Work together on your decision apps and iterate faster with increased visibility across your entire team – from model management to experimentation. It’s as simple as inviting users to join your team in the Nextmv console.

Shadow testing isn’t just for the MLOps crowd. Decision algorithm teams can have it too for their optimization models solving routing, scheduling, allocation, and fulfillment problems across supply chain, retail, healthcare, and more.

Every time you run your app, Nextmv captures metadata that provides insight into how the app is performing. Use a simple script to pull that run data into Google Sheets to analyze and visualize KPIs over time.

You’re pretty sure your staging model is ready for production. Pretty sure... But you want to test it under real-world conditions without real-world impact. Shadow testing gives your “understudy” algorithm the stage to prove its mettle.

We are excited to release the public beta of our new out-of-the-box routing engine that lets users solve VRPs with a low-code SaaS – and use the same model to customize and incorporate testing into their dev workflows.

Decision optimization technology plays a critical role in everyday operations — food delivery, healthcare staffing, and subscription box assembly — but it can often stand in its own way. It doesn’t have to.

We’re thrilled to announce the beta release of algorithm acceptance testing with Nextmv. We’ve streamlined making go/no-go decisions for decision model changes – paving a clear path to production.

Identify how updates to your model will impact business metrics using batch experiments. Plus, add more context to your apps by naming your runs, viewing the run input directly in the console, and more.

Welcome to the what-if wonderland of parallel universes. What if you expand your delivery region? Or fulfill orders from stores and distribution centers? Or hire more staff? Scenario tests help provide the answers. And, no, it’s not the same as simulation.

You need to change your decision model, but you’re not sure how it’ll impact your KPIs. Will there be unexpected effects? Batch experiments allow for exploration through summary statistics to orient yourself to impacted metrics.

You’ve got a production decision model and an updated decision model. Should you ship the new model to prod? Will the new model meet your acceptable KPI thresholds? Acceptance testing provides the answers by delivering a documented, repeatable decision-making process.

Do you launch decision model A or B? What happens if an operator makes a manual override? How does a new optimization model perform against real-world data? Testing and experimentation has the answers, but getting them has traditionally been a challenge.