We offer three model sizes, Overview, Insight, and Precision, to give you the right balance between speed, depth, and detail, depending on your needs

• Overview:
  

  Designed for quick, high-level insights. It uses a smaller number of drivers, making it ideal when you need fast answers or a broad understanding without diving into details.

• Insight:
  

  Provides more context and detail than the Overview model, using 10 drivers. It helps you understand the key drivers and their relationships, offering a balanced view without overwhelming complexity.

• Precision:
  

  Designed for deep analysis. It incorporates 15 drivers, allowing you to explore how each factor influences each other and the outcome in detail. Perfect for strategic planning and confident decision-making.

Our models are built on Bayesian networks, which are designed to capture the complex relationships between different drivers (nodes) in your model. In a Bayesian network, each driver can influence not only the final outcome but also other drivers within the network. This means that when you adjust one setting, it may have ripple effects, impacting several other drivers, sometimes in ways that aren't immediately obvious.

Because of these interconnected influences, a change you make to one driver might indirectly affect others, which can then combine to produce a result that feels counterintuitive or even opposite to what you expected. This is a natural property of Bayesian networks and is part of what makes them powerful for modeling real-world complexity.

Our process involves multiple steps to ensure we address your inquiry thoroughly.

Our research agents then go to work collating information regarding your specific inquiry and then our system generates a comprehensive model of your query.

The exact timing of this process varies based on the nature of your question and the size of the model your chose to build.

Model Sizes and their estimated response times:

• Overview: between 12 and 16 minutes.
• Insight: between 22 and 28 minutes
• Precision: between 35 and 45 minutes

Our models are built using Bayesian networks, which are designed to handle uncertainty in a structured and transparent way. Rather than producing a single, fixed prediction, Bayesian models generate a probability distribution over possible outcomes. This means they don't just tell you what might happen, they also tell you how likely each outcome is.

This approach allows us to express confidence levels in predictions. For example, the model might indicate that there's a 75% chance of Outcome A and a 25% chance of Outcome B. These probabilities are based on the relationships between drivers in the network and how they influence each other.

However, like any probabilistic model, the most favorable or most likely outcome is not guaranteed to occur in the real world. When you are building your model, you may not have considered a possible outcome and it is not capture by the model. Real-world systems are complex, and unexpected factors can influence results. That's why understanding the range of possibilities, and their likelihood, is often more valuable than relying on a single prediction.