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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.

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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, with ingestion insights that provide real-time visibility into processing status and diagnostics (available through the API and SDK). Vector databases transform these files into searchable knowledge bases through document chunking and embedding generation.

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.

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Data workflow

The typical data engine workflow:
1
Upload raw content files to storage.
2
Create vector stores for retrieval applications.
3
Generate AI-ready datasets from selected files.
4
Use outputs for fine-tuning, agent knowledge bases, or evaluations.

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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
Last modified on June 23, 2026