Bhavitech sources real-world codebases, JIRA exports, Communication Threadss, and Figma files — with the relationships between them intact. Built for fine-tuning, evals, and benchmarks.
Trusted by teams building frontier models
Most data vendors sell isolated files. Bhavitech sells the relationships: a commit linked to its JIRA ticket, linked to the Communication Threads where it was discussed, linked to the postmortem if something broke.
This is what makes evals and fine-tuning more realistic — models trained on connected artifacts understand how real engineering decisions flow across tools.
commit a1b2c3d fix: payment retry logic
├── jira PROJ-1234 Payment timeout bug
├── slack #backend "retry should cap at 3"
├── figma Payment Flow v2
└── postmortem 2024-01-15 incidentUnlike competitors who sell synthetic or scraped data, we deliver authentic engineering artifacts that have been thoroughly evaluated for existing test coverage, real collaboration patterns, and production-ready quality.
Every repository in our dataset contains authentic code written by real engineers solving real problems. No AI-generated content, no synthetic examples, no scraped GitHub repos without context.
All repositories contain authentic engineering work from real projects. No generated or artificial code.
Repositories are evaluated for existing f2p and p2p test files and resolved test cases in PR merges.
We identify and flag repositories with excessive 'vibe coding' - code written without proper testing or structure.
Complete version control context with meaningful commit messages and logical progression.
Pull requests with real code reviews, discussions, and iterative improvements.
Continuous integration and deployment configurations showing real engineering practices.
Real engineering challenges require real engineering data
Six categories of engineering artifacts, delivered with metadata and clear licensing.
Production repositories with real contributors, commit history, PRs, and branching patterns. Not toy projects.
Tickets, epics, sprints, and comments. See how engineering teams plan, prioritize, and track work.
Engineering discussions, architecture debates, and decision threads. The context that never makes it into code.
Design-to-implementation artifacts. See how visual decisions translate into engineering requirements.
Business and product requirement documents. Understand the 'why' behind engineering decisions.
Incident reports and resolution threads. How teams debug, recover, and prevent recurrence.
Training models on realistic, multi-file engineering tasks
Building evals that test real-world reasoning, not just code completion
Improving code assistants with authentic engineering workflows
Studying how models handle complex, multi-step engineering decisions
Stack, domain, artifact type, volume, quality bar
Programmatic checks + human review against your spec
Metadata, clear licensing, and secure transfer
Get sample datasets delivered within days.