Live - MVP
LinkedCulture
Unified Cultural Search
Search Harvard Art Museums, the Metropolitan Museum of Art, and Smithsonian Asian collections simultaneously through a single vector search interface. Built on Nomic embeddings and Qdrant. No LLM in the loop. Pure semantic similarity at scale.
Pilot Fit
A useful pilot would index one collection or a small cross-institutional set, then evaluate search quality against real research, education, or public discovery tasks.
Who It Serves
Museums, archives, libraries, researchers, digital humanities teams, and cultural heritage organizations exploring semantic discovery.
Themes
Shared Process
From fragmented inputs to usable outputs.
Ingest collection records
Generate embeddings
Index in Qdrant
Search by semantic similarity
A research platform for cultural collection discovery: unified vector search across major open-access collections, without an LLM in the retrieval loop.
I support museums, archives, and cultural institutions with digital infrastructure that empowers discovery. Less manual friction, more time for storytelling, stewardship, and shared knowledge.
Workflow: From Raw Data to Public Insight
Museum APIs and collection exports
Systems:
Harvard, Met, Smithsonian
Convert records into semantic vectors
Systems:
Nomic embeddings
Store and query similarity at scale
Systems:
Qdrant
Compare cross-collection discovery quality
Systems:
Search logs and result review
Search by concepts, materials, symbols, and meaning
Systems:
Next.js interface
LinkedCulture System Relationship Diagram
This diagram visualizes how collection metadata, embedding generation, vector search, and the public interface coordinate in the prototype.