Memgraph

Building RAG over documents that can’t leave your environment? On Nov 3, Jacob Coles from Redfield AB walks through a fully local prototype for tax investigation. It uses LLMs to turn unstructured documents into a graph in Memgraph, so investigators can find suspicious payment patterns and conflicts of interest and ask questions in plain language. 6 PM CET / 12 PM ET Register here: https://lnkd.in/ddmTEuUb

What if your coding agent remembered? Join our Community Call on Oct 29: Ante Javor walks through the AI memory reference implementation in Memgraph’s AI Toolkit. Install in one line, then open your agent’s knowledge, actions and workflows as a graph. 6 PM CET / 1 PM ET Register here: https://lnkd.in/dt2Vu27A

Graph traversal is your workload, not one model among five? Memgraph's entire engine is built around one workload, with an in-memory-first, C/C++ architecture.

Running Neptune Database and Neptune Analytics side by side? Memgraph runs transactional and analytical graph workloads in one engine. No second system to provision, pay for or keep in sync.

Graph queries slowing down your LLM pipeline under load? Memgraph’s in-memory C++ engine delivers sub-millisecond multi-hop traversals, even under concurrent writes. Same query language.

Traditional machine learning scores transactions. Graph technology uncovers the hidden relationships behind them. Learn how Capitec combines graph databases and machine learning to improve fraud detection at production scale. Watch the recording instantly.