TKRISK: Model. Compute. Analyze.

Empower your risk management with our advanced probabilistic graph application.

Intuitive Design. Powerful Analytics.

Our intuitive interface puts advanced risk modeling at your fingertips, combining simplicity with robust analytical capabilities.

Simple

Build advanced risk models effortlessly with our flexible and well-documented UI.

Powerful

Leverage analytical capabilities powered by Tenokonda's R&D team's cutting-edge quantitative libraries.

Scalable

Seamlessly integrate with existing cloud infrastructure for unlimited growth potential.

TKRISK Scenario Generation

TKRISK Scenario Generation is a sophisticated UI-driven application designed to facilitate the construction, specification, and analysis of probabilistic graphs. It seamlessly integrates an intuitive user interface with powerful Python-based backend computation, enabling users to model complex risk scenarios effectively.

At the UI level, users can visually build Directed Acyclic Graphs (DAGs), defining random variables as nodes and specifying their conditional dependencies through edges.

This graphical approach simplifies the formulation of probabilistic models, making risk analysis both accessible and interpretable.

The backend consists of specialized Python libraries that handle the mathematical and statistical operations required for scenario generation. These modules enable key functionalities such as:

✔ Graph sampling

Generating data points that conform to the probabilistic structure

✔ Conditional probability computations

Estimating outcomes given prior information

✔ Monte Carlo simulations

Running multiple iterations to assess uncertainties

✔ Bayesian inference

Updating probabilities based on new evidence

By decoupling the UI from the computation engine, TKRISK ensures both user-friendly interaction and scalable, high-performance calculations.

Analysts and engineers can define and explore what-if scenarios, assess risk factors, and generate synthetic data—all within a visually guided yet computationally rigorous framework.

This hybrid architecture makes TKRISK a powerful tool for finance, climate modeling, public health, and other risk-intensive domains, where understanding uncertainty is critical for decision-making.

TKRISK Scenario Analysis

TKRISK Scenario Analysis is a UI-driven application that enables users to visualize, analyze, and interact with multistep probabilistic graphs, also known as Dynamic Bayesian Networks (DBNs).

By incorporating the overtime component, it allows users to explore how risk evolves across different time steps, making it an essential tool for time-dependent decision-making and forecasting. At the UI level, users can:

✔ Visualize

the evolution of probabilistic dependencies over time

✔ Perform "What-If" analysis

Estimating outcomes given prior information

✔ Assess sensitivity

by quantifying how changes in one variable propagate through the network

✔ Analyze conditional probabilities

dynamically across multiple time steps.

By leveraging Dynamic Bayesian Networks, TKRISK Scenario Analysis allows users to simulate and analyze evolving risks in domains where time-dependent uncertainty is critical.

This includes financial forecasting, climate modeling, supply chain risk, epidemiology, and other applications requiring sequential decision-making.

The integration of an intuitive UI with a powerful computational backend ensures that users can construct, analyze, and refine their models efficiently, making data-driven decisions with a clear understanding of how risks unfold over time.

TKRISK Functionalities

Data scientists and engineers can integrate TKRISK Python into their own workflows, leveraging its powerful libraries to build custom probabilistic models in industries such as finance, climate risk, epidemiology, and AI-driven decision-making. By offering a flexible, scalable, and high-performance modeling framework, TKRISK Python empowers organizations to quantify uncertainty, evaluate risks, and make data-driven decisions with cutting-edge probabilistic techniques.