Artificial Intelligence / Development / Rappid (JointJS+)

Building AI models 40-50% faster

AppOrchid

The company is based in Silicon Valley and specializes in AI (artificial intelligence) and cognitive computing. Apporchid develops applications that make the internet of everything think like a human brain, as stated in their mission.
Big data, AI and NLP technologies used in the apps they build help identify patterns, risks, and opportunities previously impossible with traditional analytical tools.

Introduction
About the project

When we were first approached by AppOrchid, they had a broad idea of what they wanted to be developed – an application component helping build AI models in a visual way substituting programing. They were looking for developers who would have experience with diagramming, preferably working with some open-source tools.

Our tasks

Developing drag and drop capability for visual components

The client had a rough idea in mind how to achieve it, and we had developed it further to provide the client with a working solution in the end.

Make a tool easily configurable

We had to work out the solution that would allow the client to extend the application with new tasks types easily in the future.

01

Challenges

1. Finding a way to compose AI models in a visual way

As the client had a rough idea in mind about the end result to be achieved they looked for a software development partner who would be able to build a design application from scratch.

2. Finding developers proficient at Rappid (JointJS+) – an open-source diagramming framework to perform the project

Having decided to develop the tool in Rappid (later JointJS+) the client looked for an experienced that would be skilled at working with diagramming frameworks.

Neoteric was a great choice for us. By our estimate, the app will help build AI models at 40%–50% faster once it is fully implemented. That will loosely translate into several man-hours of coding saved.
Krishna Kumar, AppOrchid CEO

Krishna Kumar, CEO of AppOrchid

02

Solutions

1. Building a visual design app for pipeline visualization

We developed the Pipeline Composer application that helps companies to visually compose AI applications. The visual designer app can even be used by people who are not technical. It also helped reduce time spent on custom development of features substituting it with ready-to-use components.

2. Developing a configurable tool

The solution is heavily configuration driven, so we hydrate the application with configurations for tasks and their properties fetched via REST API. Each pipeline is provided with a custom set of tasks it can be built of, and definitions of those tasks are provided by backend via a configuration file – that makes extending the application with new task types very easy.

Technology we used
Angular
Rappid
03

Project Results

Building AI models in a visual way 40-50% faster

Our client received a tool helping build AI models in a visual way 40-50% faster than before.

Delivering the project on time and budget

The app was delivered on time and on budget, living up to the expectations of the client both in terms of usage of the open-source platform and UX design. While still in beta, the app received positive feedback from key stakeholders.

Conclusion

We supplied AppOrchid with a fully-functional tool to help visually compose AI models. The Pipeline Composer we built helped save 40-50% of time spent on building AI models. In addition, making this application configurable, a new set of tasks within the tool can be easily added in the future.

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