Your data has relationships. We make them visible.
Making the World’s
Data Ai Native.
Evokoa is a lab that is building virtual graph technology that allows AI to understand and reason over petabytes of data (in place).
The Problem
Software ate the world. It still can’t read.
The Bottleneck
AI is only as good as the data it can reach.
The best model in the world is useless if it can't see your data. Today, making enterprise data available to AI means months of pipeline work, a second database, an ETL team, and a prayer that nothing drifts. Most companies never start.
The Primitive
The graph is the missing primitive.
Customers connect to tickets. Tickets connect to invoices. Invoices connect to approvals. These relationships already exist in your operational databases. The problem is that no system treats them as first-class objects.
The Approach
We keep the map, not the territory.
Evokoa builds a lightweight in-memory graph of how your records are connected. We don't copy your data. We traverse the relationship map, figure out which rows matter, and hydrate them from your existing source of truth.
The Relationship Layer
Not another database. A layer that makes your data traversable.
The layer that sits between your existing systems and the AI that needs to reason over them. A single API that makes your entire company traversable. By humans, by code, by agents. No data movement. No second source of truth.
Source systems
Rows stay in Postgres
Evokoa
Maps IDs and relationshipsApp or agent
Asks about connected data
Matching IDs
Only relevant rows are fetched
Why Rust
AI-native means traversable by default.
When an agent can traverse every relationship in a company, from customer to contract to SLA to violation to responsible team, it stops being a chatbot and starts being infrastructure. That is why the engine is built in Rust.
Zero GC pauses
Agent workflows are latency-sensitive. Rust gives Evokoa a hot path without garbage collection, so context retrieval stays predictable under load.
Lightweight by design
Traditional graph databases are heavy. Rust lets Evokoa keep a compact relationship index beside your source data without bloating the infrastructure.
Fearless concurrency
Your data keeps changing while agents query paths. Rust helps Evokoa synchronize and traverse safely under concurrent read and write pressure.
Performance
We keep the map, not the territory.
We don’t copy your data. We don’t replace your database. We traverse the relationship map, figure out which rows matter, and hydrate them from your existing source of truth. On the Panama Papers dataset, this approach used ~34× less RAM than traditional graph databases.
No data movement. No second source of truth. No six-month integration.
less RAM than traditional graph databases
From the blog
Thinking out loud for operators who think deeply.
Postgres as a Graph Database: Four Approaches Compared
Your Postgres tables already contain graph-shaped data. Here are four ways to query it as a graph, from recursive CTEs to in-memory traversal engines, and when each one breaks down.
Dale EverettBuying pggraph.com: A Domain Meant to Be
If you build infrastructure in 2026, you expect the definitive .com to be held hostage by a squatter for fifty grand. We just bought pggraph.com for ten bucks. A glitch in the matrix.
Dalton PrescottLessons from John Carmack: Why We Built pgGraph Like a Game Engine
To build a graph engine capable of serving AI agents in real-time, we had to stop looking at traditional database architecture and start looking at DOOM. Here is what John Carmack taught us about memory layout, the hot loop, and stripping away abstractions.
Dale EverettGet Started
Every company will be AI-native within a decade.
Point Evokoa at your existing database. We build the relationship graph in memory. Your agents traverse it in milliseconds. Your data never moves.