Solutions · Argus QA

Autonomous regression testing for Banking

Autonomous regression testing for banking. Banks operate under Central Bank of Azerbaijan supervision and banking-secrecy rules, so customer data cannot go to foreign clouds.

Autonomous Regression Testing for Banking

Allmaz provides a self-hosted AI testing platform designed for the rigorous demands of the banking sector. By combining autonomous computer-use agents with a strict self-hosted architecture, we enable banks to maintain high-quality web applications while adhering to Central Bank of Azerbaijan supervision and banking-secrecy rules. This ensures that sensitive customer data and internal workflows never leave your controlled environment, removing the risks associated with external SaaS dependencies. Our approach replaces fragile selector-based testing with AI agents that observe screenshots and reason through objectives. By utilizing versioned YAML scenarios and a multi-role execution engine, the platform can simulate complex financial workflows—such as those involving creators, approvers, and suppliers—each operating in independent, authenticated sessions. This allows banking institutions to achieve deep regression coverage and detect product defects autonomously without compromising security or regulatory compliance.

Capabilities

Solving Banking Compliance and Quality Challenges

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Maintain strict data residency and banking-secrecy obligations via a fully self-hosted deployment of ten containers.

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Generate comprehensive audit and regulatory compliance evidence with persisted step-by-step reasoning for every action.

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Reduce manual overhead in detecting fraud and complaints through autonomous regression of complex, multi-role workflows.

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Ensure stability for high-volume support and collections portals using objective-based testing instead of fragile selectors.

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Maintain a clear chain of custody for test execution with detailed user, environment, and build stamp logging.

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Optimize operational costs through a two-tier model execution strategy that escalates to stronger models only when necessary.

Enterprise-Grade Testing Capabilities

Self-Hosted Architecture

Deployed as ten containers with a shared artifact volume, removing external SaaS dependencies to keep data within your infrastructure.

Multi-Role Workflow Validation

Simulate complex banking processes where creators, approvers, and suppliers each operate in fresh, independently authenticated browser sessions.

Deterministic Security

Logins are performed by scripts using an encrypted secret store, ensuring AI agents never handle raw credentials.

Independent Verification

Assertions are checked by an independent verifier rather than the agent performing the action, preventing self-confirmation bias.

Model-Agnostic Routing

Configure model selection and provider routing in the database to switch models without redeploying code.

The Autonomous Testing Process

1Define scenarios as versioned YAML objectives specifying roles, assertions, and budgets instead of coordinates.
2The agent observes a screenshot, states its reasoning, and takes one action per turn to progress toward the objective.
3A two-tier execution model uses a cost-effective model first, escalating only agent-side failures to a stronger model.
4The system records every step and build stamp, classifying failures as product defects, environment issues, or timeouts.
5Suspected product bugs are flagged in a ready-to-file report for human review before being submitted to Jira or Qase.io.

Frequently Asked Questions

How does the platform handle banking secrecy and data privacy?

The platform is entirely self-hosted, meaning no customer data is sent to foreign clouds or external SaaS providers, keeping you compliant with Central Bank of Azerbaijan regulations.

Does the AI automatically file bugs in our tracking system?

No. To maintain human oversight, the AI flags suspected bugs in a report; a human must review and file the defect to Jira or Qase.io.

How is the cost of AI model usage managed?

Every model call is metered and costs are aggregated from the call level up to the scenario, suite, and release level for full transparency.

Can this replace our existing Playwright scripts?

It is designed to complement Playwright. Use deterministic scripts for stable, simple flows and AI agents for long, multi-role, or frequently changing scenarios.

How are test cases imported and managed?

Test cases are imported verbatim from Qase.io without model rewrites. Qase test runs become Argus suites, and results are posted back to Qase with links to evidence, while API-tagged cases are skipped.

Secure Your Banking Infrastructure

Contact Allmaz today to implement autonomous, self-hosted regression testing that meets your regulatory requirements.

Request a demo