Struggling with optimization problems that Gurobi can’t model accurately — or at all? Or is your Gurobi model too large and slow to solve efficiently?
Get in touch with us! We’ll build a surrogate model for you — free of charge — that’s faster and/or more accurate.
Alternatively, you can try building a surrogate model yourself using causara. Check out our demos, such as how we created a Gurobi model for molecule prediction. You can also provide both an existing Gurobi model and a more detailed objective function—causara will then fine-tune the model to close the gap between it and the objective function.
AI-powered Optimization
ABOUT US
We are a team of optimization and machine learning experts aiming to revolutionize the way optimization problems are formulated and solved. Our vision is to build the world's most advanced AI system for mathematical optimization to enhance model accuracy, accessibility, and real-world impact.
WHY CAUSARA?
Create Better Gurobi Models
Effortlessly build, refine, and deploy Gurobi models with AI-driven automation and intelligent optimization.
01
Optimize any objective function
Define your optimization problem directly in plain Python. Our AI compiles this function into an exact or approximate Gurobi model, searching for the implementation with the fewest constraints and the fastest, easiest optimization.
If the function is too complex, causara trains an accurate surrogate model to approximate the objective function. If a Gurobi model is provided alongside the more detailed Python objective function, causara will fine-tune the Gurobi model to close the gap between the model and the objective.
Finally, causara applies intelligent post-processing routines to further align the results of the approximate or surrogate Gurobi models with the original Python objective function.
02
AI-enhanced GUI for Gurobi models
We’ve developed an intuitive GUI that enables non-technical staff to run Gurobi models with ease. Load problem instances from Excel or other sources, modify the Gurobi model using natural language to meet temporary requirements, visualize results, and much more.
Example: in a classic TSP problem, real-world constraints may require temporary modifications. With a simple command like, "ensure that city X and city Y are within the first 10 stops of the route", end-users can adjust the model without writing a single line of code.