Can You Really Apply AI to Calculate Commissions?

We’re building techniques that could have far-reaching implications for FinTech, and while the progress is exciting, it comes with hard-earned lessons. Here are a few:

1️⃣ Data Structure Is Everything

AI thrives with unstructured data—creative writing, conversation, brainstorming. Commission calculations are the opposite: structure is non-negotiable.

Not just any structure, but one tailored to the exact problem. If your inputs aren’t organized so the AI can reliably parse and operate on them, you won’t get accurate or explainable results.

We’ve invested heavily in schema and pipelines that enable both precision and transparency—two things our customers demand.

2️⃣ Know What an LLM Is (and Isn’t)

Interacting with an LLM can feel like talking to a superintelligent colleague. But it’s not magic—it’s statistics.

An LLM predicts the next word based on patterns in data. That’s powerful, but in math-heavy contexts like commissions—where there’s only one correct answer—language’s flexibility becomes a liability.

Our approach: layer in safeguards that steer the model toward precision and determinism.

3️⃣ AI Can Write Code—But Guardrails Matter

AI-assisted code generation has been a game changer for speed and prototyping. But enterprise-grade software needs to be auditable, maintainable, and secure.

This isn’t about removing humans from the loop—it’s about augmenting engineers with tools that boost productivity while keeping quality high.

Programming has evolved from assembler → C++ → Java → Python → AI. It may feel like AI is “speaking English,” but you still need clear protocols for generating production-ready code.

💡 Let’s Build the Next Generation of FinTech If you’re applying AI to enterprise or FinTech problems—especially where precision and auditability are non-negotiable—I’d love to compare notes.

What challenges have you faced? What’s worked well for you? Let’s share strategies and push this space forward.

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