Xillio Aspected
Xillio Aspected | High-Precision Retrieval Architecture for Enterprise AI
High-Precision Retrieval Architecture for Enterprise AI
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Aspected as the core retrieval layer
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Modular, production-grade enterprise architecture
- Designed for relevance, explainability, and control
Why Enterprise AI Hits a Retrieval Ceiling
Large Language Models have improved rapidly — but retrieval has not.
Most enterprise AI systems rely on vector similarity combined with metadata filters. While this works at small scale, it breaks down as content grows:
- Vector similarity does not equal relevance
- Metadata filtering fragments semantic meaning
- Precision degrades as corpora expand
- Hallucinations emerge despite strong models
- Costs rise due to over-retrieval and re-ranking
These are structural retrieval problems, not model problems.
Not a Product. A Reference Architecture.
Xillio Aspected is not a monolithic platform or a Copilot alternative.
It is a reference architecture that demonstrates how enterprises deploy Aspected — a retrieval primitive — in real-world, production environments.
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Aspected is the invariant core
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Other components are modular and replaceable
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The architecture scales from local to enterprise deployments
This separation allows enterprises to retain control while benefiting from a fundamentally better retrieval model.
Xillio Aspected Architecture
Xillio Aspected shows how high-precision RAG systems are built around Aspected in practice.
Prepare (Xillio-owned)
- Content ingestion and scraping
- Text extraction and enrichment
- Chunking, PI detection, and filtering
- Dataset preparation and source merging
Orchestration
- Workflow automation via n8n (external)
Retrieval (core)
- Aspected multi-aspect vector index
- Context-aware relevance computation
Interfaces
- Compatible with multiple LLMs and UIs
- MCP-based integration layer
All components except n8n are developed and owned by Xillio.
What Makes Xillio Aspected Different
Metadata directly influences similarity
Documents are represented across multiple semantic aspects
Retrieval behavior is explainable at aspect level
Precision improves as structure increases
Where Xillio Aspected Is Used
- High-precision RAG systems
- Agentic AI requiring deterministic behavior
- Regulated or sovereign AI deployments
- Large, heterogeneous enterprise document landscapes
Proven in Production
Xillio Aspected is already running in production environments, including public-sector organizations with strict requirements around accuracy, sovereignty, and explainability.
What customers validate is not a user interface — but retrieval behavior that holds up under real-world conditions.
The Role of Aspected
Aspected is the retrieval primitive at the heart of Xillio Aspected.
It can be:
- Embedded into existing AI stacks
- Deployed as part of the Xillio Aspected reference architecture
- Integrated independently of orchestration or UI choices
Xillio Aspected demonstrates how Aspected is operationalized — not what it is limited to.
Talk to an Expert
Why Enterprises Trust Xillio
Xillio brings over two decades of experience working with the most complex enterprise content environments.
- 20+ years solving the hardest enterprise content problems
- Deep expertise with legacy ECM, governed estates, and permission models
- ISO 27001 security and compliance standards
- Microsoft Content AI Preferred Partner
- Proven track record of delivering on enterprise risk guarantees
