Aspected Database

Unified retrieval for RAG systems

Most RAG systems don’t fail at generation, they fail at retrieval. Today’s approach is fragmented:

•    Vector search handles meaning
•    Metadata is applied as filters
•    Rerankers try to fix the gaps

This leads to complex pipelines and unreliable results.

Aspected Database replaces this with a single, unified model.

Aspected Image Metadata Embeddings Context

One Representation. One Query. Better Results.

Aspected Database encodes semantics and metadata together into a single vector.

Instead of:
•    Embedding + filters + reranking

You get:
•    Multi-dimensional retrieval in one step

Traditional RAG vs Aspected
Comparison

From Filters to Signals

Traditional systems ask:
“Does this document match the filter?”

Aspected Database asks:
“How much should this matter for relevance?”

This enables:
•    Weighted ranking instead of hard constraints
•    Context-aware retrieval
•    More natural and accurate results

Pre and Post Filters vs Aspected
Comparison

Whitepapers

More in dept information about the Aspected Database, the technology and idea behind it and its place in the Aspected solutions and stack.