Kosmos Raises $5M to Help Enterprises Eliminate the Hidden Cost of IT Incident Investigations

Chicago-based startup Kosmos has raised $5 million in seed funding led by Norwest as it launches an AI-native operational intelligence platform designed to address one of the most persistent and expensive challenges in enterprise IT: the time spent investigating incidents.

While organizations have invested heavily in observability, monitoring, and IT service management tools, support and engineering teams still spend countless hours manually tracing the root cause of outages and customer-impacting issues across fragmented systems. Kosmos aims to reduce what it calls “investigation costs”—the operational burden created when engineers, support leaders, and incident response teams are forced to manually connect information spread across ticketing systems, source code repositories, customer support platforms, and observability tools.

The Growing Problem of Investigation Costs

Over the past decade, enterprises have adopted a growing collection of tools to monitor infrastructure and manage software operations. Platforms such as Jira, ServiceNow, GitHub, Salesforce, Datadog, Grafana, and Splunk each provide valuable insights into different parts of the technology stack. However, when incidents occur, critical context often remains siloed across these systems.

As modern software architectures become increasingly distributed, the challenge of correlating information across multiple platforms has intensified. Teams may have all the necessary data available, but finding and connecting it quickly remains difficult. The result is longer resolution times, recurring incidents, and engineering resources being diverted away from product development and innovation.

For many organizations, the most experienced engineers become the default investigators whenever a major customer issue arises. Those engineers are often pulled away from strategic work to reconstruct timelines, review code changes, analyze support tickets, and determine what actually caused an incident. The hidden cost of these investigations extends well beyond downtime itself.

An AI-Native Approach to Operational Intelligence

Kosmos is positioning itself as a layer that sits across existing enterprise systems rather than replacing them. The platform connects data from GitHub, Jira, Salesforce, ServiceNow, Datadog, Grafana, Splunk, and other operational tools to create a unified view of incidents and customer escalations.

According to the company, its platform automatically correlates customer cases, code changes, service incidents, and infrastructure signals to surface likely root causes. Rather than relying solely on autonomous AI decisions, Kosmos employs a human-in-the-loop approach where machine-generated correlations are reviewed and validated by users before becoming part of the platform’s knowledge base.

This creates a continuous feedback loop that improves the system’s ability to identify patterns over time while maintaining transparency and trust. Instead of generating more alerts, the goal is to provide teams with the context needed to understand why problems occurred in the first place.

Built From Firsthand Experience

Founder and CEO Sanjay Gidwani built the company around a problem he encountered repeatedly during more than two decades working in enterprise delivery operations and within the Salesforce ecosystem.

Throughout his career, Gidwani observed the same pattern: when a major customer issue emerged, organizations would mobilize their most experienced technical personnel to investigate. Yet those experts often spent days gathering information from disconnected systems before arriving at a root cause.

That experience shaped Kosmos’ core thesis: one of the biggest operational inefficiencies in enterprise IT occurs before remediation even begins. If organizations can identify the source of a problem faster, they can resolve incidents more quickly and reduce the likelihood of similar issues recurring.

Looking Ahead

The launch of Kosmos reflects a broader shift occurring across enterprise technology. As organizations adopt increasingly complex cloud environments, microservices architectures, and AI-powered applications, operational data continues to grow while remaining scattered across dozens of platforms.

The next generation of operational intelligence tools aims to bridge those gaps by automatically connecting signals, identifying patterns, and preserving institutional knowledge that would otherwise remain locked inside individual teams. Beyond faster incident resolution, these systems could help organizations reduce engineering toil, prevent recurring issues, and allow technical talent to focus more time on building products rather than investigating failures.

As enterprise environments become more complex, the ability to transform fragmented operational data into actionable intelligence may become just as important as monitoring the systems themselves. Kosmos is betting that reducing investigation costs will be a critical part of that future.