Case Studies

• Project: Automating High-Volume Claims Processing for a Global Insurer

• The Problem (Legacy Model): Ticketing & Manual Triage.

  • Description: A multi-step manual process for reviewing, categorizing, and routing high-volume claims created significant overhead and delays.

  • Friction ID: Telemetry identified claims processing as the primary bottleneck, with human operators spending 60% of their time on manual workflow navigation.

• The Solution (Mec Zarbi Agentic Model): Autonomous Proactive Resolution.

  • Description: Mec Zarbi mapped the unwritten claims logic and deployed goal-oriented agents to reason through, categorize, and execute the entire claims multi-step journey autonomously.

• The Impact (Maturity Roadmap Phase: Pilot): Continuous learning and agent performance tracking.

  • Key Results:

    • 92% Autonomy: The support resolution for claims became 92% autonomous.

    • Support & Operations: Triage and operations shifted from human coordination overhead to multi-step orchestrated agents, enabling self-healing ops.

    • Friction Reduction: Total operational friction was reduced by 78%.

Project: Self-Healing Ops for DevOps Efficiency in Manufacturing

The Problem (Legacy Model): Coordination Overhead.

• Description: Software and hardware operations in a globally distributed manufacturing network were managed through complex, menu-heavy, manual tasks and a reactive support system.

• Friction ID: Critical business logic was trapped in silos and human memory, leading to recurring configuration errors and reactive, instruction-based software operation.

The Solution (Mec Zarbi Agentic Model): Strategic Architecture for Operational Scaling.

• Description: Mec Zarbi reimagined DevOps workflows by formalizing unwritten rules into digital logic for proactive agentic AI workflows. The humans were reskilled as strategic architects from operators.

The Impact (Maturity Roadmap Phase: Scale): Deploying self-healing operations across the stack.

• Key Results:

  • 85% Improvement: DevOps efficiency improved by 85%.

  • Scaling: Operational scaling shifted from linear headcount growth to scalable autonomous intelligence.

  • Friction Reduction: Telemetry now drives continuous friction removal, contributing to a substantial reduction in coordination overhead.

Project: Faster Sales Cycle and Friction Removal in Multinational Finance

The Problem (Legacy Model): Reactive Support and Instruction-Based Software.

• Description: Complex financial product sales cycles were slowed by manual, multi-step workflows across data silos and unwritten rules, creating high overhead and friction for teams and customers.

The Solution (Mec Zarbi Agentic Model): Intent-Driven & Goal-Oriented Customer Journeys.

• Description: Mec Zarbi used AI telemetry to pinpoint friction points and replace the instruction-based system with agentic orchestration. Goal-oriented agents now reason through complex financial rules and proactively execute next steps, creating an intent-driven UX.

The Impact (Maturity Roadmap Phase: Optimization): Replacing bottlenecks with proactive, self-healing agentic AI workflows.

• Key Results:

  • 65% Faster: The sales cycle became 65% faster.

  • UX/UI: The user experience shifted from a menu-heavy and manual tasks model to an intent-driven and goal-oriented approach.

  • Friction Reduction: Total workflow friction was significantly reduced, allowing elite talent to return to high-level strategy and innovation.