HEAL Software, the pioneer of preventive AIOps and APM software has added several new customers during the past quarter. The company also announced updates to its AI/ML (artificial intelligence/machine learning) algorithms to cater better to the post-pandemic enterprise demands. It also expanded its integration pool with plug-and-play connectors for Azure, AWS, SAP and enhanced Kubernetes support to cater to an increasingly complex ITOps landscape with multiple monitoring tools.
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Girish Muckai, HEAL’s chief sales and marketing officer, is optimistic about the company’s outlook for the Indian market. “HEAL’s unparalleled expertise in the Indian market and strong value proposition is what’s driving our growth especially in the BFSI, telecom, tech enterprise, and eCommerce verticals. The fact that 7 out of 10 top banking institutions trust us with their monitoring and AIOps needs is a testament of the value HEAL brings to our customers on their digital transformation journey,” he said.
Speaking about the new product updates Atri Mandal, Head AI and ML said, “We are continually updating our AI/ML models to better cater to the dynamic nature of the APM ecosystem. The recent update specifically helps SREs and ITOps leaders better forecast capacity and plan resource allocation months in advance, be it a major e-commerce sale or a banking event. Our brand-new ML-based multivariate capacity forecasting model is equipped with superior extrapolation capability, which minimizes projection errors by accurately modeling non-linear behaviour in time series data.”
Over the past few releases, HEAL has been continually raising the bar in terms of its AI and ML features. Most notably, HEAL uses an innovative preventive healing strategy to predict, analyze and prevent problems or outages much before they actually happen. This is a paradigm shift from a reactive “break-fix” model used by existing AIOps systems to a preventive AIOps model. Another notable AI/ML feature of HEAL is its advanced anomaly detection algorithm, which correlates workload and behaviour metrics for more accurate predictions. It can also apply the knowledge learned from similar time-series datasets to predict anomalies, in the absence of sufficient training data. This enables HEAL to start providing ML insights as soon as data ingestion begins, without waiting for training data to accumulate.
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