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The problem: working software, missing context.

Imagine a client with a business-critical C# application maintained by several developers over many years. Orders still process, but documentation is thin. Before moving to Azure, the team needs to know which rules must survive the transition.

A small pricing method reveals the challenge: preferred customers receive a discount, but the reasoning behind that decision is not documented. Rewriting it without understanding it could change customer invoices.

AI helps ask better questions.

With client-approved tools and permitted code, AI can summarize the method, identify dependencies, and suggest tests. An engineer checks those suggestions against the source, calling code, and business requirements. AI cannot tell us whether an undocumented rule is still the right policy.

1. Inspect the existing rule

public decimal CalculateTotal(Order order)
{
    var total = order.Items.Sum(
        item => item.Price * item.Quantity);

    if (order.Customer.IsPreferred)
        total *= 0.90m;

    return total;
}

The code applies a 10% discount to preferred customers. It does not establish tax, rounding, or refund policy; those questions need further investigation.

2. Capture behavior before changing it

[Theory]
[InlineData(true, 90)]
[InlineData(false, 100)]
public void PreservesExistingPricing(
    bool preferred, int expected)
{
    var order = MakeOrder(
        preferred, price: 50m, quantity: 2);

    Assert.Equal((decimal)expected,
        new PricingService().CalculateTotal(order));
}

Abbreviated xUnit example: MakeOrder is a test fixture helper; Order and PricingService represent the application's existing types.