Bhavitech helps teams source real-world engineering artifacts for AI training and evaluation, and helps enterprises embed AI into existing systems, workflows, and internal tools.
Trusted by teams building frontier models and enterprise AI systems
Bhavitech works across two connected layers. We help AI teams get realistic, linked engineering artifacts for training and evaluation, and we help enterprises connect AI to the systems where real work already happens.
That means better model inputs on one side, and more useful production AI on the other. The common thread is context: data, workflows, and system relationships that reflect how teams actually operate.
commit a1b2c3d fix: payment retry logic
├── jira PROJ-1234 Payment timeout bug
├── slack #backend "retry should cap at 3"
├── figma Payment Flow v2
└── enterprise_ai CRM copilot + workflow automationUnlike 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
AI-ready engineering artifacts plus enterprise AI implementation capabilities.
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.
We also help enterprises embed AI into their existing systems, workflows, and internal tools so teams can automate work, improve decision-making, and deploy practical AI in production.
Help teams search internal knowledge, answer operational questions, and work faster inside existing tools.
Embed AI into repetitive enterprise flows like support triage, document review, summarization, and approvals.
Connect AI to CRMs, ERPs, dashboards, document systems, and internal applications without rebuilding your stack.
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
We deliver datasets directly to your infrastructure — no manual downloads, no friction.
Direct delivery to your S3 buckets with IAM role-based access
Seamless transfer to GCS buckets for GCP-native teams
Secure delivery to Azure storage accounts
Structured data sharing via Snowflake data marketplace
Push datasets directly to your private HF repositories
Traditional encrypted file transfer for air-gapped environments
All transfers are encrypted end-to-end with access controls and audit logs.
Get sample datasets delivered within days.