London-based startup Trace has secured $3 million in seed funding to tackle one of enterprise AI’s biggest bottlenecks: context.
The round includes backing from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital, WeFunder, and angel investors Benjamin Bryant and Kevin Moore. The company was part of Y Combinator’s 2025 summer cohort.
The Enterprise AI Agent Problem
Despite rapid advances in large language models and agentic systems, enterprise adoption has lagged. While AI agents can perform tasks autonomously, deploying them effectively inside complex corporate environments remains difficult.
Trace believes the issue is not model capability but missing organizational context.
AI labs have built highly capable digital workers. What enterprises lack, according to Trace’s leadership, is the orchestration layer that understands where and how those agents should operate inside a company’s workflows.
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Building The Context Layer
Trace develops workflow orchestration infrastructure designed to map how organizations actually function.
The system connects to existing workplace tools such as email, Slack, Airtable and other operational platforms. From there, it builds a structured knowledge graph representing processes, relationships, and data flows across the company.
Once that context layer is established, users can issue high-level instructions such as planning a new product microsite or preparing a multi-year sales strategy.
Trace then generates a structured workflow. Tasks are automatically distributed between AI agents and human team members. When an AI agent is triggered, it receives precise contextual data relevant to its specific sub-task.
The objective is to remove the friction involved in onboarding and coordinating AI agents inside real-world enterprise systems.
Competing In A Crowded Market
The market for enterprise AI agents is becoming increasingly competitive. Large AI providers are introducing agent frameworks tailored to departmental use cases, while established productivity platforms are embedding native AI capabilities into their own ecosystems.
Trace is differentiating itself through its knowledge-graph-driven architecture. Instead of building isolated agents, the company focuses on context engineering — embedding structured understanding of organizational workflows directly into the orchestration layer.
According to the founding team, the shift is moving from prompt engineering toward context engineering. In this view, the infrastructure that provides the most relevant context at the right time will become foundational to AI-native enterprises.
Trace is positioning itself as that infrastructure layer.
