Galaxia applications

Large Language Models (LLMs) hold great promise but struggle with issues like hallucinations, impacting their reliability. Smabbler mitigates these problems by enhancing LLMs with its proprietary knowledge graph technology.

Galaxia creates a knowledge foundation for the entire AI product lifecycle.

Beyond enhancing LLMs, our graph-based technology offers powerful solutions across a wide spectrum of AI and data processing applications. It focuses on improving data quality, extracting insights, and enhancing machine learning models.

Smabbler's solution	Benefit
Graph RAG as-a-service	Reducing hallucinations via extended context.
Text labeling for controlled LLM fine-tuning	Increasing model accuracy.
Guardrails for LLMs	Detecting unwanted or toxic content with LLM-independent output control.
Feature extraction from text for classic ML	Ensuring high performance in traditional ML models by extracting relevant features from text.
Graph-based embeddings	Improving LLM training by using high-quality, contextually rich data.
AI knowledge base	Building a foundation for AI leveraging graph and NLP.
Structured data for BI / process automation	Transforming text into quantifiable data, facilitating business intelligence and process automation.
Topic detection	Discovering latent topics and identifying relevant documents for targeted analysis.