Built for operators, by operators.
We started Evokoa because we hit the same wall every enterprise AI team hits: the agent doesn't know enough about the company to do anything useful. We decided to fix the infrastructure instead of working around it.
Our Story
The MVP
We deployed a Postgres prototype to SMEs to test a radical idea: isolating structural data for AI. The AI gave flawless answers, but the database latency was fatal.
The Enterprise Blueprint
We took those learnings to national telecom providers, major logistics enterprises, and global SIs. We mapped their exact infrastructure bottlenecks.
The Rebuild
We engineered a completely new architecture from the bare metal up to solve the exact pain points the Fortune 500 validated.
The Architecture
Why a hypergraph.
In a standard graph, one edge connects exactly two nodes. That’s fine for social networks. It’s terrible for enterprise data.
Think about a real sales order. It connects a customer, a product, a sales rep, a location, and a payment method. That’s one relationship, not five separate edges. A standard graph has to decompose that into pairwise connections and immediately loses information.
A hypergraph lets one edge connect any number of nodes. That’s exactly how SQL join tables already work. A join table with 6 foreign keys is a hyperedge connecting 6 entities. We just model it natively instead of forcing it into a structure it was never meant to fit.
But here’s where it gets more powerful: in our model, relationships can connect to other relationships.Take Tom, who works at Acme — and was hired by Jane. That hiring event isn’t just a link between Tom and Acme. It’s a relationship that itself connects Tom, Jane, Acme, a role, a date, and a department. You can traverse across it in any direction. Standard graphs can’t represent that. Ours does natively.
This is why we can ingest massive join tables without breaking a sweat, and why our traversals don’t suffer from the path explosion that kills traditional graph databases at depth.
Why Speed Matters
Capability, not just convenience.
Legacy graph databases choke and time out on deep multi-hop reasoning. Our speed doesn't just make things faster. It makes the impossible possible.
Bypassing the pipeline
Our architecture mitigates path explosion, so we don't need heavy ETL pipelines. We use your existing SQL join tables and foreign keys directly, even when they join 6+ records together.
Unlocking 24/7 agentic reasoning
When an AI swarm runs dozens of deep reasoning loops every second, legacy database latency is a fatal bottleneck. We make 24/7 agentic swarms economically viable.
Making real-time AI viable
Voice AI collapses if the agent waits 3 seconds for context. Fraud detection needs 8 to 40-hop checks mid-transaction. Logistics needs instant port verification and supply chain mapping. All of these require microsecond infrastructure.
What We Believe
AI needs structure, not more tokens.
Everyone is trying to make AI agents smarter by giving them more text. More embeddings. More context window. We think the problem is different. The agent doesn't need more words. It needs to understand how things are connected.
That's a structural problem, not a language problem. And it requires structural infrastructure.
If the reasoning layer is slow, the agent is slow. We don't compromise on traversal latency.
Your data stays where it is. We build a live topological shadow. Nothing gets duplicated.
Connect your Postgres. Evokoa auto-discovers your schema and builds the topology. No pipelines. No consultants.
We built this for engineering teams shipping real products, not for analysts running ad hoc queries.
Get Started
Build on the reasoning layer.
Infrastructure for enterprise world models. Deploy in minutes, reason in microseconds.
Early access opens soon