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.
THE MOVE, IN ONE PICTURE
01 · WATCH
The 70-second explainer
One question, from uncontrolled to controlled — the whole argument in seventy seconds.
Mosaic Community Health, on stage
A customer pilot, told in the customer’s own words — what their assistant did before Aspected, and what changed. Recorded at the Northwest Community Health AI Summit, Portland.
02 · READ
The deck, the deep dive, the demo
THE COMPANY DECK
Add AI control one layer down — the presentation
The canonical story in 20 slides: where control lives today, the two flaws, the invention, the demo in five beats, two advantages, four use cases, pricing. Forwardable PDF and editable PowerPoint.
THE MESSAGING KIT
The one-page messaging kit
The line, the payoff, the rebuttal, the five-beat demo script, the language rules — one page for anyone who tells the Aspected story: your team, your partners, your champions inside an account.
THE WHITEPAPERS
The technical argument, for the skeptics
The worked example in full: one record, three retrieval methods, three verdicts — why filter-and-rerank pipelines fight themselves, and why the signals must be in the ranking equation itself. Written for readers who already run metadata filtering — and want to see the difference.
THE LIVE DEMO
One question, watched all the way through
Meaning only → one aspect on → aspects fused → saved as the retrieval policy. The four states, clickable, with the year punchline: an aspect is a dial, not a gate.
Worked examples & field notes
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
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
Querying Aspected: A Code Tutorial with Multi-Aspect Searches
How to query the Aspected Database across text, metadata, and contextual dimensions.
May 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
Aspects: A New Vector Search Paradigm
A unified vector search approach that embeds both meaning and metadata into a single representation, making relevance more natural and efficient.
January 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
05 · TRY
Then debug your own ten worst queries
Cockpit Debugger — the retrieval debugger — is the free version: one developer, your own internal content — never the public web — full dials, and one allowance in two rulers: 25 GB of raw content — the prep pipeline handles all chunking — or 1,000,000 records if you bring your own chunks, indexed as they are. And from September 7, the same Cockpit Debugger is the free trial on AWS Marketplace — in your own account, no sales call required.