Opvious

We enable data-scientists
to optimize effortlessly

Solve mathematical programming problems at scale with our batteries-included optimization platform.
No separate solver or expensive licenses required.

How it works

Go from idea to decisions in minutes

Define your model

Get started from your favorite IDE or a Python notebook with a high-level declarative modeling API - or by directly writing the math.

Deploy it

Upload your model with a simple call. Saved models go through extensive static validations, catching many categories of errors early - even before adding data.

Get solutions

Solve your model from anywhere with an API call, integrate with pandas-compatible data sources, or embed optimization into a web application.

We optimize for your productivity

Strong safety guarantees

All models are statically checked for a wide array of errors, including mismatched indices, unexpected equation degrees, unused variables... You can have confidence that your model will run, even before running it.

Multi-objective support

Not sure how to balance multiple objectives? No problem - we support the most common multi-objective strategies, from simple linear combinations to efficient epsilon-constraints using warm-starts.

Smart infeasibility detection

Our model representation unlocks a variety of powerful model transformations, including semantic constraint relaxation. This allows you to quickly identify the root cause of many infeasibilities.

Live progress notifications

It's not fun to wait in the dark for long-running solves to complete. Our API and SDKs stream real-time updates back to clients, including objective value and relative gap, so you can keep track of their progress.

Performance insights

Detect potential bottlenecks and common sources of numerical issues from automatically generated statistics: parameter weight distributions, reified constraint counts, quadratic objective sparsity...

LaTeX equation generation

Generate the model's source of truth--its mathematical definitions--from readable Python code and confirm at a glance that its implementation is consistent with its specification.

Modular modeling patterns

Reduce complexity with built-in optimization patterns (activation variables, masked subsets, ...) and expand your modeling toolkit by implementing your own with composable modeling fragments.

LP format exports

Want to inspect the generated problem as seen by the underlying solver? No problem - our API allows you to export it in LP format, with all constraints and variables fully annotated.