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>case study

retrieval-augmented enterprise knowledge agent

A workflow-aware internal knowledge agent designed for grounded answers, confidence surfacing, and graceful failure.

>problem

Staff often need accurate answers from fragmented enterprise knowledge sources.

The challenge is not only retrieval speed.

The system must provide grounded answers, expose uncertainty, avoid hallucinated confidence, and fail safely when the evidence is weak.

>constraints

  • > internal knowledge sources
  • > source grounding
  • > confidence surfacing
  • > workflow fit
  • > governance requirements
  • > graceful failure
  • > maintainability
  • > clear escalation paths

>my role

I designed the workflow, defined the retrieval behavior, built the integration path, tested operational use cases, and refined the system based on real-world constraints.

>architecture

  user question
       │
       ▼
  ┌─────────────────────┐
  │  retrieval layer    │  ← query expansion · source selection
  └─────────────────────┘
       │
       ▼
  ┌─────────────────────┐
  │  source ranking     │  ← evidence scoring · recency · authority
  └─────────────────────┘
       │
       ▼
  ┌─────────────────────┐
  │  grounded response  │  ← model conditioned on retrieved evidence
  └─────────────────────┘
       │
       ▼
  ┌─────────────────────┐
  │ confidence surfacer │  ← expose uncertainty when evidence is weak
  └─────────────────────┘
       │
       ├──▶ answer + citations    (high confidence + grounded)
       └──▶ escalate / decline    (low confidence or no evidence)

>what mattered most

The important work was not simply connecting a model.

The system needed to know when to answer, when to show evidence, when to expose uncertainty, and when to stop and escalate.

>outcomes

The project established a reusable pattern for grounded enterprise knowledge access.

It also exposed practical failure modes that matter in real deployment: weak source ranking, stale content, overconfident responses, and unclear fallback behavior.

>lessons learned

  • retrieval quality matters more than raw model fluency
  • confidence must be visible
  • fallback behavior must be designed intentionally
  • workflow fit determines adoption
  • governance cannot be added at the end

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