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Three early-stage startups, each funded in 2026, are each solving a different piece of the AI infrastructure problem that McKinsey’s August 2026 survey made unavoidable: 44% of organizations say AI is scaling enterprise-wide, yet only 37% report any positive EBIT contribution. Capsa AI raised an $18 million Series A to build a unified knowledge layer for private capital funds, growing ARR 14x year-on-year. Palette raised €3 million to give non-engineers a model-neutral workspace for running AI agents against company files. Beelzebub raised €3 million for AI-powered deception-based AI-native security infrastructure. Read together, they sketch the layer below the model.
What this means for your business
The McKinsey gap is a production problem, not a model problem. Your organization almost certainly has access to capable foundation models. What it probably lacks is durable institutional context, a workflow layer that survives the next model upgrade, and a security posture that operates at the same speed as the attacks. That gap is where productivity gains stay personal and never hit the income statement. The three funding rounds here are each betting the infrastructure layer is where enterprise value actually accrues.
Palette’s framing deserves serious attention from any CIO currently standardizing on a specific model vendor. The team’s core design choice, swappable models against durable company context, is a direct counter to the lock-in dynamic that every major foundation model provider is trying to create. The organizations that build their workflows on top of a specific model’s proprietary features will face the same renegotiation cycle that plagued ERP migrations. The ones that treat the model as a commodity and invest in their own context layer will have a structurally stronger negotiating position.
Beelzebub’s honeypot approach, fake servers and APIs designed to expose attacker movement rather than simply block it, reflects a real shift in security posture that CIOs should be tracking. Perimeter defense built for human-speed attacks doesn’t hold when the attacker is also running agents. The signal worth watching: whether enterprise security budgets start flowing toward active deception infrastructure at the same rate they flowed toward endpoint detection five years ago. If they do, vendors like Beelzebub will look prescient. If not, this remains a niche play.
Concept deep-dive: Durable context layer
A durable context layer is an organization’s accumulated knowledge, decisions, documents, and workflows, stored in a form that AI agents can query regardless of which underlying model is running. It exists because foundation models have no memory of your business by default; every new model upgrade resets the clock. Think of it as the difference between a contractor who reads the project brief each morning and one who has worked the account for years. Capsa and Palette are both building versions of this for their respective domains. The business connection is compounding returns: the longer the layer accumulates, the harder it is for a competitor to replicate.
Based on reporting from Beyond the Copilot: How AI-Native Infrastructure Is Rebuilding the Enterprise, originally published 2026-09-23 08:21:00.
