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Use cases

From DevSecOps to enterprise AI adoption, with measurable outcomes.

Industrializing DevSecOps (first use case)

DevSecOpsSecurityGovernance
The Problem

Security comes too late: checklists, exceptions, and end-of-cycle rework.

The Solution

Argy's first use case: standardized DevSecOps workflows codified in versioned modules, with approvals, auditability, and traceable execution.

Outcomes

Less rework, Explicit decisions, Measurable maturity

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Governing enterprise AI

AIGovernanceSecurity
The Problem

POCs multiply, API keys sprawl, and costs and risks are hard to control.

The Solution

A single LLM Gateway with quotas, audit, filters, and tenant-aware RAG to keep usage under control.

Outcomes

Controlled AI adoption, Full traceability, Vendor independence

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Building assistants and AI agents

AIAutomationGovernance
The Problem

Each team experiments with agents without a shared framework or data boundaries.

The Solution

Governed agents connected to RAG, orchestrated by reusable modules and shared guardrails.

Outcomes

Faster production rollout, Reusable workflows, Preserved confidentiality

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Launching an IDP for a scale-up

ScaleSelf-serviceStandardization
The Problem

A small platform team, high demand, and pressure to deliver without breaking production.

The Solution

Argy provides a self-service experience with ready-to-configure 'golden path' modules.

Outcomes

Faster time-to-value, Fewer Ops/SecOps tickets, Adopted standards

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Standardizing multi-team delivery

StandardizationGovernance
The Problem

Every team does it 'their way': pipelines, conventions, gates, environments... hard to govern.

The Solution

Versioned and configurable golden paths, applied per environment and per product.

Outcomes

More predictable releases, Reduced drift, Faster onboarding

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Reducing Ops/SecOps tickets

OperationsSelf-serviceGovernance
The Problem

Ops teams become a bottleneck: access, environments, recurring changes.

The Solution

Controlled self-service via modules + approval policies: standardized, automated, traceable requests.

Outcomes

Fewer manual requests, Better quality of service, Traceability

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Accelerating environment provisioning

ProvisioningStandardizationSelf-service
The Problem

Creating an environment is slow, and the result is never identical.

The Solution

Environment models (Dev/Staging/Prod) with schema-driven configuration and guardrails.

Outcomes

Faster provisioning, Reproducibility, Integrated controls

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Steering execution (SLOs, observability, FinOps)

OperationsObservabilityFinOps
The Problem

Lack of steering loops: reliability, costs, incidents. Rituals are not standardized.

The Solution

Dashboards (DORA, SLOs), drift detection, incident management, and AI quota steering (tokens).

Outcomes

Better reliability, Controlled costs, Continuous improvement

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Regulated banking & fintech

FinanceSecurityGovernance
The Problem

Strict audit requirements, manual validations, and a high compliance burden.

The Solution

Embedded policies, traceable approvals, and centralized execution evidence per tenant.

Outcomes

Continuous compliance, Fewer manual checks, End-to-end traceability

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Healthcare & sensitive data

HealthcareSecurityGovernance
The Problem

Strong GDPR constraints, segmented access, and protected internal data.

The Solution

Multi-tenant isolation, tenant-aware RAG, and an LLM Gateway with filtering and auditability.

Outcomes

Protected data, Controlled access, Governed AI usage

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Industrial & multi-site IoT

ManufacturingOperationsStandardization
The Problem

Heterogeneous sites, multiple environments, and complex governance.

The Solution

Standardized golden paths + on-site agents to execute locally.

Outcomes

Multi-site standardization, Consistent deployments, Governed local execution

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European SaaS

GDPR compliant & hosted in EU

No Lock-in

Built on open standards

API-First

Everything is automatable

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