Tried RAG or Copilot — and didn’t trust the answers?
Many organizations discover that AI struggles once it meets real enterprise information.
Xillio helps fix that — using the Aspected architecture.
If this feels familiar, you’re not alone
It worked in the demo
But once connected to real documents, systems, and permissions, answers became inconsistent.
We adjusted prompts and models
But missing, outdated, or mis-scoped information kept slipping through.
Trust dropped quickly
Once users see wrong answers, adoption stalls.
This isn’t a model issue. It’s a retrieval issue.
Why RAG and Copilot struggle in real environments
Enterprise knowledge lives across documents, systems, and formats — often with decades of history, inconsistent structure, and strict access rules.
Most RAG and Copilot approaches assume clean data and perfect metadata. In reality:
- Relevant information is missed
- Constraints are applied too late
- Systems compensate with re-ranking and retries
- Costs rise without improving outcomes
AI fails at retrieval long before it fails at reasoning.
The Aspected approach — implemented by Xillio
Aspected is an AI knowledge architecture designed for organizations where traditional RAG approaches break down. Xillio helps implement this architecture end-to-end — from existing source systems to AI consumption.
Instead of layering fixes on top of failing retrieval, Aspected makes retrieval itself reliable — even when metadata is imperfect, queries are vague, and governance applies.
Why organizations work with Xillio
- Deep experience with complex, governed information landscapes
- End-to-end responsibility — not just tooling or advice
- Proven in regulated and operationally critical environments
- No forced platforms, models, or user interfaces
We own the outcome — not just the architecture.
Trusted in production environments
Production deployments where correctness and governance are non-negotiable.
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Why this works better - in practice
Traditional AI stacks separate semantic relevance from constraints like scope and access. This often leads to multiple queries, filtering, re-ranking, and retries.
Aspected resolves relevance and constraints together at retrieval time, reducing failure modes and making AI behavior more predictable in complex environments.
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
