Git commits are not sufficient to train the next generation of frontier models and agents. This data is often noisy, the majority of problems it captures is already easily solvable for state-of-the-art systems, and stratifying this data by difficulty is highly nontrivial. Frontier models require curated corpora of verifiably difficult problem–solution pairs and increasingly, high-signal traces, evaluation data, and professional-grade supervision that reflects real deployment constraints.
At Parsewave we work with some of the world’s leading AI research labs to design and deliver custom datasets across a wide range of task formats. Recent formats we have delivered include:
- Terminal-style coding and command-line reasoning tasks
- ML-heavy engineering tasks
- Legacy-language and legacy-stack task suites
- End-to-end solution traces (trajectories)
- Expert evaluations and preference data across dozens of occupations, and many more
These are representative examples, not an exhaustive list. We regularly design new formats to match a lab’s exact research needs. Every datapoint is authored by seasoned professionals, primarily senior software engineers, created under NDA, and calibrated to lab-specific model pass rates and acceptance criteria.
How We Work
- Initial sample turnaround after an email or call is typically within 24 hours, enabled by an active, diverse community of contributors and reviewers we can mobilize quickly.
- Collaborate with your research team to define data scope, task formats (for example traces, evaluations, legacy suites, terminal-bench style tasks), difficulty calibration, and model pass-rate targets.
- Produce tailored problem sets, solution traces and trajectories, structured evaluations, and reasoning annotations that match your technical requirements.
- Refine through multi-layer QA, reproducibility checks, and reviewer consensus, with unlimited revisions until the dataset meets research-grade quality.
- Provide rapid scaling through our global network of experienced engineers and domain specialists, with a focus on senior software professionals.
- Deliver 10 to 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 agents. The most effective approach is to use curated, real-world tasks that represent reliable ground truth, including traces of expert behavior, rubric-based judgments, and tasks spanning both modern and legacy engineering environments. Parsewave datasets are designed for use in supervised fine-tuning, reinforcement learning and preference learning, and as benchmarks to assess model performance.
We will provide a tiny sample corpus for evaluation to any AI lab researchers