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We love SaaS

We got introduced to Cloud Computing in year 2011. This was the period when “Giant Players” were entering into the market. So, we decided to get some handholding from technology evangelists.

Our core strength was in development and now additionally, we also have a professionally managed platform or also known as run-time environments for hosting our binaries. Finally, the cloud hosting. It can be tiny or massive, we had the option to build it the way we want it to be.

At this point, the stage is all set for SaaS Product and the story continues.

Let’s connect

Can’t we aim OEE (Overall Equipment Effectiveness) through analytics on connected devices providing real-time actionable insights? Isn’t that becoming the need of enterprises from their plethora of devices.

Yes, our product offers that out of the box. It is a matter of pride when our enterprise customers achieve 99.03% OEE on their distributed systems at scale.

Field driven AI

Emerging markets are today blessed with variety of products, i.e. both large and small players offer products. Same is the story with environment (with power fluctuations, earthing issues), internet connectivity, reliability of product used & installation etc.

So, basically the neural nets that we cook, need to be fed with tons of data from field. We need quantity and variety of data, so we decided to roll up our sleeves and get those data from the field.

AI for everyone

Our vison is to cater to a huge customer base with our product, hence we offer it at a very affordable package. Once the neural nets demand high end hardware, there will be more to the story. So the story does not end here 😊.

Just to let you know, before we go. We have done something unique, fine-tuned our platform to run edge AI in $80 hardware.

Massive centralization & decentralization

We are proud that our product does both and that exactly is the need of the day.

When product offers Overall Equipment Effectiveness at fingertips, the sensor data need to reach cloud servers for real-time analytics & ML. Same is the story when backup is offered in the cloud.

Now when it comes to Vision Neural Nets, demanding tons of video frames reaching cloud servers is a wrong positioning. Doing this would invite all sorts of different challenges including band-width challenges or sometimes, just becoming a headache to SDWAN. So, we do de-centralization here by making the AI work at edge, that too in $80 hardware.

By the way, does that mean, after deployment AI model will get outdated? Answer is NO. Our Neural Net Algorithm could mark outliers & trigger re-training and updated Neural Net reaches edge. Thus, edge remains updated always.

This competence of our algorithm, in turn fosters our relationships with customers that lasts for a long time.