If your agents work with images, documents, video, or structured records, not just chat logs, you've likely hit the same wall we did. Most memory tools treat everything as text chunks in a vector store. An agent walks into a repeat problem like it's the first time, every time, because the memory underneath it was never built to hold what the agent actually saw.
Aperture Nexus is our answer. It's built directly on ApertureDB, the unified graph-vector-multimodal database already running in production, so multimodal facts stay first-class instead of being reduced to captions or extracted text. Context, who, what, when, why, and how, is stamped on every commit. Every commit traces back to its original source, the image, the document, the actual session, today; lineage surviving across updates is a v2 item. Knowledge and Memory both live together in ApertureDB.
We are almost at the end of our Summer of Workflows series! This week we are featuring the Label Studio Workflow inside ApertureDB Cloud: See It In Action
Spin up Label Studio connected to your ApertureDB instance
Label & annotate images right where your data lives
Build labeled datasets faster without manual transfer of cloud bucket URLs.
Perfect for anyone building multimodal AI agents or applications and looking to streamline annotation + dataset creation. Ready to try it yourself? Start here Read The Docs | Explore The Code | Additional Resources Only 2 workflows left in the summer series stay tuned!
Feedback always welcome we re building this for the AI/ML community!
How do you easily generate embeddings, detect objects, infer new attributes, or query your multimodal data? Stop wrestling with your datasets - use ApertureDB Multimodal AI workflows instead! Ingest or enrich complex datasets, run Jupyter notebooks, and more.
