MathGPT Pro

Engineer uploads a PDF scan of the blueprint.

MathGPT extracts the vector data and material equations.

The model builds a Python script to run finite element analysis.

The result provides the safety margin and suggests structural adjustments.

Student snaps a photo of their handwritten work.

MathGPT identifies a sign error in the third line of the derivation.

The AI provides a hint about the conservation of energy principle.

The student corrects the work and the AI confirms the final value.

Analyst inputs the custom formula in LaTeX.

MathGPT performs a sensitivity analysis (Greeks) on the model.

It flags potential edge cases where the model breaks down.

Outputs a summary report for the risk management team.

Professor uploads the batch of scanned exams.

MathGPT identifies correct logic even when final answers differ due to early arithmetic errors.

The AI assigns partial credit based on a rubric.

Generates a feedback summary for each student.

Researcher uploads the CSV data.

MathGPT suggests the appropriate non-parametric test (e.g., Mann-Whitney U).

The AI executes the statistical test and generates a distribution plot.

Provides the final p-value and a written interpretation for the paper.

Developer pastes the code snippet.

MathGPT calculates the current time complexity.

The AI suggests a dynamic programming approach with better space complexity.

Provides the optimized code and a mathematical proof of the performance gain.

Parent asks for a lesson plan on sine waves in music.

MathGPT generates interactive audio-visual examples.

AI provides practice problems that adapt in difficulty.

Tracks the child's progress through a mastery dashboard.