> ## Documentation Index
> Fetch the complete documentation index at: https://docs.valmi.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Problem Statement

> The challenges AI platforms face with billing and payments

## The Billing Challenge

You've built something amazing. Your AI agents are solving real problems, customers are using them, and you're ready to scale. But there's a problem: **traditional billing systems weren't built for what you're doing.**

### What Traditional Systems Can't Handle

* Systems like Stripe Billing or Zuora don't understand tokens, or outcomes
* They're built for simple subscriptions and straightforward usage patterns
* Your world is more complex

### What You Actually Need

* Track LLM costs that vary by model and provider
* Attribute costs to specific agents and customers
* **Price based on outcomes**—charge for successful hires, qualified leads, or actual conversions—not just raw usage
* Understand which parts of your business are actually profitable

### The Outcome-Based Pricing Challenge

Traditional billing systems force you to charge for usage (tokens, API calls, compute time). But your customers care about **outcomes**:

* A successful hire, not the number of resumes reviewed
* A qualified lead, not the number of emails sent
* A completed transaction, not the number of API calls

You need to bill for what matters—the value you deliver, not the infrastructure you use. This requires tracking outcomes, attributing costs to them, and pricing accordingly. Most systems can't do this.

### The Cost of Flying Blind

Without proper cost attribution:

* You can't tell which agents make money
* You can't identify which customers are profitable
* You don't know how your margins change as you scale
* You end up building custom solutions that take months and cost a fortune
* Your team gets pulled away from building great AI products

**The result?** You spend more time wrestling with billing infrastructure than building features your customers want. That's the problem we're solving.