DATALENT owns private codebases across 20+ domains and languages. We extract 2000+ structured LLM training tasks from each one — PR review pairs, bug-fix sequences, code explanations, reasoning traces. Data your competitors cannot access because we own the source.
DATALENT owns the source. We run the extraction. You receive production-quality task datasets with no access to the underlying codebase required.
We own and maintain private codebases across 20+ domains — finance systems, healthcare platforms, logistics engines, developer tooling, and more. These are not scraped from GitHub. They are not synthetic. They are real proprietary codebases that no other data provider has access to. The exclusivity of the source is the product.
Our extraction pipeline scans each codebase systematically — pull requests, commit histories, bug reports, code reviews, and function documentation. For every valuable signal, we generate a structured training task: an instruction, the relevant code context, and a high-quality response. Each codebase produces a minimum of 2000 tasks across six task categories.
Every generated task passes automated quality checks — response completeness, code validity, instruction clarity — followed by expert human review. Tasks are delivered in JSONL format compatible with OpenAI fine-tuning, HuggingFace Datasets, and all major LLM training frameworks. Data cards, schema documentation, and version metadata included.
Each codebase yields tasks across all six categories. Buyers can purchase the full set or filter by category to target specific model capabilities.
Real PR diffs with expert review comments. The model learns to read code changes, identify issues, suggest improvements, and explain the reasoning behind review decisions — exactly as a senior engineer would.
Real bugs from production history paired with their fixes. Each task includes the buggy code, the error context, the fix, and an explanation of why the bug existed. Trains models to debug, not just pattern-match.
Complex functions, architectural patterns, and non-obvious implementation decisions paired with clear, accurate explanations at multiple levels of depth. Trains models to explain code to both technical and non-technical audiences.
Multi-step reasoning walkthroughs for complex code logic — algorithm analysis, performance tradeoff decisions, architecture choices. Each task preserves the complete chain of thought that leads to the conclusion. Critical for training code-reasoning models.
Real functions paired with comprehensive test suites written by the original developers. Tasks include both the generation instruction and the expected test output — including edge cases, boundary conditions, and error scenarios that only production experience reveals.
Before-and-after code pairs from real refactoring commits. Each task includes the original code, the improved version, and a structured explanation of what was improved and why. Covers readability, performance, maintainability, and design pattern improvements.
Every task is a self-contained training example with instruction, code context, and a verified high-quality response. Ready to load directly into your fine-tuning pipeline.
Any team can scrape GitHub. Only DATALENT can sell tasks from private codebases your competitors have never seen.
Our private codebases are not in any public dataset. Models trained on CodeData learn from code patterns, decisions, and problem-solving approaches that have never appeared in pre-training data — which means genuine capability improvement rather than memorisation of patterns the model already knows.
Public GitHub code skews toward tutorial repositories, academic projects, and clean examples. Our private codebases come from real production systems — with the messy architecture decisions, legacy constraints, and hard-won engineering judgments that real software contains. Models trained on this data perform better in real deployment environments.
The most valuable training signal in our tasks is not the code itself — it is the reasoning that accompanied real engineering decisions: why this approach was chosen over alternatives, what tradeoffs were considered, what the review caught that the author missed. This decision context is invisible in public code and only exists in private repositories with rich commit and review history.
A model trained only on Python web services will struggle with Go microservices or Java enterprise systems. Our codebases span 20+ domains — finance, healthcare, logistics, developer tooling, embedded systems, data infrastructure — and 15+ languages, giving your model exposure to the full diversity of real-world software engineering practice.
Every task response is verified by a senior software engineer before it enters the training set. We do not auto-generate responses and assume they are correct. Incorrect reasoning in training data is worse than no training data — it teaches the model to be confidently wrong. Our QA pass rate on code tasks is 99%, maintained by human expert review at the response level.
Tasks ship as JSONL in the instruction-response format used by OpenAI fine-tuning, Axolotl, LLaMA-Factory, and every major fine-tuning framework. HuggingFace Datasets compatible. Each delivery includes a data card documenting task distribution, language breakdown, domain coverage, and known limitations. Versioned for reproducible training runs.
Our private codebases span the languages and domains where production software actually lives — not just the languages that dominate tutorials.
Our private codebases are not available anywhere else. The LLM training tasks we extract from them are not reproducible from public data. Request CodeData before your competitors do.