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Our Values

As an organization, we value meritocracy with following guiding principles

Self-expression

Be yourself. Everybody else is already taken.

Scientific Thinking

We emphasize scientific thinking over analogy-based thinking.

Accountability

We match authority and responsibility.

Process-orientation

We aim to install a robust system that wins consistently.

Empathy

We work to make changes that we like to see in the world.

Transparency

We aspire to be in harmony with what we think, what we say, and what we do.

Sustainability

Sustainability means striking the rightbalance between today’s urgency and tomorrow’s need.

You are welcome to join the force!

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In the absence of an appropriate pattern recognition algorithm, granular and real-time data is just computing burden. To fully utilize it, it is important to be supported by an efficient pattern-recognition algorithm.
Enterprise-grade servers supporting AI workloads are at least few orders of magnitude more expensive than the traditional volume servers. The equipment failure cost is too high to be ignored.
Typical lifetime of an IT equipment is 6-18 months, while that for a facilities equipment such as a Liebert cooling systems 15-20 years. The deployment times are also very different. Furthermore, the typical time-scale for data center facilities operations is in order of few minutes, while that for IT systems is few minutes. Therefore, matching data center facilities to its IT demand is very difficult.
Following Moore law’s, the number of transistors in electronic circuit is rapidly increasing at 1.3x annual rate. Moreover, virtualization of computing resources made data center workload rapidly non-uniform. This two factors lead to a complex data center operation.
Data centers are increasingly supporting application ecosystem such as video streaming, high-frequency merchandise, and real-time image recognition. This new generation applications require fast and high-throughput computation.
A typical data center hosts a multitude of IT, power, and cooling systems with varied response characteristics. This makes data center thermal profile non-uniform and complex. In addition to that, turbulent air cooling adds to the data center thermal profile to an extent that it become non-linear. Therefore, it is literally impossible to build a traditional formula-based mathematical modeling for data center thermal evolution. It necessitates a data-driven real-time framework for data center thermal modeling and prediction.
As much as 50% of data center downtimes happen due to thermal issues. AdeptDC’s data-informed predictive maintenance and automated cooling optimization reduces the risks of these downtimes.
Most of the hardware failures occur due to temperature, voltage, power, or humidity related issues. AdetpDC’s solution reduces the hardware mortality rates with its granular and real-time monitoring and end-to-end feedback control solution.
With data center cooling demand being difficult to predict, data center managers are forced to over-compensate their cooling capacity. AdeptDC’s end-to-end automated cooling optimization removes guessworks from the cooling controls, making it more efficient.
Although most of the data center managers are trained IT professionals, a significant part of their work hours are spent on calibrating cooling system and fixing thermal issues. AdeptDC’s predictive maintenance application intends to save time for IT professionals so that they can focus more on their core jobs.
Data center maintenance could be an intractable problem with unpredictable nature of data center operations and complex thermal response patterns. AdeptDC aims to removes this uncertainty and provides data center managers with much needed peace of mind.