Modern Relay
Modern Relay builds shared knowledge/context infrastructure for enterprise AI agents so people, software, and agents can coordinate work from the same organizational reality. Its stated aim is to preserve context, govern change, and keep customers in control of their data and models.
At a Glance
- Enterprise organizations adopting multiple AI agents
- Companies needing governed organizational knowledge and operational context
- Engineering, research, and operations teams
- Organizations requiring self-hosted, air-gapped, hybrid, or customer-owned infrastructure
- +1 more
AI Tools by Modern Relay
(1)
Omnigraph
Open Source Graph Database for Agents
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Latest News
Omnigraph Cloud announced as a private-preview hosted product with a waitlist.
Modern Relay published “Omnigraph: a graph database designed for agents,” detailing graph-as-code and agent coordination design principles.
Modern Relay’s $3 million seed round was reported, led by Point Nine with participation from Emerge, Amino Collective, Common Magic, Masia, and angel investors; the company’s CEO and CTO were identified as Ragnor Comerford and Aaron Goh.
Modern Relay came out of stealth, announced $3 million in funding, and released Omnigraph as its first open-source product.
Products & Services
An open-source, lakehouse/object-storage-native graph database and context engine for AI agents. It supports Git-style branching, typed ontology/schema, multimodal and hybrid retrieval, versioning, review, governed merges, and customer-owned deployment.
A hosted Omnigraph offering with a control plane for operations, enterprise SSO, isolation, policy, and declarative configuration; the website invites users to join a waitlist.
Market Position
Modern Relay positions itself as shared context and coordination infrastructure for enterprise AI, rather than an end-user agent application. Omnigraph differentiates through an open-source, object-storage-native graph engine that treats schema, context, policy, UI, and deployment as code and adds Git-style branches, review, typed merges, provenance, and governance so multiple agents can work without directly corrupting a canonical graph.
Leadership
Founders
Ragnor Comerford
Co-founder and CEO. He has publicly described the enterprise-agent coordination problem and the need for a shared foundation connecting agents across departments.
Aaron Goh
Co-founder and CTO. His public profile describes him as a product, ontologies, and knowledge-graphs founder; Crunchbase search results identify previous work as a Senior Software Engineer at Geminus.
Executive Team
Ragnor Comerford
Co-founder and CEO
Co-founder leading the company’s strategic vision and public articulation of enterprise AI coordination and shared context infrastructure.
Aaron Goh
Co-founder and CTO
Co-founder leading technical development; his public profile focuses on product, ontologies, and knowledge graphs, and he previously worked as a Senior Software Engineer at Geminus.
Founding Story
Modern Relay was started around the founders' view that enterprises would soon run many agents across departments, but that agents would create fragmentation when organizational context remained scattered across documents, chats, legacy databases, and unwritten knowledge. The initial vision was a shared, governed context layer where people and agents could preserve context, coordinate work, and merge approved changes into a canonical organizational graph, while running on infrastructure owned by the customer.
Business Model
Revenue Model
Modern Relay distributes an open-source self-hosted product and is developing a hosted Omnigraph Cloud offering with a control plane, enterprise SSO, isolation, policy, and operations. The Cloud site is in private preview/waitlist stage; public usage pricing was not shown.
Target Markets
- Enterprise organizations adopting multiple AI agents
- Companies needing governed organizational knowledge and operational context
- Engineering, research, and operations teams
- Organizations requiring self-hosted, air-gapped, hybrid, or customer-owned infrastructure
- Agent-native software and knowledge-graph developers
- Enterprise AI agents that need a shared organizational context
- Coordinating work between human teams and multiple agents
- Research drafting, code shipping, and day-to-day operational workflows
- Knowledge-graph and context assembly for agent applications
- Safe bulk enrichment and data changes using isolated branches and review
- Governed schema, policy, and ontology evolution