Enterprise AI Infrastructure Funding: From Hype to Systems Change
Enterprise AI infrastructure funding refers to investment flowing into the databases, workflow engines, routing layers, and context systems that allow AI agents to operate reliably inside complex business environments rather than as isolated pilots or prototypes. The latest rounds show money moving beyond model labs into the stack required to run enterprise AI agents in production. PhoenixAI’s USD 80 million (approx. RM368 million) Series B for its agentic AI database highlights demand for data layers designed for unpredictable, high-volume agent queries. Gradial’s USD 65 million (approx. RM299 million) Series C positions agentic marketing operations as a new “system of work” embedded in existing stacks. Conduct’s €51 million (about USD 60 million / approx. RM276 million) round, plus early funding for Niteshift and Zaro.ai, underline a shared thesis: enterprises want AI-ready systems and AI-native operations tooling that plug into their current software rather than replace it outright.
Agent-Native Data, Context, and Coding Layers
At the data and infrastructure layer, investors are betting that agents will become the primary consumers of enterprise systems. PhoenixAI’s agentic AI database unifies live and historical data so agents can fire thousands of unplanned queries and still get sub-second answers across hundreds of millions of rows. That is core enterprise AI infrastructure, not an add-on feature. Niteshift focuses on AI coding infrastructure: a model-agnostic routing layer that orchestrates between frontier and open-source models, charging per-minute infrastructure fees and acting like a cloud provider for coding agents rather than a replacement for developers. Zaro.ai targets the fragmentation problem inside enterprises, offering a single context layer where AI agents, data, and custom apps share state while routing tasks to cheaper or more capable models as needed. Together, these startups show investor appetite for neutral, agent-compatible foundations, not vertically locked tools.
Making Existing Enterprise Systems AI-Ready
A second cluster of funding targets the messy reality of legacy software. Conduct describes itself as an “AI OS” that makes enterprise systems legible so agents can understand and act on decades-old customisations in ERP, CRM, and operational platforms. Its €51 million (about USD 60 million / approx. RM276 million) Series A, backed by investors including SAP, fits a wider pattern: capital flowing into tools that help AI work with existing systems instead of replacing them. According to EU-Startups, Conduct sits alongside ERP transformation platforms, finance-focused agents, knowledge infrastructure, and governance layers for enterprise AI agents, all aimed at turning opaque software estates into AI-ready terrain. Zaro.ai follows the same logic at the workspace level, creating a unified enterprise context layer owned by the business rather than a single vendor. The signal to investors: AI deployment success depends on compatibility with what enterprises already run.
Agentic Operations and Vertical AI Workers
The surge is not limited to infrastructure; investors are also backing enterprise AI agents that run operations end to end. Gradial’s USD 65 million (approx. RM299 million) Series C is framed as a bet on agentic marketing operations, where AI agents author, QA, compliance-check, tag, and publish content while routing through approvals and existing tools. The company reports more than 10x ARR growth in 12 months and customer outcomes such as SLA turnaround moving from 10 days to same-day, positioning itself as a core marketing “system of work.” Beyond marketing, funding is flowing into AI workers for logistics and home care, such as Cargofy and Vali Health, which apply similar agent patterns to carrier operations and in-home patient support. The common theme is AI-native operations tooling that executes real workflows within regulatory, brand, and process constraints rather than producing isolated outputs.
M&A Signals: AI-Native Ops Tooling Becomes Strategic
Elastic’s acquisition of DeductiveAI for up to USD 85 million (approx. RM391 million) shows how fast AI-native operations tooling is turning into strategic M&A currency. DeductiveAI built AI agents that connect to code, logs, metrics, traces, and events, reasoning over a live knowledge graph to find root causes and resolve incidents. The company claimed up to 90% reductions in incident resolution time, and early customers such as DoorDash reportedly saved more than 1,000 engineering hours a year. Elastic is folding those agents into its observability platform, adding autonomous resolution on top of earlier AIOps buys and new agentic Kubernetes workflows. For investors in startups like Niteshift, Conduct, PhoenixAI, and Gradial, this deal is proof that the market now pays a premium for enterprise AI agents wired deeply into operations, not only for models or dashboards.
