When an operational issue is reported, reproducing it is one of the first steps in an investigation. Finding and connecting the data you need to triage the issue can be intensive. With Nextmv, replaying history (with easy access to all the data) is just a click away.
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Push your Python decision model from a local file to a remote application in minutes – whether you’re using a notebook or running in another Python environment. Conduct tests with fully featured experimentation tooling, collaborate and share results with teammates, and get observability into model performance.
Run multiple solvers or models in parallel to automatically select the best plan for your business with ensemble runs. Use your unique rules as selection criteria to easily converge on a plan, validate model configuration, and encourage stakeholder buy-in.
2024 was the year of the Python modeling experience, optimization integrations, and helping modelers find solutions even faster with parallel runs and interactive visualizations. In 2025, we look forward to combining ML & OR, more data integrations, and decision pipelines for smoother operations.
Increasingly, OR practitioners are seeking to incorporate more real-world uncertainty into decision models instead of only relying on deterministic optimization approaches. In this interview, we’ll explore this topic through the lens of Seeker, a new stochastic optimization solver.
Whether you’ve already built a decision model or are just getting started, developing your optimization project on the Nextmv platform will give you the framework, testing tools, and ease of integration required to prove the value of your decision model.
