Human-authored. Expert-verified. Reproducible. Difficulty-graded reasoning data with verified correct answers — the signal that makes a model measurably better, and that scraped or synthetic data can’t provide. Built for frontier labs, foundation-model teams and sovereign AI programmes.
Reproducibility
Our models, sample datasets and held-out numbers are public. Download them, run them on your own inputs, and reproduce what we report.
The fine-tuned models behind our results are released openly, so you can run them on your own inputs.
View the fine-tuned models →A representative sample of each dataset is available to download, mirroring the full structure and quality.
Download a sample →Every case study shows the held-out test set, the metric, and the exact comparison conditions.
Read the research →The fundamental difference
Synthetic data amplifies what a model already knows. Scraped web data is unverified and structurally incoherent. Neither gives a model the difficulty-graded, multi-step reasoning signal it needs, and neither carries the verified correct answers that make verifiable-reward training (RLVR) work.
Our human-authored worked solutions contain the expert reasoning explicitly: the thinking that produced the answer, not just the answer. The output is structured, pipeline-ready training data.
The scale behind it
Who we build for
Verified-answer reasoning corpora for RLVR, SFT instruction pairs, and multimodal data, at a quality synthetic generation cannot reach.
Domain corpora with the conceptual depth and structured reasoning chains production models require, grounded in verified expert knowledge.
Curriculum-verified, Indic-script data across 8 scripts, aligned to national AI infrastructure goals.
Dataset catalogue
Mathematics and science reasoning, foundational to advanced, with explicit step-by-step worked solutions and verified answers.
Download sample →Structured corpora across 8 Indic languages, curriculum-graded and expert-authored.
Download sample →Labelled diagrams with grounded question-answer pairs for vision-language training and evaluation.
Download sample →Stay close to the work
A low-volume research update for ML teams — new results, new datasets, and what we are learning. No marketing.
Two ways in
Most teams begin by trying a sample on their own inputs. When you are ready, you talk to the people who built the data, not a sales desk.
Tell us where to send it and what you are working on. The sample arrives by email.
For scoping a licence or a custom dataset. A few more details so we can come prepared.