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Many data science and analytics teams use machine learning to gain insights from historical data. A growing group has started using optimization (prescriptive analytics) to further support the decision-making process. Prescriptive analytics is another AI technique that enables decision-makers to evaluate opportunities and trade-offs amid business constraints. This is crucial to make data and business rules actionable instead of only providing insight into your data’s behavior.

Available soon: Mathematical optimization modeling guide

The benefits of using a low-code platform to build and deploy optimization apps

The only low-code platform that empowers you to develop and deploy mathematical optimization based apps

With AIMMS, your team gets flexibility, speed, app deployment and maintenance built-into a cutting edge technology. Our low-code platform simplifies testing new approaches on the fly. You can go from idea to a prototype quickly and iterate with end-users, resulting in higher adoption and satisfaction. Build, improve, deploy and repeat, all in a single platform that integrates with your existing IT architecture.

What customers love

Mature & Flexible

AIMMS helps you develop a working model and end-user app in an intuitive way that is readable for non-mathematicians supported by a broad range of diagnostic tools.

Integrated visualization

With minimal design knowledge you can create modern web-based user interfaces to deliver intuitive and interactive applications to your decision-makers.

One-step deployment

Easily test new prototypes. No need to code your own interfaces, deployment setups etc., AIMMS Deployment connects it all for you in one step.

Always enough power

The AIMMS cloud deployment will ensure there is always enough capacity and performance to help you solve the toughest problems at lightning speed.

The power of AIMMS compared

Spreadsheets are:

  • Not set up to handle today’s complex problems.
  • Hard to collaborate on. The logic behind them is often only clear to the owner.
  • Very manual and time-consuming to use.
  • Rarely integrated with other systems.
  • Limited in the amount of data they can handle.
  • Error-prone.

With AIMMS you get:

  • A robust optimization engine that helps you tame complexity.
  • Centralized data management for ease of collaboration.
  • Easy deployment, training and support.

 Other algebraic modeling languages have less of a focus on:

  • Visualization; it’s often not an integral element.
  • End-users; only experts can read the models.
  • Data input; it’s often not easily taken care of.
  • Data manipulation; can’t be done straight from the app by end-users.

With AIMMS you get:

  • A webUI builder focused on end-user experience.
  • Fast prototype deployment to iterate with end-users.
  • A clear data model to avoid spending time understanding and debugging obscure models.
  • A set of diagnostic tools, such as a debugger, a profiler, and a one-of-a-kind math program inspector to save time and effort fixing errors.

Open source software:

  • Don’t have a vision for optimization-based app building at their core.
  • Don’t have end-user visualization built-in, you have to code it.
  • Don’t have deployment setups in place, you need to build it.
  • It takes more time and effort to build and maintain apps.
  • It’s more difficult to switch solvers or doing multi-scenario runs.
  • Leads to a higher TCO over time

With AIMMS you get:

  • A low-code platform with built-in diagnostic tools.
  • One-step deployment to get a fully functional prototype straight to decision-makers.
  • Access to features that turn your apps into true multi-user decision support tools.

Explore the benefits of using a low-code platform to build and deploy optimization apps