Making the World's Data
AI-Native
The database engine for autonomous agents
Most databases were built for applications, not agents. Evokoa provides the infrastructure to make your data truly AI-native, allowing agents to understand your existing data without pipelines, copies, or losing structural meaning.
Your data already has the answers
We are not building another data warehouse. We make your existing Postgres data AI-native, enabling agents to navigate relationships like a human expert instead of just matching keywords.
In-Place Context
Query relationships directly inside your existing tables. No separate stores, no ETL, no copies of your data sitting somewhere else.
Relational Graphs
Multi-hop traversals and path queries run as native index lookups instead of slow joins. Agents get an instant, connected view of your data.
Agent Memory
Give agents a read-write interface into your relational schema, so they can build and retrieve structured memory without an external store.
The Scaling Limit of Flat Data
As data volumes scale, traditional databases and isolated vector silos become a bottleneck. To make data AI-native, infrastructure must connect related information instantly instead of relying on complex, expensive joins.
Global Data Creation Curve
Volume of digital content captured, copied, and consumed (Zettabytes)
Three extensions. One Postgres.
All built on standard Postgres. All designed to make your existing data legible to AI.
Polygres
Our managed virtual database layer provides graph traversal, vector search, and memory indexing directly inside Postgres. No separate infrastructure required.
pgGraph
A Postgres extension that turns your relational schema into a live graph. BFS, Dijkstra, and multi-hop traversals execute as index lookups instead of slow join chains.
pgContext
A drop-in replacement for pgvector, currently in development. Embeddings are stored and searched natively within your tables, delivering the throughput to match dedicated vector databases like Qdrant.
Built to stay out of your way
No new databases to manage. No pipelines to maintain. It runs inside the Postgres you already have.
Insights from the Lab
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.
Read Post →May 14, 2026Buying 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.
Read Post →May 13, 2026Lessons 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.
Read Post →Join Evokoa Labs
We're still early. If you're building with AI or thinking about the database problem, we'd like to hear from you.