This section provides conceptual information about SeekrFlow's core components and how they work together to enable end-to-end AI development.

Understanding components

SeekrFlow components are the building blocks of the platform. Each component serves a specific purpose in the AI development lifecycle, from preparing data to deploying production models. Understanding how these components work—both individually and together—helps you design effective AI solutions.

Core components

Agents

Configurable AI systems that reason through problems and execute tasks autonomously. Agents combine models and tools to handle everything from simple workflows to complex, open-ended objectives.

When to use: Building AI systems that need to make decisions, use tools, and take actions to accomplish goals.

Learn more: Agents

Data engine

Transforms raw content into structured, AI-ready datasets for training and retrieval. Includes file storage, vector databases, and automated data preparation workflows.

When to use: Preparing your data before fine-tuning models or creating retrieval systems for agents.

Learn more: Data engine

Fine-tuning

Automates the creation of training datasets to adapt pre-trained models to specific domains or use cases. Supports both instruction fine-tuning and context-grounded fine-tuning.

When to use: Customizing model behavior for domain-specific knowledge or specialized tasks.

Learn more: Fine-tuning

Deployments

Launch, monitor, and manage model endpoints for real-time inference. Provides deployment automation and production monitoring dashboards.

When to use: Making trained or fine-tuned models available for production use.

Learn more: Deployments

Explainability

Understand model outputs through influential training data and transparent reasoning. Surfaces which training examples contributed to specific model responses.

When to use: Debugging model outputs, auditing decisions, or building trust in AI systems.

Learn more: Explainability

Content moderation

Evaluate text safety and brand risk with specialized moderation models including Seekr ContentGuard and Meta Llama Guard.

When to use: Assessing content safety, brand suitability, or policy compliance.

Learn more: Content moderation

How components work together

A typical AI development workflow connects multiple components:

1. Data engine – Upload and prepare your source data
2. Fine-tuning – Train models with your prepared data (optional)
3. Deployments – Deploy trained models for inference
4. Agents – Build AI systems using deployed models and tools
5. Explainability – Understand and validate model outputs
6. Content moderation – Ensure outputs meet safety standards

Not every workflow uses all components. Simple use cases might only use agents with base models, while complex enterprise applications might leverage the full platform.