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Case studies & workflows

See the decisions
behind the delivery.

Explore how a well-scoped data program could work, from the first requirement to review, evidence and handoff.

Illustrative by design. These are detailed worked examples, not customer case studies. They contain no claimed engagements or measured results.

VS / FIELDNOTES

Speech review / study 01

Listen. Review. Understand.

  1. IN
    Source recordingsConditions + terminology
  2. QA
    Reviewed referencesUncertainty + adjudication
  3. OUT
    Diagnostic evidenceError categories + source IDs
Illustrative workflow · not customer results

Workflow study / 01

From difficult recordings to a usable reference set.

How a speech team could structure transcription, specialist review and delivery when noisy calls and domain terminology make a first-pass transcript insufficient.

For
Speech engineering & data operations
Takeaway
A traceable reference set and an error analysis plan
Read the workflow study
VS / FIELDNOTES

Multilingual review / study 02

Meaning needs context.

  1. IN
    Locale scenariosIntent + product context
  2. QA
    Linguistic judgmentMeaning + terminology + fit
  3. OUT
    Actionable issuesEvidence + the right owner
Illustrative workflow · not customer results

Workflow study / 02

A locale launch needs more than translated words.

A worked example of evaluating meaning, terminology and task completion when an AI support experience expands into new language contexts.

For
Localization, product & language teams
Takeaway
A locale-specific review rubric and issue register
Read the workflow study
VS / FIELDNOTES

Agent evaluation / study 03

Inspect the path, not just the answer.

  1. IN
    Task & initial stateRequest + authorization
  2. QA
    Observable trajectoryRetrieval + tool calls + recovery
  3. OUT
    Verified outcomeFinal state + failure analysis
Illustrative workflow · not customer results

Workflow study / 03

The final answer is only part of the agent’s work.

A worked evaluation plan for an assistant that retrieves information, selects tools and changes state, with attention to the path as well as the outcome.

For
AI engineering, evaluation & product teams
Takeaway
A trajectory review and failure taxonomy
Read the workflow study

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