Northstar sample only
Back to proof
AI Implementation OS
A demo-ready prototype that turns messy discovery notes into a reviewable AI implementation package.
backlog, memo, roadmap, controls
no external AI calls in the demo
Recruiter answer
What proof does this case study show?
This case study shows AI implementation ability through discovery intake, use-case prioritization, business-case framing, governance checkpoints, rollout planning, and enablement design.
Role fit
- AI implementation consulting
- Workflow discovery and prioritization
- Governance-aware rollout planning
Validation signals
- Synthetic data only
- Local deterministic demo flow
- Governance and rollout included before pilot launch
Interview questions answered
The short version a hiring team should understand.
- Messy workflow
- AI discovery work often leaves teams with scattered notes, unclear owners, half-formed use cases, governance concerns, and no practical rollout sequence.
- System designed
- A discovery-to-delivery package that turns intake notes into prioritized opportunities, a decision memo, governance notes, rollout milestones, and enablement tasks.
- AI-assisted build
- AI coding assistance helped build the local prototype, deterministic demo flow, data structures, and UI surfaces from the workflow spec.
- Validation
- The demo uses deterministic local logic, synthetic data, and inspectable outputs so the workflow can be reviewed without API keys or client data.
- Usage/adoption
- This is a portfolio-ready implementation package prototype, built to show how I would structure customer-facing AI discovery and rollout work.
- Proof signal
- It directly maps to AI implementation roles: intake, prioritization, business case, governance, roadmap, and enablement in one reviewable package.
- Improve next
- Add a second synthetic industry scenario and a lightweight evaluator that compares package quality across use cases.
- Public boundary
- The public version uses fake Northstar data. It is a demo-ready prototype, not a production client deployment.
Problem definition, workflow design, evaluation criteria, source selection, validation, rollout, user feedback, adoption, and outcome framing.
Code, app structure, scripts, UI wiring, parsing, tests, and iteration support. I stayed accountable for whether the workflow was useful and honest.
Problem
AI implementation discovery can stall when notes, stakeholder concerns, pilot ideas, governance needs, and rollout tasks are scattered across calls and documents.
What I Designed
Designed a discovery-to-delivery workflow: intake notes, extract candidate workflows, score value and effort, surface governance risks, draft the business case, and turn the result into a practical roadmap.
Validation
Kept the prototype deterministic and local so the public demo can be inspected without API keys, client data, or model-output ambiguity.
Public Handling
The public screenshots use synthetic Northstar data and frame the tool as a demo-ready prototype, not production software or a deployed client system.
Artifacts and proof
What the public proof can show.
These artifacts make the workflow concrete while keeping private data, credentials, logs, internal strategy, and real customer/user records out of public view.
Synthetic discovery notes flow into a structured implementation package.
Use cases are ranked by value, effort, risk, and pilot readiness.
Business case and control notes make the recommendation reviewable.
30/60/90 milestones and enablement tasks turn strategy into execution.
Positioning note
This is operator-led AI implementation proof: workflow judgment, validation, rollout thinking, adoption, and AI-assisted build support where coding is involved.
Good fit for teams that need someone who can turn ambiguous AI discovery into prioritized, governed, rollout-ready implementation work.