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IndustryAI & GenAIArchitecture
International group (confidential)

100 AI use cases identified, thousands of person-days of gains: now someone has to build the platform that makes them possible

Before

100+

eligible AI use cases but no platform to industrialize them

After

100+

AI use cases identified

100+

AI use cases identified

Thousands of person-days

in potential productivity gains

3

full-time consultants, clear mandate, strike-team mode

The client, an international group operating in several countries in businesses with strict confidentiality constraints, had identified massive potential for acceleration through AI. The inventory brought to light more than 100 eligible use cases across teams, with several thousand person-days of productivity gains and a significant acceleration of services delivered to customers and internally. Leadership was clear: accelerate. But building a multi-agent orchestration platform in a sensitive environment, without creating dependency on a single vendor, is a major architecture challenge. Nobori Partners was brought in with a clear mandate and 3 dedicated full-time consultants.

THE STORY

The client is not taking its first steps with AI. The potential has been mapped and the will to accelerate is there: more than 100 eligible use cases across teams, several thousand person-days of potential productivity gains, and a significant acceleration of services delivered to customers and internally. What is missing is the platform that makes all of it possible.

The challenge: build a multi-agent harness with no vendor lock-in. Running an AI agent is not the hard part. The hard part is building the harness (the orchestration layer that frames, routes and governs the agents) to run dozens of them reliably, securely and interchangeably. The goal: an equivalent of Claude Cowork, but with no vendor lock-in and integrated into the client’s highly sensitive environment. In practice, that means an abstraction layer over the models (LangChain) so the LLM can be swapped without rewriting the agents, a deterministic orchestration engine with state management and error recovery (LangGraph), and a framework for long-running tasks that require planning and sub-agents (DeepAgents). The harness also routes inference according to data classification, manages tokens and costs at scale, and provides end-to-end observability to audit every decision an agent makes. The infrastructure is deployed as code with Terraform.

3 consultants in the lead, drawing on the client’s resources. This engagement is not staff augmentation. Three Nobori Partners consultants take the lead on the program and draw on the client’s resources to move fast: internal teams, existing infrastructure, CI/CD pipelines. Nobori does not rebuild in parallel: it leads, makes the calls and accelerates using what is already there. The executive committee tracks progress directly. This is not an IT project buried in a backlog; it is a strategic priority governed at the right level.

The platform MVP is delivered: an equivalent of Claude Cowork with no vendor lock-in, integrated into the client’s sensitive environment. The 100+ use cases now feed the roadmap. The potential productivity gains run to thousands of person-days, in services delivered to customers as well as in internal productivity.

OUR APPROACH

3 steps to get there

1

Inventory of 100+ use cases and quantification of gains

Exhaustive inventory of AI use cases across the group's teams and business units. More than 100 use cases mapped, each qualified by business value and productivity gain potential. The result: several thousand person-days of potential gains, both in faster services delivered to customers and in internal productivity. This work made it possible to prioritize the platform scope and justify the investment to the executive committee.

2

Architecture of a multi-agent harness with no vendor lock-in

The core technical challenge: build a harness (an orchestration layer that frames, routes and governs AI agents) without creating dependency on an LLM vendor. Running one agent is not the hard part. The hard part is building the foundation that runs dozens of them reliably, securely and interchangeably. That means an abstraction layer over the models (LangChain) so the LLM can be swapped without rewriting the agents, a deterministic orchestration engine with state management, checkpoints and error recovery (LangGraph), and a framework for long-running tasks that require planning and sub-agents (DeepAgents). The harness must also route inference according to data classification in a highly sensitive environment, manage tokens and costs at scale, and provide end-to-end observability to audit every decision an agent makes. The infrastructure is managed as code with Terraform to keep deployments reproducible and auditable.

LangGraphDeepAgentsLangChainPythonAzureTerraformFastAPIDocker
3

Strike-team delivery: 3 consultants in the lead, drawing on the client's resources

The engagement is structured as a strike team: 3 Nobori Partners consultants in the lead, drawing on the client's resources (internal teams, existing infrastructure, CI/CD pipelines) to move fast. Nobori does not rebuild in parallel: it leads, makes the calls and accelerates using what is already there. The executive committee tracks progress directly, which keeps strategy aligned and clears obstacles quickly.

RESULTS

The measurable impact

100+AI use cases identified
Thousands of person-daysin potential productivity gains
3full-time consultants, clear mandate, strike-team mode

More than 100 AI use cases identified and prioritized, representing several thousand person-days of potential productivity gains. The platform MVP (an equivalent of Claude Cowork with no vendor lock-in) is delivered and integrated into the client's sensitive environment. The 3 Nobori consultants in the lead drew on the client's teams and infrastructure to move fast.

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