Structuring internal documents and operating knowledge so a team can query them reliably instead of asking the one person who remembers.

DRAFT — This case study is in development. The structure is final; metrics and implementation details are placeholders until validation is published.

TBD

Question types covered

in-progress

TBD

Answer accuracy target

in-progress

TBD

Lookup time reduction

in-progress

01 · BASELINE

Start with the current system.

Placeholder: the baseline audit will quantify where knowledge currently lives, how often routine questions recur, and how long an answer takes today. The measurement method is defined; the numbers are not yet published.

Constraints

  • Internal documents cannot leave the controlled environment.
  • Answers must cite their source document.
  • The workflow must be maintainable by a non-engineering owner.

02 · CHALLENGE

Look past the visible task.

The visible request is a chatbot. The real problem is that the corpus is unstructured and partly stale — retrieval quality is decided before any model is involved. The case will show why corpus curation, not model choice, was the first decision.

03 · RESPONSE

Separate the system from the spreadsheet.

Placeholder: the response section will walk through the knowledge inventory, the decision of what not to index, the chunking and metadata rules, and how answer citations are enforced.

Key decisions

  • Curate the corpus before choosing tooling.
  • Force citations so wrong answers are traceable.
  • Define a fallback path instead of letting the system guess.

04 · ARCHITECTURE

Make the operating path visible.

01

Source corpus

Curated documents with ownership and freshness rules.

02

Index layer

Structured chunks with metadata for filtering.

03

Retrieval

Query-to-context assembly with relevance thresholds.

04

Answer layer

Cited answers with an explicit fallback when confidence is low.

05 · IMPLEMENTATION

Build the smallest useful system.

Placeholder: implementation notes will cover the indexing pipeline, the evaluation set of real questions, and the update routine that keeps the corpus from going stale.

06 · VALIDATION & ADOPTION

Prove that the system can be used.

Validation

Placeholder: validation will publish the evaluation question set, accuracy scoring method, and results across question types.

Adoption

Placeholder: adoption notes will cover who asks what, how the fallback path routes to a human, and how document owners keep sources current.

07 · OUTCOME

Evidence, with boundaries.

This case study is in development. The outcome section will state measured effects only after validation completes; no results are claimed yet.

PROOF NOTES

  • All metrics on this page are placeholders pending validation.
  • Source documents and internal identifiers are anonymized.
  • The evaluation set is still being assembled.

Next iteration

  • Finish the knowledge inventory and publish the baseline numbers.
  • Run the evaluation set and publish accuracy by question type.
  • Document the maintenance routine and its owner.