Data engine
Transform raw content into structured, AI-ready datasets for training and retrieval.
The data engine transforms raw content into structured, AI-ready data. It manages the complete data lifecycle from file ingestion through preparation for training and retrieval workflows.
Core capabilities
The data engine provides two primary functions:
Storage
Storage manages raw content through file ingestion and vector database creation. Files are uploaded, processed, and organized for downstream use. Vector databases transform these files into searchable knowledge bases through document chunking and embedding generation.
Learn more: Storage
AI-ready data
AI-ready data generates and transforms datasets, converting raw content into training-ready formats. These jobs produce structured outputs optimized for model fine-tuning and alignment, including standard instruction datasets and context-grounded datasets.
Learn more: AI-ready data
Data workflow
The typical data engine workflow:
- Upload raw content files to storage.
- Create vector stores for retrieval applications.
- Generate AI-ready datasets from selected files.
- Use outputs for fine-tuning, agent knowledge bases, or evaluations.
Integration points
Data engine outputs integrate across SeekrFlow:
- Fine-tuning – AI-ready datasets feed model training pipelines
- Agents – Vector stores power FileSearch tool for knowledge retrieval
- Evaluations – Structured datasets support model testing and validation
Updated 8 days ago