Dev AI Training Pipeline · Live GitHub Sync
Connect Repo.
Train At Scale.
Connect your GitHub account to sync all your real private and public repositories via official GitHub API, upload datasets and codebases under 5GB, and transmit jobs directly to the Nirvyuh Private Limited Engineering team for model training.
Workspace Console
Dev AI Training
Ingestion Portal.
Connect your authentic GitHub account, stage dataset files or codebase archives, and define your architecture requirements.
🛡 Supabase Enterprise Data Vault & Dispatch
- Direct Supabase Storage: High-bandwidth direct upload stream up to 5GB into
enterprise-datasetsbucket. - PostgreSQL Live Recording: Every uploaded video, image, codebase, and developer phone number is committed to
public.dataset_submissions. - Secure Dispatch: Fast transmission pipeline for Nirvyuh Private Limited's high-speed GPU training clusters.
- Data Encryption: Payloads are protected in transit with TLS 1.3 and stored with AES-256 encryption.
📋 Nirvyuh Private Limited Received Submissions Vault (Internal View)
This real-time vault tracks every customer submission, uploaded data payload (<5GB), attached description, developer email, and direct phone number.
| Job ID | Timestamp | Developer, Email & Phone | Domain / Category | Staged Data / Files | Payload Size | Description & Notes | Actions |
|---|---|---|---|---|---|---|---|
| No submissions received yet. When any developer submits above, their data appears here instantly. | |||||||
Ingestion Standards
Enterprise Data &
Storage Specifications.
Designed for high-throughput AI engineering teams, autonomous systems, and enterprise data officers.
5GB Direct Upload Window
Direct browser-to-vault streaming allows zip bundles, raw codebases, JSONL instruction pairs, and columnar Parquet tables under 5GB to be validated with SHA-256 integrity checks in seconds.
Live GitHub API Synchronization
Real GitHub API integration via Personal Access Tokens with repo scope gives genuine access to your full catalog of private company repositories, commit trees, and branches.
Dataset Curation & Prep
Our engineering team performs automated tokenization, deduplication, synthetic dataset augmentation, and outlier cleaning prior to starting distributed multi-GPU training jobs.