(PRWEB) May 22, 2017
SignifAI, the leading provider of predictive machine intelligence tools for TechOps teams, today announced the general availability of its cloud-based platform, delivering fast and accurate answers to the most important challenges that technical operations engineers face on a daily basis.
SignifAI’s platform reduces alert noise generated by existing monitoring tools, analyzes monitoring data in real-time, and applies a logical framework that is informed by human experts, leveraging a wide variety of computational techniques stemming from the fields of artificial intelligence. The end result is TechOps teams getting quick access to accurate answers and insights that help them prevent issues before they affect uptime.
“Uptime is the lifeblood of any company and industry. If your systems are down, your business and customers are directly affected,” said JP Marcos, CEO of SignifAI. “We make it simple and efficient for businesses to minimize and prevent downtime with automatic and relevant answers and insights.”
SignifAI’s engineering team has an extensive background in technical operations and DevOps, having managed complex systems for years. “We were frustrated by the capabilities of existing monitoring tools,” said Guy Fighel, CTO and Co-Founder of SignfiAI. “So, we decided to build the technology we wished we had. This technology became the basis of SignifAI’s machine intelligence platform.”
SignifAI finds correlations in real-time, among very large volumes of log, event and metrics monitoring data. These correlations are driven by algorithms plus a TechOps team’s collective expertise. This means teams can get to root causes and solutions quickly, regardless of the seniority of the engineers currently on shift.
“I think there is enormous upside to applying machine learning techniques to our system monitoring events. For a human, correlating different events across components and time is like a particularly tedious game of memory.” said Chris Haag, Director of Engineering at Agari. “We get good at it eventually but a purpose built Machine Learning system should be able to suggest correlations out of the box. With training, I suspect it could out perform humans. The best part will be freeing our developers to focus on solutions rather than reacting to problems.”
Because of alert noise, poor correlation capabilities and the uneven skill sets of the engineers who may happen to be on call, many operations teams struggle with making the transition from a team that responds to alarms, to one that proactively resolves issues.
“Uptime is obviously mission critical to our organization,” said Lior Gavish, VP at Barracuda Networks. “Machine intelligence gives us the ability to avoid downtime by pointing to potential issues that could affect our customers ahead of time. This is a big step towards working proactively vs reactively to fix problems.”
SignifAI believes that when TechOps teams deliver more uptime, they can find the time to work on more complex problems that require creative solutions -- precisely the things that machines can’t do -- and add a lot more value to the company.
SignifAI was founded by a team of TechOps engineers who faced the challenge of delivering uptime at scale for years. Even with the best monitoring tools, delivering on service level agreements was a daily struggle. Frustrated by the available solutions, they decided to take a new approach to TechOps. That new approach was machine intelligence. SignifAI is a cloud-based machine intelligence platform that makes use of a TechOps team’s operational expertise and existing monitoring tools to automate the identification, prevention and remediation of production problems. SignifAI is backed by Highland Capital Partners, Bloomberg Beta, Correlation Ventures and headquartered in Sunnyvale, CA, with offices in Tel Aviv. For more information, visit: https://signifai.io/