Use cases
From DevSecOps to enterprise AI adoption, with measurable outcomes.
Industrializing DevSecOps (first use case)
Security comes too late: checklists, exceptions, and end-of-cycle rework.
Argy's first use case: secure-by-default workflows codified in modules, with policies, gates, and runbooks.
Less rework, Explicit decisions, Measurable maturity
Governing enterprise AI
POCs multiply, API keys sprawl, and costs and risks are hard to control.
A single LLM Gateway with quotas, audit, filters, and tenant-aware RAG to keep usage under control.
Controlled AI adoption, Full traceability, Vendor independence
Building assistants and AI agents
Each team experiments with agents without a shared framework or data boundaries.
Governed agents connected to RAG, orchestrated by reusable modules and policies.
Faster production rollout, Reusable workflows, Preserved confidentiality
Launching an IDP for a scale-up
A small platform team, high demand, and pressure to deliver without breaking production.
Argy provides a self-service experience with ready-to-configure 'golden path' modules.
Time-to-value in weeks, 60% fewer Ops tickets, Adopted standards
Standardizing multi-team delivery
Every team does it 'their way': pipelines, conventions, gates, environments... hard to govern.
Versioned and configurable patterns, applied per environment and per product.
More predictable releases, Reduced drift, Faster onboarding
Reducing Ops/SecOps tickets
Ops teams become a bottleneck: access, environments, runbooks, recurring changes.
Controlled self-service via modules + policies: standardized, automated, traceable requests.
Fewer manual requests, Better quality of service, Traceability
Accelerating environment provisioning
Creating an environment takes days, and the result is never identical.
IaC baselines + environment models (DEV/UAT/PRD) with parameters and guardrails.
Provisioning in hours, Reproducibility, Integrated controls
Steering execution (SLOs, observability, FinOps)
Lack of steering loops: reliability, costs, incidents. Rituals are not standardized.
Runbooks and SRE/FinOps baselines in the catalog, with indicators and ownership.
Better reliability, Controlled costs, Continuous improvement
Regulated banking & fintech
Strict audit requirements, manual validations, and a high compliance burden.
Embedded policies, traceable approvals, and centralized execution evidence per tenant.
Continuous compliance, Fewer manual checks, End-to-end traceability
Healthcare & sensitive data
Strong GDPR constraints, segmented access, and protected internal data.
Multi-tenant isolation, ACL-based RAG, and secure-by-default workflows.
Protected data, Controlled access, Governed AI usage
Industrial & multi-site IoT
Heterogeneous sites, multiple environments, and complex governance.
Standardized golden paths + on-site agents to execute locally.
Multi-site standardization, Consistent deployments, Automated run ops
European SaaS
GDPR compliant & hosted in EU
No Lock-in
Built on open standards
API-First
Everything is automatable
Ready to turn AI into an enterprise operating system?
Share your context (toolchain, constraints, org). We’ll propose a pragmatic rollout that makes AI governed, scalable, and sovereign.