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Case study / Applied AI

Operational AI workflow moved from prototype to governed rollout.

A services team had a promising model-assisted workflow, but the path from demo to daily use was blocked by unclear review rules, uneven source quality, and no shared way to judge output reliability.

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Engagement
Discovery through rollout plan
Timeline
8-week launch path
Disclosure
Anonymized; no named endorsement implied

01

Problem

The initial prototype could draft useful recommendations, but every edge case required informal judgment. Operators lacked confidence in when to trust the output, when to escalate, and which source material had influenced a response.

  • No explicit routing between auto-draft, human review, and escalation.
  • Source documents mixed stable policy, working notes, and one-off exceptions.
  • Quality feedback lived in conversations instead of reusable evaluation fixtures.

02

Approach

GTA Studios treated the AI feature as an operating workflow first and a model integration second. The engagement mapped decisions, inputs, risk levels, and ownership before tightening prompts or retrieval behavior.

  • Separated trusted reference material from situational notes and exceptions.
  • Defined review thresholds for low-risk drafts, assisted review, and manual handling.
  • Created a small evaluation set from real operational scenarios and failure modes.

03

Implementation work

The resulting implementation plan covered retrieval boundaries, prompt contracts, review-state data, feedback capture, and release sequencing. The artifacts were written so product owners could reason about behavior without reading model-provider documentation.

  • Workflow diagram for intake, retrieval, draft generation, review, and escalation.
  • Prompt and retrieval notes with required sources, forbidden assumptions, and fallback language.
  • Evaluation worksheet covering expected answer, risk rating, source coverage, and reviewer notes.

04

Outcome

The team left with a governed rollout path instead of an open-ended AI experiment. The workflow could be piloted with measured quality gates, clear human ownership, and visible reasons for each escalation.

  • Reduced launch uncertainty by tying model behavior to review states and source quality.
  • Gave operators a repeatable way to flag misses and improve the fixture set.
  • Created handoff notes for monitoring, support, and future workflow expansion.

Technical artifacts

Artifacts buyers can inspect and teams can operate.

Human-in-the-loop operating map

Workflow artifact

Intake -> retrieval -> drafted recommendation -> review state -> escalation or approval.

Quality and release readiness

Evaluation artifact

Scenario fixtures with expected decision, acceptable source coverage, and reviewer notes.

Operational ownership

Handoff artifact

Owner notes for prompt updates, retrieval refresh cadence, fallback handling, and support review.

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  • 01AI, cloud, product, and systems work
  • 02Discovery through implementation
  • 03Production-minded handoff