ORPilot, an open-source LLM-for-OR platform, distinguishes itself with an Intermediate Representation (IR) that targets two production concerns: reproducibility and portability. Unlike academic prototypes that regenerate solver code for every data refresh, ORPilot stores the model’s mathematical structure in a solver‑agnostic, typed JSON schema. This design lets teams rerun, share, or migrate models without incurring extra LLM calls.

ORPilot’s IR comprises five top‑level sections: Sets, Parameters, Variables, Objective and Constraints. Sets reference a single sets.csv file, filtering rows by set_name to define entities like Workers or Tasks. Parameters link CSV columns to indexed data, explicitly declaring index columns to avoid mismatches. Variables encode decision types, while the objective and constraints are expressed as solver‑neutral symbolic trees.

By capturing the full mathematical definition rather than language‑specific code, the IR enables rapid scenario testing—such as swapping Gurobi for an open‑source solver or adjusting capacity limits—without rebuilding the model. ORPilot thus offers a production‑ready workflow where optimization pipelines remain consistent across environments and over time.

The IR’s explicit schema also validates data at generation time, catching missing rows or typo‑induced empty sets before they reach the solver. This pre‑emptive checking reduces costly debugging cycles and aligns optimization code with standard data engineering practices, making ORPilot a viable choice for enterprises seeking reliable, maintainable AI‑driven decision tools.