Knowledge, at the speed of thought.
An ultra-fast knowledge-graph database, purpose-built for the AI era. FluxDB gives large language models a memory of facts and relationships they can draw on instantly — turning raw data into knowledge your models can actually use.
A large language model knows a great deal about the world in general and almost nothing about yours — your customers, your products, your history. The gap between a model's reasoning and your organization's knowledge is where most AI projects stall. Bolting a search box onto a database doesn't close it; the model still can't see how anything relates.
FluxDB closes that gap. It stores your knowledge as a graph of facts and the relationships between them, and serves exactly the right context to a model, the instant it's needed. Ask a question in plain language and FluxDB retrieves the connected knowledge behind the answer — the foundation for retrieval-augmented AI that is accurate, grounded, and current.
Give your models a memory that is fast, structured, and unmistakably yours.
Facts and their relationships in one connected model — so answers carry the context that makes them true.
Feed models the precise, connected context they need — the backbone of accurate, hallucination-resistant RAG.
Query your knowledge conversationally, or with precision when you need it — no specialist skills required to get answers.
Built for speed and a small footprint, so it drops into your stack and keeps up with real-time AI workloads.
FluxDB powers grounded chat assistants, retrieval-augmented search, recommendation and reasoning engines, and any system where an AI needs to draw on your knowledge to be useful. It anchors the AAIRC data line — with MANTIS as its embeddable graph engine — and feeds the memory that goal-driven systems like CORTEX reason over.
Raw data is a liability. Connected knowledge is an advantage.
