**California**: Observo AI aims to streamline telemetry data management for organisations using AI, having secured $15 million in funding amid noteworthy revenue growth. Their agentic AI solution promises a 50% reduction in observability costs and automates routine tasks to tackle the complexities of unstructured data.
The emergence of generative AI has introduced significant challenges in managing the large volumes of telemetry data generated by machine learning and artificial intelligence models. Traditional observability and security systems are struggling to cope with the ensuing data overload, which includes logs, metrics, and traces. This has resulted in escalating operational costs and increasing complexity for security and engineering teams.
Nancy Wang, Product Builder at Mercor and former GM at AWS Data Protection, highlighted the pressing nature of these challenges in conversations with Chief Information Security Officers (CISOs) from both startups and large corporations. Speaking to Datanami, Wang stated, “For years, one challenge has come up again and again… observability and log data have become a top 5 cost driver. Security and engineering teams are feeling the pressure not just from soaring storage costs, but also from pipeline complexity and alert fatigue, making it harder to extract critical insights.”
In response to these pressing issues, Observo AI, a California-based startup, has developed a solution that leverages AI-native data pipelines to manage telemetry data flows more efficiently. The company has recently secured $15 million in seed funding, a financial boost that was led by Lightspeed Venture Partners and Felecis. This investment comes at a time when Observo AI is in high demand from enterprises looking to process substantial amounts of data daily, amid reports of the startup achieving a remarkable 600% revenue growth quarter-over-quarter since its launch in April 2024.
Observo AI’s platform has proven effective for its clients, such as Bill.com and Informatica, by reducing response times by over 40% and cutting observability costs by 50%. The startup aims to further its objectives to streamline data pipelines, enabling companies to handle AI-generated data more quickly, securely, and at a reduced expense.
The intricacies of handling unstructured data present a significant challenge for response systems; an influx of information can lead to increased costs and false positives, while filtered data can compromise accuracy and scalability. Observo AI contends that AI-driven optimisation can bridge this gap.
Using machine learning (ML) and large language models (LLMs), Observo AI claims to operate a platform that is 5-6 times more efficient than traditional tools. The company’s approach abandons rigid, rule-based methods in favour of a model that dynamically filters, routes, and adapts to noisy and unstructured data in real-time. Gurjeet Arora, co-founder and CEO of Observo AI, remarked, “Observo uses LLMs and Agentic AI to revolutionize observability and security. Our platform automates routine tasks, highlights key insights, and lets teams focus on preventing breaches and ensuring reliability.”
Observo AI’s technology transforms data pipelines into adaptive systems that can learn and improve autonomously. The startup asserts that its platform can automatically optimise data pipelines as new threats and anomalies are detected. Highlighting the significance of their innovative use of agentic AI, Guru Chahal, Partner at Lightspeed Venture Partners, mentioned, “Observo AI’s use of Agentic AI with streaming observability creates a powerful system that constantly learns and improves, making data pipelines efficient and intelligent. This is game-changing technology for enterprises grappling with the data challenges of observability and security infra.”
The founders of Observo AI, Gurjeet Arora and Ricky Arora, bring firsthand experience of the challenges faced within observability and security environments from their previous roles at Rubrik. They recognised that existing observability tools struggled to adapt rapidly to surging data volumes driven by AI, which proved to be a costly and unsustainable inefficiency. Their expertise has been pivotal in creating an AI-native architecture designed to enhance observability pipeline optimisation.
While AI-powered data observability is not a novel concept, the introduction of agentic AI tools has added an autonomous capability to such solutions, setting Observo apart from established competitors like Cribl, Splunk, and DataDog. However, as the market for these technologies evolves, it is anticipated that current and emerging competitors will integrate similar capabilities into their offerings. The future trajectory of the industry will not solely focus on adopting AI technology, but more critically on the effectiveness of its application in enhancing data pipelines.
The seamless integration of observability into data and AI processes will be essential for enterprises seeking to maximise the advantages of artificial intelligence. With its newfound capital, Observo AI plans to enhance its product’s AI capabilities further and accelerate its market presence.
Source: Noah Wire Services
