Perspica Networks Helps IT Teams Instantly Diagnose Problems In Virtualized Data Centers

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Silicon Valley based startup Perspica Networks has recently announced a $4 Million in Series A funding. The company has build a strong team of experts from data science, networking and enterprise software industry and in a short time released the first of its kind product to automatically detect performance anomalies and instantly point to the source of network problems. Below is our interview with Prem Talreja from Perspica Networks team:

Q: What is Perspica Networks startup story?

A: We have seen from the other side, as network administrators that in a modern virtualized data center, there are performance issues that are very tedious to resolve. The “aha” moment was that there is no shortage of log data, but it is humanly impossible to correlate the information and isolate the root cause and if can use machine learning and big data analytics techniques can fill this gap with automation. As we spoke with early customers we were very encouraged by the validation.

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Q: What are main advanteges of Perspica Incidents Navigator?

A: We can dramatically reduce the Mean Time to Resolution (MTTR) of performance issues in a dynamic data center. We increase staff productivity in an environment where IT Operations exec are asked to do more with less. We provide a way for IT operations to perform postmortem on problems and see what went wrong and how to avoid the problem in the future. In the end we save companies from huge costs they would incur in SLA violations without our system.

– Perspica Networks product launch party –

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Q: Who is your ideal customer and why?

A: Enterprise with private and hybrid data centers with mission critical applications with dynamic workloads. That would include fortune 500 companies and medium businesses with critical applications.

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Q: How does Perspica differ from other IT operations analytics companies?

A: A In the IT Operations Analytics we are the first to offer all the way from learning, anomaly correlation, root cause identification and recommendations on how to resolve the problem.