What challenges did NLB Bank face before AI implementation?

Scale GenAI and AI agents in a regulated banking environment without unapproved tooling, governance blind spots, auditability gaps, or unpredictable costs. Avoid shadow IT and preserve control for sensitive workflows such as AML, credit, and compliance.

What was the impact of the AI solution?

NLB deployed the platform and its first AI use case into production within 5 months. The bank reported an 80% reduction in AI use case implementation time and cost. The platform provides complete auditability and traceability and reduces security and compliance risk while enabling scalable adoption across departments.

Source

MicrosoftProductionEvidence: Medium50/100

NLB scales AI agents with enterprise agentic governance

Use case typeAI governanceUpdated Mar 17, 2026

Nova Ljubljanska Banka (NLB), Slovenia’s largest banking group, partnered with Adastra to deploy a centralized enterprise agentic AI platform for scaling internal AI agents under bank-grade controls. The platform standardizes agent design, approval workflows, role-based access, policy enforcement, dedicated experimentation/testing/production environments, cost controls, and observability while preserving model choice across cloud GenAI services. NLB says the approach enables safe, scalable AI adoption across departments with full traceability and predictable costs.

Organization: NLB Bank
Industry: Finance
Location: Slovenia
Published: March 2026

Reported outcomes

−80%

AI use case implementation time and costTime & speed

Strategic outcomes

Risk & complianceComplete auditability and traceabilityRisk & complianceReduced security and compliance riskSpeed & agilityFirst production use case delivered quicklyScale & capacityRepeatable model for department-by-department AI rollout

Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

AI use case implementation time and cost: 80% decrease

AdastraMar 17, 2026Partner storyExplicit claimMedium evidence strength

80% reduction in AI use case implementation time and cost required

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: NLB Bank
Provider: Microsoft
Maturity: Production
Linked source: Adastra

NLB deployed the platform and its first AI use case into production within 5 months

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1AI governance
2Agent orchestration
3Workflow automation

Scale GenAI and AI agents in a regulated banking environment without unapproved tooling, governance blind spots, auditability gaps, or unpredictable costs.
Avoid shadow IT and preserve control for sensitive workflows such as AML, credit, and compliance.

Adastra built a centralized enterprise agentic AI platform for NLB that standardizes how internal agents are designed, approved, and operated.
The platform includes role-based access, policy enforcement, identity-based approval through Entra ID, dedicated environments for experimentation, testing and production, cost monitoring with rate limits and spend caps, and observability through an admin portal and Grafana dashboards.
The setup also uses Adastra AGate to provide visibility, control, and cost management across AI models and agents.

Technologies

Microsoft Entra ID Azure AWS Grafana

NLB deployed the platform and its first AI use case into production within 5 months.
The bank reported an 80% reduction in AI use case implementation time and cost.
The platform provides complete auditability and traceability and reduces security and compliance risk while enabling scalable adoption across departments.

Architecture

Centralized enterprise agentic AI platform with governance controls, Entra ID-based identity and approvals, policy enforcement, environment separation for experimentation/testing/production, cost monitoring, rate limits, spend caps, and dashboard-based observability via Grafana and an admin portal; AI mission-control layer (Adastra AGate) above cloud GenAI services.

Sources & evidence1

Evidence: Medium50/100Evidence strength