Reshape enterprise R&D with AI

A code AI must first be usable, useful and governable in every engineer's hands — that's step one of embedding AI into the R&D loop.

An enterprise coding agent embedded in VS Code and JetBrains. Deployed inside your network, calling your own models and knowledge bases; enterprise know-how is packaged into custom agents and distributed centrally, ready to invoke from the IDE. Large and small models collaborate to balance performance against compute cost, and humans can interrupt, steer, and approve or reject at any point.

Testing is the first thing squeezed off the schedule — put an agent on the line so the quality gate holds.

TestAgent covers the testing lifecycle: it generates high-coverage unit tests from code logic, shifting testing left into the coding stage; assembles modules for integration checks that catch cross-module defects early; and layers on performance, security and compatibility system testing with quantified quality reports.

In aerospace, nuclear and quant work, the step from paper algorithm to production code hurts most — slow manual translation, silent errors, no traceability. Plug an agent in.

Math2Code pairs a rule engine with large models to break the format barrier: it precisely parses complex mathematical expressions in LaTeX, design documents and academic papers, then generates C / C++ / Python / Java in one step. Built-in logic-consistency and numerical-stability checks harden the output, and a unique mapping links every line of code to its source formula — trace from code back to math, or from math to code.