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Apiphani Raises $25M in Series A Funding

Apiphani announced $25 million in Series A funding led by global software investor Insight Partners. 

The new capital will accelerate apiphani's growth, expanding its footprint in regulated industries such as energy, utilities, and telecommunications, where uptime, security, and performance are paramount.

"At a time when global integrators will assign hundreds of people to support a relatively vanilla, mid-sized enterprise IT environment — and still miss service levels — we're proving there's a better way," says Justin Folkers, Co-Founder and CEO of apiphani. "CIOs and CFOs know that downtime is unacceptable. It's our belief that a combination of expert engineers and AI-driven automation is the only model that scales with resilience."

At the core of apiphani's approach is luumen, its proprietary observability platform powered by the company's AI-based Deep Automation™ technology. Originally developed to support apiphani's managed services, it is now a product offering available directly to enterprises. Luumen gives engineers real-time visibility into environments and integrates seamlessly with IT toolchains across monitoring, alerting & escalation, ITSM, security, and backups. Designed as both an ecosystem and an engineer's workbench, luumen includes a library of preconfigured automations for common application and infrastructure tasks. It also offers extensive add-ons and integrations that let teams tailor the platform to their environment. Beyond observability, luumen enables enterprises to build and monitor bespoke automations that reduce manual work, eliminate ticket sprawl, and increase the value of existing IT investments.

"We didn't inherit the technical debt of legacy providers, so we built from the ground up with automation at the center," says Cynthia Borgman, apiphani Co-Founder and Chief Delivery Officer. "This isn't about labor arbitrage. It's about enabling exceptional talent to do meaningful work in an environment that is both rewarding and supportive."

"Enterprises have heard many promises over the past decade — resilience, better security, and workflow automations that save time and resources, to name a few. Today, outages and security breaches are even more common, and enterprises are investing millions in AI without seeing ROI. Apiphani has built the software product and managed services offering to meet those promises for their customers, and we're thrilled to be partnering with them in this next chapter," says Richard Matus, Principal at Insight Partners.

Founded in 2018, apiphani began with on-premise SAP support and has since expanded its model to meet the needs of modern cloud and hybrid infrastructures. With new funding, the company plans to scale its engineering and go-to-market teams in Boston and Lisbon, while continuing to expand capabilities and broaden industry reach. "Our thesis was bold," says Folkers. "But we've proven it works at scale. With the support of Insight Partners, we're ready to help enterprises realign their operations with business objectives in ways that simply weren't possible before." Moelis & Company served as exclusive financial advisor to apiphani.

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Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Apiphani Raises $25M in Series A Funding

Apiphani announced $25 million in Series A funding led by global software investor Insight Partners. 

The new capital will accelerate apiphani's growth, expanding its footprint in regulated industries such as energy, utilities, and telecommunications, where uptime, security, and performance are paramount.

"At a time when global integrators will assign hundreds of people to support a relatively vanilla, mid-sized enterprise IT environment — and still miss service levels — we're proving there's a better way," says Justin Folkers, Co-Founder and CEO of apiphani. "CIOs and CFOs know that downtime is unacceptable. It's our belief that a combination of expert engineers and AI-driven automation is the only model that scales with resilience."

At the core of apiphani's approach is luumen, its proprietary observability platform powered by the company's AI-based Deep Automation™ technology. Originally developed to support apiphani's managed services, it is now a product offering available directly to enterprises. Luumen gives engineers real-time visibility into environments and integrates seamlessly with IT toolchains across monitoring, alerting & escalation, ITSM, security, and backups. Designed as both an ecosystem and an engineer's workbench, luumen includes a library of preconfigured automations for common application and infrastructure tasks. It also offers extensive add-ons and integrations that let teams tailor the platform to their environment. Beyond observability, luumen enables enterprises to build and monitor bespoke automations that reduce manual work, eliminate ticket sprawl, and increase the value of existing IT investments.

"We didn't inherit the technical debt of legacy providers, so we built from the ground up with automation at the center," says Cynthia Borgman, apiphani Co-Founder and Chief Delivery Officer. "This isn't about labor arbitrage. It's about enabling exceptional talent to do meaningful work in an environment that is both rewarding and supportive."

"Enterprises have heard many promises over the past decade — resilience, better security, and workflow automations that save time and resources, to name a few. Today, outages and security breaches are even more common, and enterprises are investing millions in AI without seeing ROI. Apiphani has built the software product and managed services offering to meet those promises for their customers, and we're thrilled to be partnering with them in this next chapter," says Richard Matus, Principal at Insight Partners.

Founded in 2018, apiphani began with on-premise SAP support and has since expanded its model to meet the needs of modern cloud and hybrid infrastructures. With new funding, the company plans to scale its engineering and go-to-market teams in Boston and Lisbon, while continuing to expand capabilities and broaden industry reach. "Our thesis was bold," says Folkers. "But we've proven it works at scale. With the support of Insight Partners, we're ready to help enterprises realign their operations with business objectives in ways that simply weren't possible before." Moelis & Company served as exclusive financial advisor to apiphani.

The Latest

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...