Git commits are not sufficient to train the next generation of frontier coding models. They are noisy, the majority of problems they capture are already trivial for state-of-the-art agents, and stratifying them by difficulty is highly nontrivial. What is required are curated corpora of verifiably difficult problem–solution pairs.
At Parsewave we work with some of the world’s leading AI research labs to design and deliver custom datasets of terminal-style coding and command-line reasoning tasks that push the limits of frontier models. These datasets are authored from the ground up by senior engineers across Central Europe, Australia, and the United States. Every datapoint is created under NDA, strict QA standards, and calibrated to lab-specific model pass rates.
How We Work
- Collaborate with your research team to define data scope, difficulty calibration, and model pass-rate targets.
- Produce tailored problem sets, solution traces, and reasoning annotations that match your technical requirements.
- Refine through multi-layer QA and unlimited revisions until the dataset meets research-grade quality.
- Provide rapid scaling through our global network of experienced engineers.
- Deliver 10–50 representative samples for internal validation and calibration before full-scale production.
Why It Matters
Recent advances and the availability of large but noisy datasets, such as algorithmic exercises and git commit logs, have not solved the challenge of training high-performing coding agents. The most effective approach is to use curated, real-world engineering tasks that represent a reliable ground truth. Parsewave datasets are designed for use in supervised fine-tuning, reinforcement learning, or as benchmarks to assess model performance.
We will provide a tiny sample corpus for evaluation to any AI lab researchers