Synapse

Inspiration

Enterprise knowledge is scattered across documents, emails, databases, and business tools. AI agents can retrieve information, but often lack the connected context, persistent memory, and traceability needed to make reliable decisions. We built Synapse to bridge this gap by turning fragmented enterprise data into a living knowledge graph that AI systems can reason over.

What it does

Synapse is an Enterprise Knowledge Intelligence Platform that connects enterprise entities, relationships, decisions, and events into a unified knowledge graph. It aims to preserve provenance, track how facts evolve, and enable multi-hop retrieval across connected information. Instead of returning isolated snippets, Synapse helps AI agents understand how information relates, retrieve relevant context across sessions, and ground their answers in evidence.

How we built it

We designed Synapse around a modular ingestion and knowledge processing pipeline. Data from heterogeneous sources is normalized into a canonical representation, processed to identify entities and relationships, and organized into a graph for retrieval and traversal. The architecture separates ingestion, knowledge extraction, graph operations, evidence tracking, and API access. We are building toward a shared context layer that can power AgentSuite, our complementary platform for coordinating specialized AI agents.

Challenges we ran into

One of our biggest challenges was designing a system that could handle heterogeneous data without losing its meaning or provenance. Extracting relationships is not enough; the system must also preserve source evidence and account for facts that change over time. Another challenge was deciding how to separate persistent enterprise knowledge from an agent's temporary execution state, while keeping the architecture modular enough to support future integrations.

Accomplishments that we're proud of

We are proud of designing Synapse as more than a conventional search layer. Its core vision combines connected knowledge, persistent context, provenance, and temporal awareness into a foundation for enterprise AI. We also designed it to integrate with AgentSuite, extending the graph's value from information retrieval to coordinated agent workflows and evidence-based validation.

What we learned

We learned that the real value of a knowledge graph is not simply storing entities and relationships. It comes from making those relationships influence retrieval, reasoning, and decisions. We also learned that persistent memory must be grounded in structured, retrievable knowledge, and that trustworthy AI requires evidence and outcome verification rather than relying solely on an agent's response.

What's next for Synapse

Our next steps are to expand data connectors, strengthen entity resolution and temporal knowledge management, improve graph-powered retrieval, and make source-level evidence more accessible. We also aim to integrate Synapse more deeply with AgentSuite so multiple agents can share enterprise context, coordinate their work, and verify outcomes. Our long-term goal is to build a scalable knowledge foundation for enterprise AI that can understand not only what the organization knows, but how that knowledge connects and changes over time.

Built With

• ai-agents
• data-engineering
• docker
• entity-resolution
• falkordb
• fastapi
• graphiti
• graphrag
• knowledge-graphs
• llms
• multi-agent
• natural-language-processing
• neo4j
• postgresql
• python
• rag
• react