The before and after of AI adoption, recorded in operating terms
We withhold names while publishing concrete environments, decisions, implementation steps, outcomes, and the points where human judgment remained essential.
A number never stands alone
Every story identifies scope, observation period, evidence type, and limits. We do not manufacture direct customer quotations.
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: Customer-reported and pilot-observed. Figures are approximate, limited to the participating applications and period, and are not an organization-wide productivity measurement.Read the full story→02Financial 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: Customer-reported and pilot-observed. Timing figures are approximate ranges for meaningful AI-assisted change reviews in participating teams.Read the full story→03Manufacturing · 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: Customer-reported and phase-observed. Results are approximate and limited to participating systems and incident classes.Read the full story→04Public 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: Customer-reported and operationally observed. The 5–7× range does not describe organization-wide productivity; it is an approximate comparison against prior manual processes for selected AI-suitable workflows.Read the full story→Fictional implementation patterns, kept separate from customer outcomes
The following scenarios explain product architecture by industry. They are clearly separated from observed customer evidence.
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 pattern↗02Financial 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 pattern↗03Manufacturing · 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 pattern↗04Public 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↗