Source code is essential training data for AI coding models. But code alone only shows the outcome of engineering work—not the context, trade-offs, collaboration, and operating knowledge that led to it.
Real software engineering happens across an entire organization. A ticket becomes a plan; a plan becomes a design; a design becomes a pull request; and the pull request evolves through review, testing, release, and sometimes an incident. To train and evaluate agents that can work in that environment, models need access to the surrounding engineering artifacts as well as the codebase itself.
What we provide
We source and curate private production codebases alongside the artifacts that explain how those systems are built, changed, and maintained. This creates richer, more realistic datasets for training and evaluating AI agents across the software development lifecycle.
Codebases and repository context
The foundation is real production code: repository structure, source files, configuration, tests, build systems, dependencies, and version history. Models can learn to navigate multi-file systems, understand local conventions, trace dependencies, and make changes that fit the existing codebase.
Pull request artifacts
Pull requests capture how teams turn an idea into a safe, reviewable change. We can provide PR descriptions, review threads, inline comments, code diffs, and the surrounding discussion. These artifacts help models learn to explain implementation choices, identify risks, respond to feedback, and improve a change before it ships.
Planning and design documents
Product specs, design documents, and architecture decision records reveal the intent behind the implementation. They show requirements, constraints, alternatives considered, and the reasons a team chose one approach over another—exactly the context an agent needs to reason about a proposed change instead of merely generating code.
Sprint and delivery documents
Engineering work is planned and delivered in increments. Sprint plans, backlogs, tickets, retrospectives, and release notes connect strategic goals to concrete work. They give models examples of scoping, prioritization, progress tracking, and the operational reality of getting software released.
Product-side communication
Requirements are rarely static. Email threads from product teams often document scope changes, clarifications, approvals, and decisions that shape an implementation. Including this communication helps models understand the product context that sits behind an engineering request and recognize when a requirement has changed.
Meetings and issue history
Meeting transcripts, notes, and issue tracker history preserve the reasoning that may never appear in the final code. They can show how a team investigated a problem, narrowed down a root cause, resolved ambiguity, or coordinated across functions. This is valuable context for training agents to ask better questions and follow an investigation over time.
Operational working artifacts
Software does not end at deployment. We also work with artifacts such as runbooks, postmortems, diagrams, and dashboards. These materials expose the operational side of engineering: how systems are monitored, how incidents are handled, how reliability lessons are captured, and how teams communicate system behavior.
Why the full context matters
A capable engineering agent must do more than produce a patch. It needs to understand what is being requested, why it matters, what constraints apply, how prior decisions affect the solution, and how success will be verified. The relationship between these artifacts is what turns disconnected documents into a useful representation of real engineering work.
- Codebases show what exists today.
- Plans and product communication explain what needs to change and why.
- Pull requests and reviews show how teams validate and refine implementation.
- Tickets, meeting notes, and issue history capture investigation and decision-making.
- Runbooks and postmortems show how systems are operated and improved after release.
Together, these sources support tasks that demand repository navigation, requirements reasoning, implementation planning, code review, debugging, incident response, and evaluation grounded in actual workflows—not simplified, synthetic prompts.
Built for the data you need
Every organization has a different target capability. Some teams need models that can review pull requests; others need agents that can work from product requirements, investigate production failures, or navigate a large codebase safely. We curate exclusive datasets and generate custom tasks from the artifacts most relevant to those objectives.
The next generation of AI coding models will not be trained on code in isolation. They will learn from the complete record of engineering work: the code, the decisions behind it, the collaboration around it, and the operational knowledge that keeps it running.
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