Synthetic Data Pipelines: Solving the Scarcity Problem

There was a time when the limiting factor for AI was compute. Today, it is data. We have effectively read the entire internet. To scale further, we need high-quality reasoning data—and that data does not exist in sufficient quantities in the wild.

The Problem with Real Data

Human-generated code on GitHub is often buggy, insecure, or poorly documented. Training a model on "average" human code results in an "average" coding assistant. To build a "Super-Coder," we need training data that is better than what humans typically produce.

Enter Synthetic Reasoning Chains

At Codewright, we use a technique called "Evolutionary Synthesis." We use our best models to generate thousands of solutions to a complex problem. We then run these solutions through a compiler and a set of unit tests.

The solutions that fail are discarded. The solutions that pass are then "critiqued" by another model for readability and efficiency. The survivors become training data for the next generation of models. This creates a flywheel effect where the model effectively "bootstraps" its own intelligence.

This approach allows us to create specialized agents for industries like aerospace or bio-engineering, where public training data is scarce or proprietary. We act as the data factory for your specific domain.