instructions, which simplifies the model creation process. The Predictive Notebook includes all of the needed code, allowing users to view queries, create custom tables, and understand the training dataset’s logic. This approach makes predictive analytics accessible to non-technical users by streamlining data preparation and model creation.
In what ways do you see Predictive GenAI transforming various industries and business functions?
Predictive GenAI empowers businesses to make data-driven decisions with unparalleled accuracy and efficiency. In manufacturing and logistics, it optimizes operations by forecasting demand and streamlining supply chains. In customer-centric industries, it enhances satisfaction and loyalty through targeted marketing and tailored recommendations. Predictive GenAI also fuels innovation by predicting market trends, guiding product development, and speeding up time-to-market. Its applications extend to healthcare for disease prediction and personalized treatment plans, and to sustainability efforts by optimizing resource usage and reducing environmental impact.
How does Pecan AI make sure the accuracy and reliability of its predictive models?
We ensure accuracy and reliability through rigorous testing and ongoing validation. Pecan AI uses separate training and test datasets to guage model performance, much like grading a college test. Key metrics like accuracy, precision, and recall are used to validate models during development and in production. We also promote transparency through explainable predictions, helping users understand the aspects influencing each prediction and fostering confidence in AI-driven insights.
How do you envision the role of Predictive GenAI evolving in the subsequent few years?
Looking ahead, the long run of AI shouldn’t be nearly predicting events but additionally prescribing actions based on those predictions. Predictive GenAI goals to automate decision-making processes and optimize business operations. Nevertheless, it’s crucial to know the associated risks and make sure the responsible use of AI. Because the technology evolves, it should play a critical role in enhancing operational efficiency, fostering innovation, and driving strategic decision-making across various industries.
