The Calculator Paradox of Generative AI

If you cannot trust an LLM to multiply two 20-digit numbers, how can you trust it to perform a commercial due diligence?
The short answer: You can’t. (At least, not with a plain vanilla LLM).

We all know the disclaimer: "AI can make mistakes. Check important info." But if I have to manually double-check every calculation in a market sizing model, what is the point of using AI?

There is a better way.

Think about algebra. You wouldn’t ask an LLM to compute 834,921 * 9,102 in its "head." But, you would trust an LLM to write a line of Python code that solves that multiplication.

Why? Because you can inspect the code. You can verify the inputs. And once you trust the logic, the output is deterministic. You trust it like you trust a calculator.

At Aqmen AI, we asked: Can we do the same for Market Sizing?
We didn't just build another chatbot wrapper. We developed Lio, a neurosymbolic AI agent based on the first Formal Ontology for market sizing, grounded in the classical methodology of the Value Driver Tree.

Here is how Lio works:
1. Intermediate Representation (IR): Instead of guessing the answer, Lio translates your intent into a visual Value Driver Tree.
2. Human-in-the-Loop: You don’t check the math; you check the logic. Does the tree structure make sense?
3. Deterministic Output: Once you approve the Tree (the IR), Lio compiles it into a fully working Excel model.

The Trade-off: This approach relies on a defined formal language. If a specific type of analysis isn't in our language yet, Lio can't model it. But we see this as a feature, not a bug.
We are continually expanding the language's expressivity. But by constraining the agent to valid logical structures, we guarantee correctness.

We are turning the "Art" of market sizing into an "Exact Science."

The result? Instead of needing a disclaimer like: ❌ "Gemini can make mistakes, so double-check it." we can write ✅ "Lio’s logic is verified, so the math is perfect. No need to double-check it."

That is the power of Neuro-symbolic AI. This solution template goes beyond market sizing to any analytical domain where precision is non-negotiable, proving that the future of Enterprise AI lies not in better guessing, but in verifiable reasoning.