Centralized AI Shared Services Helps Cut Cost and Scale Capacity

Overview

A Governed Shared Services Layer on Claude and EVOQ

A global enterprise managing a broad portfolio of client accounts and internal business units faced fragmented AI tooling, subscription overhead, and inconsistent service quality that threatened to limit its scale. AI tooling had grown account by account, creating duplicate subscriptions, knowledge silos, and ungoverned token consumption. Myridius centralized the organization's AI operations into a governed Shared Services model built on Claude Opus, Claude Sonnet, and Claude Haiku, integrated with the proprietary Myridius EVOQ fabric.

Key Outcomes

• Ticket resolution times cut 45 percent and more than 40 percent in total cost savings.
• Over 95 percent service consistency through unified governance.
• Capacity scaled 2.5-fold and documentation effort cut 60 percent.

Client Context

Efficiency and Consistency as Growth Levers

The client is a global AI engineering partner that delivers services across dozens of client accounts and internal business units. In an operation of this shape, efficiency and consistency are not incidental. They determine how competitively the organization can price its services and how quickly it can take on new accounts. When AI tooling and support practices grow independently in each account, the resulting duplication and variance become a direct tax on growth, paid in cost, in uneven service quality, and in the ceiling it places on scale. Bringing that sprawl under unified governance was therefore a strategic priority, not a housekeeping exercise.

The Challenge

When Tooling Grows Account by Account

The organization's AI tooling and processes worked, but they had grown account by account, each with its own subscriptions, tools, and support practices. That pattern created duplicate tooling, subscription management overhead, and knowledge silos that made service quality inconsistent from one client account or business unit to the next. Token consumption across the fragmented tool stack was hard to control, and without a unified way to orchestrate AI usage, the organization could not fully capture automation's potential. None of this stopped the business from serving its accounts, but at enterprise scale the cumulative effect was rising cost, uneven service quality, and a ceiling on how far the operation could scale.

Status Quo and Desired State

Transformation Goals

What Success Looked Like

The engagement was guided by three north stars: replace account-by-account sprawl with a single governed layer, make automation reusable so capacity grows without proportional headcount, and standardize governance so service quality is consistent across every account.

• Cost reduction and operational control: Consolidate fragmented tooling and subscriptions into one governed Shared Services layer, cutting cost and controlling token consumption.
• Scale: Build reusable AI skills and agentic automation so the organization can handle higher account volumes without adding headcount in proportion.
• Governance and trust: Standardize model orchestration and governance to enforce consistent service quality and compliance across all accounts.

Myridius Solution Approach

A Foundational Redesign, Not a Point Fix

Myridius approached the engagement as a foundational redesign of how the organization runs AI operations rather than a point fix for any single account. The work began with mapping the fragmented tooling, subscriptions, and support processes across client accounts and internal business units. From that operating picture, Myridius orchestrated a centralized Shared Services model that replaced account-by-account tooling with a single governed layer.

• Orchestrated the foundation: Established a single, governed Shared Services operating layer integrating Claude Opus, Claude Sonnet, and Claude Haiku with the proprietary EVOQ fabric, replacing fragmented, account-by-account tooling.
• Embedded intelligence into the workflow: Built a library of reusable AI skills within EVOQ and developed CLI-based agentic tooling, including the Intent to Delivery Engine and Content Processor using Claude Code, Claude Cowork, and Claude Design, to automate engineering and content workflows, alongside automated log analysis and agentic ticket responses that reduced manual support and documentation effort.
• Reimagined the operating model: Maintained a clear separation between day-to-day operational run and transformational change initiatives, building capacity that scales with account volume without proportional headcount growth.

Governance and Trust

Governance as the Organizing Principle

Governance was the organizing principle of the Shared Services model, not a control bolted on afterward. Model usage was standardized and routed through a single governed layer across all client accounts, which controlled token consumption, eliminated duplicate subscriptions, and enforced consistent service quality and compliance through unified model orchestration. Because every account draws on the same governed fabric, service quality no longer varies with local practices, and the organization can demonstrate consistent standards across its entire portfolio.

Measurable Impact

Cost, Consistency, and Capacity Rising Together

What began as an effort to centralize AI tooling became a broader shift in how the organization delivers service across every account. Cost, consistency, and capacity improved together: centralized governance made automation more consistent, and consistent automation made further cost savings and scale possible.

The result:

• Ticket resolution times dropped 45 percent through automated log analysis and agentic responses, and centralizing the AI fabric and standardizing processes yielded 30 percent operational cost savings, combining with automation and consistency gains for more than 40 percent in total cost savings.
• Service consistency reached over 95 percent, enforcing uniform quality and compliance through EVOQ governance.
• Capacity scaled 2.5-fold, enabling the organization to handle higher account volumes without proportional headcount growth, while automated knowledge base updates and routine documentation tasks were reduced by 60 percent.

Operational Transformation

Before and After the Shift

Technology Stack

The Tools Behind It

Frequently Asked Questions

What is an AI Shared Services model and what problem does it solve?

It is a single, governed operating layer that replaces fragmented, account-by-account AI tooling with unified model orchestration. For this organization it eliminated duplicate subscriptions, controlled token consumption, and standardized service quality across dozens of client accounts and business units. The result was lower cost and consistent governance without sacrificing the flexibility each account needs.

How much did centralization improve cost and speed?

Ticket resolution times fell 45 percent through automated log analysis and agentic responses. Centralizing the AI fabric and standardizing processes produced 30 percent operational cost savings, which combined with automation and consistency gains for more than 40 percent in total cost savings. Documentation effort was also reduced by 60 percent.

How does the model scale capacity without adding headcount?

Myridius built a library of reusable AI skills within the EVOQ fabric and agentic CLI tooling, including an Intent to Delivery Engine and Content Processor, so common engineering, content, and support work is automated rather than staffed. This let the organization scale capacity 2.5-fold and take on higher account volumes without proportional headcount growth, while keeping operational run separate from transformational change.

How is service quality kept consistent across accounts?

All AI model usage is routed and governed through a single layer, so quality and compliance no longer depend on the local practices of each account. This unified orchestration lifted service consistency to over 95 percent. The same governance also controls token consumption and prevents the duplicate tooling that previously drove up cost.

Shared AI Services as a Scalability Advantage

For an enterprise running dozens of client accounts and business units, every duplicated tool and inconsistent process is a hidden tax on growth, paid in cost, in service quality, and in how fast the organization can take on new accounts. Left unaddressed, that tax compounds as account volume grows. This case shows how a centralized, governed AI Shared Services model can remove that friction, cutting costs by more than 40 percent and scaling capacity 2.5-fold while standardizing governance across every account. This was not a tooling consolidation. It was a rebuild of how the organization scales AI-powered service delivery.

What's Next

If your organization runs AI operations across multiple accounts or business units without unified governance, Myridius can help you build a centralized Shared Services model that cuts cost and scales capacity without compromising service consistency.

Talk to a Myridius expert and send us a message.