The before and after of AI adoption, shown as example scenarios
Before launch, our published material consists of example scenarios, not customer cases. They draw concrete environments, decisions, implementation steps, outcomes, and the points where human judgment remains essential.
A number never stands alone
Every scenario identifies scope, observation period, evidence type, and limits. Figures are illustrative assumptions, not measurements.
Developers wanted AI. The hospital needed a boundary it could control.
A controlled Mirr Code and Mirr Enterprise rollout helped 18 developers understand and maintain aging systems without sending patient information or operational systems beyond the hospital’s boundary.
Evidence scope: Illustrative figures for an example scenario. They assume the stated applications and period and are not an organization-wide productivity measurement.View the scenario02Financial services · Mirr Code + Mirr Enterprise + CrewAI-assisted changes remained explainable weeks later
Mirr Code, Mirr Enterprise, and Crew connected AI-assisted change context across engineering, security, architecture, and operations, reducing evidence preparation from roughly 2–3 hours to 20–30 minutes.
Evidence scope: Illustrative figures for an example scenario. Timing figures are approximate ranges for meaningful AI-assisted change reviews in a fictional team.View the scenario03Manufacturing · Mirr Code + Mirr Enterprise + Crew + PilotAfter deploying agents, the company needed a way to operate them
A legacy-system analysis project expanded into an operating model for 14 specialized agents, introduced in phases through Mirr Code, Mirr Enterprise, Crew, and Pilot.
Evidence scope: Illustrative figures for an example scenario. Results are approximate and limited to the fictional systems and incident classes shown.View the scenario04Public sector · Mirr Code + Mirr Enterprise + Crew + PilotAn environment that prohibited external AI made it an everyday engineering capability
An on-premise architecture kept source code, prompts, inference, execution, and evidence inside the controlled environment, giving approximately 70 technical staff approved access to generative AI at work.
Evidence scope: Illustrative figures for an example scenario. The 5–7× range does not describe organization-wide productivity; it is an approximate comparison against prior manual processes for selected AI-suitable workflows.View the scenarioImplementation patterns by industry
The following scenarios explain product architecture by industry. They are illustrative, not customer outcomes.
Specialty hospital in Seoul
Balancing delivery speed and control for clinical-support systems
A fictional design in which an internal hospital team uses Mirr Code for changes while Mirr Enterprise governs personal-data access, approval scope, execution environments, and evidence.
Read implementation pattern02Financial services · fictional implementation scenarioKorean financial-services organization
Shortening regulated code review while preserving separation of duties
A software-delivery pattern that expands development automation while Mirr Enterprise fixes dual approval, permitted tools, and production-data boundaries as policy.
Read implementation pattern03Manufacturing · fictional implementation scenarioGlobal industrial enterprise
Modernizing production systems whose documentation no longer matches the code
A modernization pattern that moves from read-only analysis to staged deployment while controlling permissions by plant and environment.
Read implementation pattern04Public sector · fictional implementation scenarioPublic-sector development organization
Operating a coding agent inside a sovereign, disconnected environment
A sovereign deployment pattern that keeps models, tools, and repositories inside the approved boundary while requiring human approval and audit-ready execution records.
Read implementation pattern