TETRA for AI
Your RAG system is guessing.
Give it something to reason over.
Vector databases retrieve documents. They don’t retrieve relationships. When your model needs to know how two entities are connected—through what path, via what intermediaries, under what conditions—vector similarity can’t answer.
GraphRAG fixes this. 3.4× better accuracy than vector-only retrieval.
The only problem: every other graph database is too heavy to sit inside an inference loop.
3.4×
Accuracy lift
Diffbot KG-LM Benchmark
0.5ms
Shortest path
In-process query
66 MB
RAM
Total footprint
$299
/month
Flat rate, everything included
The Problem
Why RAG hallucinates
Models retrieve documents, stitch together answers from text fragments. They don’t actually know if entity A relates to entity B.
Vector similarity is good at “find documents like this.”
It’s bad at “find the connection between these two things.”
That gap is where hallucinations live. The model fills in what it doesn’t know with what sounds plausible.
The Solution
What GraphRAG does differently
Structured knowledge graphs give models explicit relationships to reason over. Not documents about Alice and Bob—but the actual org chart. Who reports to whom, through which department, as of what date.
Diffbot benchmark: 3.4× over vector-only across 43 complex business questions.
The model doesn’t have to guess. It traverses.
The Bottleneck
Why existing graph DBs don’t fit
Neo4j
Needs a cluster. 710 MB RAM minimum. Separate JVM process. Every retrieval is a network round-trip through your inference loop.
Amazon Neptune
Needs a VPC. Locked to AWS. Network latency on every query. Can’t sit on the same machine as your model.
TigerGraph
Needs a PowerEdge. Heavy infrastructure. Not something you embed alongside an inference process.
None of them can sit on the same machine as your model. Every retrieval = network round-trip.
The Answer
TETRA: 66 MB on your GPU server
Single native binary. Single mmap’d file. 66 MB RAM.
Localhost Bolt or in-process. No network hop. No JVM. No cluster.
0.5ms
Shortest path
Fast enough for synchronous retrieval inside your inference loop.
1,611/1,611
Cypher TCK
Full openCypher compliance. Your existing Cypher queries work.
30+
Graph algorithms
Community detection, centrality, embeddings—built in, not bolted on.
$299/mo
Flat rate
Everything included. No per-GB scaling. No surprise invoices.
Stop guessing. Start traversing.
See the benchmarks, or talk to us about putting TETRA inside your inference pipeline.