OpsPilotAI / Case Study
OpsPilotAI: AI Automation Planning With Responsible Guardrails
A public-safe product case study showing how messy operational pain points become scoped AI automation pilots, risk notes, and implementation briefs.
Executive Summary
OpsPilotAI is a deployed AI operations and automation concept built around a practical question: before automating a business workflow, can the team define the problem, pilot scope, risk posture, human-review requirements, and success measures clearly enough to act safely?
Problem
What needed solving
Operations teams often know a workflow is painful but do not yet know whether it is a good automation candidate. Jumping straight to tooling creates risk: vague requirements, unowned approvals, unclear success metrics, and AI workflows that look impressive but are not safe to deploy.
Context
Why this mattered
I built OpsPilotAI as a recruiter- and client-readable product showcase for applied AI automation planning. The public repository is intentionally sanitized: it demonstrates product direction, buyer-facing workflow shape, fake sample data, and architecture boundaries without exposing backend code, prompts, scoring logic, customer data, or deployment internals.
Constraints
Operating boundaries
- Use fictional sample data only; do not expose customer workflows, contact requests, roadmap records, or private notes.
- Keep the public showcase separate from private implementation details such as APIs, prompt management, scoring logic, persistence, provider configuration, and deployment wiring.
- Frame AI automation as decision support with human review, not autonomous production action.
- Make the portfolio story useful to recruiters and prospective clients without publishing sensitive implementation material.
- Show enough structure to prove product thinking while preserving security, privacy, and operational boundaries.
Build
What I built
- A public product/landing presence for OpsPilotAI focused on AI automation planning and responsible operations automation.
- A sanitized public showcase repository with README, architecture overview, fake input/output samples, static mockup, and fictional case-study narrative.
- A demo workflow that turns an operational pain point into a workflow brief, risk review, priority recommendation, pilot scope, and success criteria.
- A clear public/private boundary documenting what is intentionally omitted from the public repository.
Architecture
Workflow
- Public-facing layer: landing page, product messaging, safe demo examples, static mockup assets, and contact path.
- Input model: a fictional operations pain point describing department, current workflow, tools used, frequency, and desired outcome.
- Planning layer: converts that input into a pilot candidate with priority, risk level, pilot scope, human-review requirement, and success measure.
- Private implementation layer: backend APIs, agent orchestration, prompt management, scoring, persistence, provider configuration, and invite-gated demo access remain outside the public repo.
- Portfolio layer: mikepatraw.com points recruiters to the product story without claiming the public repo contains the full backend or private scoring logic.
Security / Operations
Key decisions
- Separated public proof from private implementation because applied AI products can leak too much through prompts, scoring rules, deployment config, or customer-specific workflow examples.
- Used fake sample data so the demo can be inspected publicly without creating privacy or compliance risk.
- Kept human approval explicit in the sample output because the right first step for many operational automations is a bounded pilot, not unattended execution.
- Documented what is not in the repo as deliberately as what is in it, reducing recruiter confusion and preventing overclaiming.
- Positioned the product around workflow analysis and pilot prioritization instead of generic chatbot capability.
Impact
What changed
- Creates a flagship portfolio example for the intersection Mike wants to own: cybersecurity discipline, operational judgment, and agentic AI/product execution.
- Shows product thinking beyond code: buyer problem, safe demo boundary, implementation brief, risk review, and measurable pilot criteria.
- Demonstrates public-safe AI portfolio hygiene by proving the work without publishing prompts, scoring internals, customer data, or private deployment details.
- Gives recruiters a concrete artifact to evaluate for AI automation, responsible AI operations, workflow automation, and implementation planning roles.
Evidence
Source trail
OpsPilot AI public showcase repository
Sanitized architecture overview
Fake input/output samples
OpsPilotAI product site
Next
What I’d improve
- Add public screenshots or short demo clips that show the workflow shape without revealing backend internals.
- Publish a second fictional scenario in a different domain, such as healthcare operations or cybersecurity ticket triage.
- Add a simple before/after matrix for time saved, review burden, risk controls, and pilot readiness.
- Keep the private implementation and public showcase synchronized at the level of product claims, not sensitive code.
Public-safe notes
Publishing boundary
- The public repository is explicitly sanitized and does not contain backend source code, agent prompts, scoring logic, customer data, deployment configuration, or internal business logic.
- Sample inputs and outputs are fictional and are used to demonstrate the product shape, not to imply a real customer deployment.
- This case study is written as portfolio evidence for product/security/AI automation judgment, not as a disclosure of private implementation details.