Every company has a graph. Almost none of them can use it.
It should not cost millions of dollars and a second database to understand how your company's data is connected.
Evokoa turns existing databases into graph-queryable infrastructure without moving the data. We are building the connective layer that makes company data legible to humans, software, and agents by default.
Every company is already a graph.
Customers connect to accounts. Accounts connect to invoices. Invoices connect to tickets. Tickets connect to employees, approvals, contracts, transactions, devices, and risk signals. The most valuable relationships in a company already exist. Almost nobody can query them.
The current answer is insane.
Today, if you want graph queries, you usually have to move your data into a graph database. That means duplicating your system of record, building sync pipelines, managing another access-control layer, paying for another database, and hoping the graph never drifts from reality.
We got pulled here.
Our first product was an AI workspace, basically an AI middle manager that understood how work moved through a company. Under the hood, we had built a graph engine fast enough for agents to reason over operational data. Customers liked the workspace. But they wanted the engine.
Agents are first-class citizens.
The best AI-native companies are making their whole company queryable. Every ticket, customer interaction, invoice, approval, and internal artifact becomes legible to agents that can monitor what is happening, compare it to what should be happening, and generate the next action. Agents need connected context, not another dashboard.
Graph traversal should be an index.
Evokoa separates the relationship graph from the payload. We keep a lightweight in-memory map of how records are connected, traverse that map first, and only hydrate the records needed from the source database. Postgres and other operational databases stay the system of record. Evokoa becomes the traversal layer on top.
The whole company should be traversable.
Graph queries should not be a luxury feature reserved for a tiny subset of data. If traversal requires duplicated storage and huge memory overhead, teams only graph the few things they can afford to graph. If traversal is a lightweight index over existing systems, the whole company becomes traversable by default.
What Actually Matters
The hard part is not drawing nodes and edges.
The hard part is connecting to messy company systems, mapping relationships between records, keeping them synced, enforcing access rules, and making traversal cheap enough to run everywhere.
On the Panama Papers dataset, Evokoa used about 34x less RAM than Neo4j while preserving traversal speed.
Graph queries where the data already lives. Tiny memory footprint. No second source of truth. Infrastructure for the closed-loop company.
Get Started
Give your existing data a reasoning layer.
Keep your existing database. Add a fast relationship cache for apps and agents.
Open Source Dropping Soon