Docs overview

What Switchbench is, how it works, and what your team can validate.

Switchbench is a synthetic payment testing and governance environment for fintech teams, payment engineers, CI pipelines, and AI coding agents. It provides controlled scenarios, policy controls, approval gates, and replayable evidence so risky payment workflow changes can be validated before they reach production systems.

docs.switchbench.com
# Use the same synthetic environment for humans, CI, and agents. Inputs: scenario, request payload, policy context Outputs: outcome, timing, validation result, replayable evidence Scope: synthetic data only
Definition

Switchbench is a validation layer, not a live payment system.

What it is

A synthetic environment for testing payment workflow changes against approvals, declines, timeouts, reversals, duplicates, policy violations, and approval boundaries.

Who it is for

Fintech teams, payment infrastructure engineers, QA, architecture, risk, compliance reviewers, CI pipelines, and AI coding agents operating under human oversight.

What it is not

Not a processor, not a card network, not a live certification platform, and not a substitute for partner testing or production readiness approval.

How it works

A controlled path from generated change to review-ready evidence.

1. Send a request

Connect your application, CI pipeline, or agent-generated code to a Switchbench endpoint using REST or payment-message-style interfaces.

2. Select a scenario

Choose the synthetic outcome, timing model, policy behavior, approval gate, or edge case you want to validate.

3. Inspect the result

Review the returned outcome, timing, validation status, and evidence trail before merge, release, or partner testing.

Examples

Representative synthetic validation workflows.

Approval and decline handling

Validate authorization logic against successful approvals, insufficient funds, fraud blocks, and do-not-honor outcomes.

Timeouts and reversals

Test timeout handling, retries, reversals, and duplicate-event behavior before risky changes reach production systems.

Agent-generated mapping changes

Check generated field mappings, required data, and transformation logic against reusable scenario packs and message validation rules.