Data Engine Overview

The SeekrFlow Data Engine is where raw content becomes structured, AI-ready data. It provides a streamlined interface for managing your files, preparing training data, and powering enhanced retrieval — all within one cohesive system.

The Data Engine contains two primary tabs that mirror the SeekrFlow UI:

Storage
AI-Ready Data

Once uploaded, files can be used across the platform — including in agents, evaluations, or to generate AI-ready training data.

🗂 Storage

The Storage tab is your starting point. This is where users upload and organize their files — whether they're PDFs, DOCX, JSON, Markdown, or others.

Each plays a key role in helping you manage and transform your data for downstream use in fine-tuning, agents, evaluations, or retrieval pipelines.

Key capabilities include:

File Upload & Management
- Upload documents directly into SeekrFlow and view file metadata, processing status, and available outputs.
Vector Store Creation
- From the same tab, users can create Vector Databases (VectorDB) — semantic indexes of your uploaded content. Once created, you can attach selected files to a vector store, enabling advanced search, retrieval, and grounding for agents or assistants.

👉 Visit the Files Storage and VectorDB Storage pages to learn more.

🤖 AI-Ready Data

The AI-Ready Data tab is where your content is transformed into structured datasets for training or retrieval workflows.

From this tab, you can create jobs that generate high-quality Q&A datasets, clean markdown, or structured outputs optimized for model alignment.

There are currently two types of AI-Ready jobs:

Standard Instruction (formerly Principle Alignment)
- A more traditional fine-tuning dataset — built from your files and aligned to general task or domain-specific instructions.
Context Grounded (RAFT) (coming soon)
- Creates Q&A training sets grounded directly in your source content. Ideal for domain-specific grounding and enhanced retrieval performance.

You’ll start by selecting the files you want to use. SeekrFlow will then generate a clean, context-aware Q&A parquet file — ready to be used in your model fine-tuning pipeline.

👉 Visit the AI-Ready Data page for setup steps, job types, and examples.

🔁 How It All Connects

Upload content in the Storage tab
Create AI-Ready Data Jobs using selected files
Build Vector Stores for retrieval, agents, and enhanced grounding
Use outputs across SeekrFlow to fine-tune models, power agents, or run evaluations

The Data Engine ensures every piece of content is traceable, inspectable, and interoperable — giving you complete control and visibility from raw file to fine-tuned output.