Startup Name: Sycamore

Industry: AI

Region: United States

Amount (USD): $65 million

The problem it is solving: Enterprises are increasingly interested in deploying autonomous AI agents, but face major challenges around security, governance, scalability, and operational control. Existing solutions often layer agent capabilities onto legacy systems, creating risks and inefficiencies. Sycamore addresses this by building a dedicated AI agent operating system that acts as a foundational control layer, enabling enterprises to securely deploy, orchestrate, and manage AI agents with full visibility, traceability, and trust.

What the Funds Will Be Spent On: Expanding engineering and applied AI teams. Advancing research in trust architectures, memory systems, and multi-agent coordination. Scaling enterprise deployments and customer adoption. Enhancing platform capabilities for governance, security, and lifecycle management of AI agents.

Startup Name: Valinor

Industry: Blockchain

Region: United States

Amount (USD): $25 million

The problem it is solving: Traditional private credit processes are often manual, slow, and reliant on spreadsheets for verification and collaboration, which creates inefficiencies and limits scalability. Valinor is solving this by bringing private credit operations onto the blockchain, using smart contracts to automate fund routing and execution based on predefined conditions. This improves efficiency, transparency, and speed in lending workflows, particularly for fintech and crypto-native companies.

What the Funds Will Be Spent On: Scaling loan volumes and expanding lending operations. Growing its customer base across the fintech and crypto sectors. Expanding its team. Further developing its blockchain-based private credit platform.

Startup Name: Neon

Industry: Data Economy

Region: United States

Amount (USD): $25 million

The problem it is solving: Individuals generate vast amounts of personal data through conversations and digital interactions, yet they rarely benefit financially from how this data is used—especially in training AI models. At the same time, AI companies face challenges sourcing high-quality, diverse conversational data. Neon addresses this by creating a platform where users can voluntarily monetise their data (audio and video interactions), while providing AI companies with ethically sourced, consent-based training data.

What the Funds Will Be Spent On: Expanding its consumer platform and mobile app capabilities. Scaling data collection across audio and video formats. Increasing partnerships with AI labs for data licensing. Growing its user base and improving payout systems. Enhancing privacy, consent, and data anonymisation systems.