Reasoning models don't always say what they think. Ours does.
Check the fields and the queries behind each premium calculation.
Reviewers can see how a result was calculated from the application data.
Check the rules and the rows used for each eligibility result.
Generative AI is like an alien that read all the internet text and chose its own internal representation to "see" color. These representations are called dimensions and we don't know what they mean. The frequency of light could be its dimension, or something we don't understand. Neither do we know how they interpret the words we speak to it.
Humans see brown as red, green and blue through three cone types in our eyes. Prereasoner is trained the same way. Its dimensions are named after schema.org properties such as country and currency. So, the question and the answer can traced back to those properties in our model.
Generative AI predicts the most probable word that comes next. It can pick one word the first time and another word the second time. That makes them, human-like in chatbots. But, in a financial calculation, it means the same question can return a different number.
Prereasoner converts a question into named dimensions such "SUM" of "SALES" in "FRANCE". This is converted into an SQL query or Python code, deterministically. When it runs, the database or code computes the number. With the same data, you get the same query and the same answer.
| What a reviewer needs | Generative AI | Prereasoner |
|---|---|---|
| Explain why a decision was made | A generated explanation that may not be faithful | The derivation itself: SQL query, steps and rows |
| Reproduce the answer for an auditor | Can vary with sampling | Same query and answer with the same data and setup |
| See which fields drove the result | Not visible inside the model | Every column used is in the query |
| Know where a number came from | Written by the model | Computed by the database from your rows |
| Handle a question the data cannot answer | Tends to answer anyway | Asks you to clarify in supported cases |
| Keep regulated data in your own cloud | Depends on the vendor | Self-hostable in your own cloud project |
Prereasoner shows the query and rows behind each answer.