What is the AI-Ready Data Engine™?

The Data Engine autonomously transforms diverse sources of unstructured data into high-quality, training-ready data tailored to domain-specific AI applications. Rather than relying on generic or synthetic datasets, it refines and organizes user-supplied data into a format that AI models can learn from, enabling the creation of trusted models for enterprise AI applications.

How it works

Step 1: Upload & organize

A user starts by uploading key knowledge—examples could be company documents, procedural guides, industry regulations, or brand guidelines. The Data Engine processes this information, structuring it for seamless AI integration so the model learns from expertise rather than generic external data sources.

Step 2: Enhance & refine

The Data Engine continuously analyzes, improves, and refines the dataset to ensure clarity, consistency, and completeness. It autonomously structures data in a format that is optimized for AI comprehension and use, making it suitable for a wide range of AI applications beyond just chat models. This enhanced structure ensures that data is easily adaptable to various use cases, from knowledge retrieval to decision-making systems and more.

Step 3: Validate & ensure quality

Every dataset undergoes a rigorous validation process, ensuring AI is trained on reliable, well-structured data that truly reflects the knowledge and expertise of the user. This process reduces errors and bias, making AI more effective and dependable for specialized use cases across industries.

The diagram below depicts this process. Importantly, this process removes the often laborious and expensive task of manually collecting, curating, and annotating data.

Agentic workflows

A key feature of the Data Engine and its ability to automatically create data for a specialist model is the use of agentic tool-based workflows.

These workflows involve one or more agents and a base/generalist LLM that may not begin with any knowledge of the data.

In addition to the structured data, the agent has access to external tools such as web search APIs, knowledge graphs, calculators, code interpreters etc.

The core idea here is that given a task definition, the agent will draft out a "plan" of what steps it needs to take to solve the problem and in what order. The tools allow the agent to iteratively research, generate, critique, and refine its understanding of the data, thus allowing the creation of high-quality, domain-specific data for fine-tuning.

Human-in-the-loop

Another key feature of the Data Engine is the ability to participate in the process of data creation by bringing a human into the loop.

For example, subject matter experts can iteratively provide feedback on the model's understanding of the data, in the following manner:

1. Review the generated synthetic data
2. Trace the most influential portions of text in individual questions or the document's intermediate
   graphical representation that led to specific answers
3. Intervene and edit question-level or document-level text for clarity and accuracy
4. Regenerate synthetic data based on their edits from our agentic system

This gives SeekrFlow™ users the ability to intervene iteratively and refine the synthetic data generation to ensure optimal representation.