RESOURCES
Watch it. Read it.
Try it on your own content.
Everything here says one thing, the way we say it: Aspected adds AI control one layer down — at the retrieval layer, where the context is chosen. Accurate at the first attempt, in one go; the agentic layer keeps its own control, with far less to do. Right the first time, for a fraction of the tokens. Underneath it all is one thing: the first vector database that uses metadata as signals instead of filters — every signal an aspect of the record, scored in one pass.
THE MOVE, IN ONE PICTURE
01 · WATCH
Client demo
A Mosaic's pilot, told in their own words — what their assistant did before Aspected, and what changed. Recorded at the Northwest Community Health AI Summit, Portland.
Worked examples & field notes
Aspects: A New Vector Search Paradigm
The original introduction of aspects: vectors within the vector — the invention behind the first vector database with metadata signals
January 2026
The Vector-Database Tax
How a flaw in AI search wastes up to a quarter of your token budget and how we fixed it.
June 2026
Querying Aspected: A Code Tutorial with Multi-Aspect Searches
How to query the Aspected Database across text, metadata, and contextual dimensions.
May 2026
Use Case: Intelligent Enterprise Document Retrieval with Aspected
How context-aware retrieval helps enterprise AI find the right documents by combining content and context in a single representation.
April 2026
From Filters to Features: Aspected Database vs Vector Databases
A comparison of how vector databases handle contextual constraints and how multi-aspect embeddings enable unified similarity search.
April 2026
Metadata Enrichment as a First-Class AI Capability
How AI-generated metadata is reshaping enterprise retrieval and turning enriched metadata into a core component of relevance.
March 2026
It Wasn’t a Hallucination. It Was a Retrieval Failure.
An analysis of the root causes of hallucinations in RAG systems, and a relevance-first approach that reduces them by design.
February 2026
The long-form arguments
WHITEPAPER FOR CIO'S
The Vector-Database Tax
How a flaw in AI search wastes up to a quarter of your token budget and how we fixed it.
PDF · 5 Pages · June 2026
NO CLEANUP
Why Copilot Projects
Disappoint
How to improve results without cleaning Up SharePoint first. Keep Microsoft. Improve the Results.
PDF · 5 pages · June 2026
FIELD GUIDE
Why Copilot requires a cleanup
A field guide to improve adoption of Copilot, the retrieval mechanics behind disappointing answers, and the architecture that fixes them without touching SharePoint.
PDF · 9 pages · July 2026
FIELD GUIDE
How RAG systems fail
How to fix them with the Aspected Database.
PDF · 5 pages · April 2026
HOW IT STARTED
Multi-Aspect Vector Search Index explained
This paper details the state of the art architecture that enables a more efficient way to search for relevant documents
PDF · 5 pages · April 2026
04 · TRY
Then debug your own ten worst queries
The 30-day free trial is the full Aspected Database in your own account — your own internal content, never the public web, full dials, up to 5 TB. The prep pipeline handles all chunking, or push your own records through the ingestion API. From September 7, start it straight from AWS Marketplace, in your own account.