Enterprise AI Funding Is Shifting Toward Infrastructure, Not Gimmicks
Enterprise AI funding now concentrates on infrastructure layers that cut delivery time and operational risk rather than on flashy front-end tools or lab experiments. Investors are backing AI systems that sit close to core workflows—observability platforms, ERP automation engines, and cognitive monitoring software that turns human attention and fatigue into measurable data—because these products promise direct savings on labor, fewer outages, and faster transformation projects. The latest funding rounds for SOMAREALITY, Qorelo, and Tsuga add up to more than €36 million and display a clear pattern: money flows to AI that removes bottlenecks from high-stakes environments. From cockpit fatigue to multi-year SAP migrations and AI-native observability, each startup targets a pain point where manual work does not scale, and where delays or errors are expensive. The result is a snapshot of which enterprise AI infrastructure bets investors think will pay off first.
Cognitive Monitoring: SOMAREALITY Turns Eye Movements into Operational Insight
SOMAREALITY secured over €3 million in an oversubscribed Series A to expand its real-time cognitive monitoring software. Its eye tracking algorithms infer cognitive load, attention, perception, fatigue, and performance from patterns such as saccades and fixations, and the platform is hardware-agnostic and explainable by design. After launching its Cognitive Load product, the company has added features for conscious perception, attention measurement, fatigue onset and is working toward broader cognitive performance and health metrics. It already reports over €2 million in B2B revenue since its first biomarker reached the market in 2024, with growing demand in aviation, healthcare, professional sports, and research. SOMAREALITY plans to use the new capital to deepen enterprise segments, support longitudinal studies, and extend into consumer use cases. This shows how cognitive monitoring software is moving from niche research into mainstream operational tooling, even if ticket sizes remain modest compared with heavier infrastructure bets.
AI-Powered ERP Migration: Qorelo Targets the 2027 SAP Bottleneck
Qorelo has raised €3 million in seed funding to build an AI-powered ERP migration engine focused on SAP S/4HANA projects. Founded in late 2025, the company targets a looming deadline: SAP customers face a 2027 cutover, with migrations typically taking 18–36 months. According to HPI Ventures, more than 60% of these migrations run over budget or schedule and only 8% are completed on time. Qorelo’s intelligence layer automates repetitive functional workstreams inside these large ERP transformations, and the startup claims it can reduce delivery timelines by 45%. The product serves both consultancies that need to scale without adding headcount and enterprises that want less reliance on external specialists, and it already counts a leading automotive company as a live customer. This is a clear example of AI-powered ERP migration attracting early but relatively small enterprise AI funding, tied directly to a fixed global deadline and a large services market.
Tsuga’s €30M Round Shows Observability Platforms Command Bigger Checks
While SOMAREALITY and Qorelo raised a few million each, Tsuga’s €30 million Series A underlines how observability platform funding can reach an entirely different scale. Tsuga deploys inside a customer’s own cloud—across major hyperscalers and regional sovereign environments—so telemetry never leaves the account, there is no extra infrastructure tax or duplication, and AI runs on complete, unsampled data. The company argues that legacy observability platforms assumed manageable data volumes, but AI agents now generate interaction and deployment telemetry at levels those designs cannot handle. Six months after exiting stealth, Tsuga reports several millions in revenue, six-figure average contract values, and customers ranging from frontier model labs to media and software firms. With a single price per gigabyte and optimization that can lower costs over time, investors see Tsuga as core enterprise AI infrastructure. Large checks are following AI-native observability and agent orchestration rather than narrower point solutions.
What This Funding Split Reveals About Enterprise AI Infrastructure ROI
Across these three rounds, the funding split—€3 million for cognitive monitoring, €3 million for AI-powered ERP migration, and €30 million for AI-native observability—shows where investors expect the strongest returns. All three startups attack operational pain: SOMAREALITY reduces safety risk and performance drag from cognitive overload, Qorelo compresses multi-year ERP delivery and services costs, and Tsuga tackles exploding observability spend and governance gaps as AI agents multiply. Yet the largest capital pool flows to the platform that underpins many AI workloads, not to single-process automation tools. This suggests future enterprise AI funding will favor products that combine broad applicability, clear cost savings, and control of sensitive data within the customer’s own environment. Cognitive monitoring software and ERP-specific automation will likely keep growing but may rely on stepwise, use-case-led adoption, while observability and agent infrastructure emerge as the central nervous system investors want to own.
