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The Elipsa Difference: Taking AI From Promise to Reality



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In the world of industrial IoT the difference between success and failure is not just measured by an operator's competence as an individual, but how their assets perform. We find that most organizations have an assumption of near 100% uptime and reliability of their assets and near-perfect output quality. This leaves operators in the tough position of delivering on what seems like unattainable expectations.


At Elipsa we are about empowering operators by providing visibility into the future. We identify potential problems and defects before they occur, ensuring efficient operation through automated predictive solutions.


The promise of AI has floated around the industry for years but the complexities of existing systems and a lack of internal expertise have prevented organizations from turning that promise into reality.


Elipsa's automated AI solutions help operators harness the power of their machine’s data in order to drive operational success.


The Elipsa Difference


So, how does Elipsa differ from other AI solutions that fail to turn that promise into reality? The answer to that is that we make AI approachable.


Elipsa's Approachable AI engine was built on the foundation of enabling operators to implement and scale AI throughout their organization no matter their technical expertise.


The three pillars to this approach are: Useability, Explainability, and Accessibility


Useability


We simplify AI to make it easier to use and implement. The mechanics of machine learning and artificial intelligence is extremely complex. However, the same can be true of any machine on the factory floor or of the devices and machines that you produce every day. However, just because the inner working of the machine or of AI is complex does not mean operating/using it needs to be.


Our no-code solution is focused on clicks not code, enabling operational users to directly apply their domain expertise towards building predictive models. We empower individuals within an organization to integrate AI into their processes with as little as 3 steps.


By automating the data wrangling, model selection, and model training, we take away the complexity and leave a solution that can finally turn the promise of AI into reality for all.


Explainability


Once an AI model is built, you need to understand how effective it is at predicting the answer to your problem. Oftentimes, the metrics used to explain model effectiveness require a Ph.D. in Mathematics to truly grasp. For those of us that lack that level of education, we need the results presented in a much more digestible format.


Making AI more approachable requires a different form of explainability to ensure a higher level of understandability. Elipsa simplifies model efficacy into explaining what the model got right, what it got wrong, and what data from your machines is driving that decision.


Furthermore, our proprietary AI creates models in such a way that you get instant results as to the efficacy of the model to determine whether it meets the requirements of your particular use case.


By making AI more explainable, you get an easy-to-understand glimpse into the results of what is typically considered the “black box” of AI.



No-Code Predictive Analytics for IoT


Accessibility


So at this point, we’ve automated the creation of a predictive model on your data, we presented the results of the model in a way that is actually understandable, and everything looks ready to go.


Ironically, from a technical perspective, the hard part is done. However, the biggest hiccup that organizations face in implementing AI is actually putting that model into production.


A predictive model that can help to eliminate production downtime, reduce quality concerns, and increase machine performance sounds great. However, this model is worthless if it is not connected to your machines monitoring them in production or in the field.


As a result, the third pillar to Approachable AI is centered around accessibility. In other words, we automate the deployment of these AI models to enable organizations to access key insights in production. Our platform is API-driven at the core, enabling us to integrate into existing infrastructure and software solutions in the cloud and at the edge.


Conclusion


With just a few simple clicks, you can connect your machine data, build a predictive model, and deploy it to production. Approachable AI finally takes machine learning from promise to reality.


We are empowering IoT with plug-and-play predictive solutions that solve the underlying problems of production downtime, machine performance, and poor quality.