AI that works.
As aspected.
Aspected combines semantic text and structured metadata into one query, allowing you to search everything in a single, unified vector space. Enterprise AI Knowledge Retrieval becomes reliable with the Aspected Database generating the right answers.
Similarity ≠ Correctness
Vector Databases retrieve similar text. Enterprise AI needs the right knowledge.
Why the RAG Stack is getting more complex
Because you are adding layers, context is added after retrieval
As similarity search lacks context, teams keep adding layers on top of their vector databases. This increases complexity, reduces reliability and still not providing the correct answers.
Vector Database
Embedding - text only
Ranking signal - semantic similarity
Metadata - filtered after retrieval
Authority - ignored
Structure - ingnored
Result - similar documents
Extra Layers
+ metadata filters
+ rerankers
+ graph DB
+ rules
The Aspected Database
We are fixing retrieval at the source and provide it as ONE system.
Instead of asking: "What text looks similar?"
We ask: "What knowledge is correct in this context?"
Simple, reliable and correct!
Aspected
Embedding - text + metadata + context
Ranking signal - multi-aspect relevance
Metadata - part of ranking at index time
Authority - first-class-dimensions
Structure - native to index
Result - correct information
Aspected for Copilot
A production use case for the Aspected Database
Many organizations are adopting Microsoft Copilot on top of SharePoint.
In practice, retrieval quality often becomes the limiting factor:
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Relevant documents are missed
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Results vary depending on phrasing
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Near-relevant information is not surfaced
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Users retry prompts to improve answers
In many environments, improving these results requires extensive cleanup and restructuring of SharePoint before AI performs consistently.
Aspected reduces the dependency on perfectly cleaned SharePoint environments.
Aspected for Copilot places the Aspected Database between SharePoint and Copilot to improve how context is prepared and retrieved for AI systems. Instead of treating a request as a single similarity query, the Aspected Database evaluates multiple aspects simultaneously, such as:
Multiple Aspects
Meaning
Time
Priority
Intent
Context
Constraints
This results in:
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More reliable Copilot answers
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Fewer silent semantic misses
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Lower retrieval complexity
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Improved retrieval consistency
Already deployed in production at AGFA.
Copilot answers. SharePoint stores. Aspected for Copilot retrieves the right context.
Organisations that deploy Aspected already today
Customers
AGFA Service engineers use AI to gather, prepare, and orchestrate enterprise knowledge, delivering the right answer from the right source, directly within their ticketing system.
Partners
Aspected works with different local and global AI consultancy and system integration partners. Such as Novadoc and Capgemini.
Frequently Asked Questions (FAQ)
Aspected solves the retrieval problem in enterprise AI. Many RAG systems retrieve text that is semantically similar, but not necessarily correct for the user’s context. Aspected helps AI systems retrieve the right knowledge by combining meaning, metadata, structure, authority, and context into one retrieval process.
Aspected is built for teams developing enterprise AI, RAG applications, knowledge assistants, service automation, and AI-powered search. It is especially relevant for organizations where retrieval quality, trust, context, and source correctness are business-critical.
The Aspected Database is a retrieval engine for AI and RAG systems. It creates a unified representation of content and metadata, so queries can retrieve knowledge using multiple signals in one step instead of relying on separate filters and reranking layers.
Traditional vector databases mainly rank results by semantic similarity. Aspected ranks knowledge using multiple aspects at once, including text, metadata, time, type, structure, and context. This makes retrieval more precise and reduces the need for extra filters, rerankers, or custom rules.
Similarity search can find content that looks related, but enterprise AI often needs content that is correct, current, trusted, and relevant to a specific business context. In complex knowledge environments, “similar” and “right” are not always the same thing.
Aspected can replace parts of a traditional RAG retrieval pipeline, especially where teams currently rely on vector search plus metadata filters, rerankers, graph databases, or rules. It is designed to simplify retrieval by bringing these relevance signals together in one system.
Aspected is a Spin-off from Xillio - a global enterprise data transformation company.
Over two decades, Xillio worked inside some of the world’s most complex enterprise knowledge environments — building software to transform and migrate large-scale content systems. This experience led directly to the creation of Aspected.
We invented the Aspected Database, a retrieval engine for AI systems that enables organizations to access the right knowledge from complex enterprise data. By combining semantic understanding with operational context such as metadata and system signals, Aspected helps AI assistants retrieve relevant information instead of just similar text. This makes enterprise AI more reliable and useful in real-world operations
Yes. Aspected works with AI consultancies and system integration partners that help customers implement enterprise AI retrieval solutions. Don't hesitate to contact us here.