Smabbler’s graph-powered platform boosts AI development by transforming data into a structured knowledge foundation - for training, validation, and testing of AI models.
The solution is powered by Galaxiaâ„¢ - a first of its kind graph language model that combines Graph and NLP. Galaxia augments AI by injecting relations and context into data.
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.
Graph-powered automated labeling boosts fine-tuning by 10x. High-quality data enables to F1 score improvement by 2.5x.
Source of labels independent from LLMs
Full control over labeling changes
Easy to expand topical knowledge
Galaxia enables quick semantic and contextual validation of user queries and LLM output to support ethical and safe AI applications.
Result transparency
Full control over changes
Topics, sentiment, context and their co-occurrence monitoring
Easy to expand topical knowledge
Quick ML model deployment
High ML model performance
Broader context for AI models
Semantics and similarities
Easy content navigation for chatbots
Knowledge for AI models
Context enrichment
Semantics, similarities and metadata
Applicable to multiple domains
Quantifiable and consistently formatted data
22 languages out-of-the box
Easy to extend to other languages
Applicable to topics, sentiment, context and their co-occurrence
22 languages out-of-the box
Easy to extend to other languages
Check out our ML example on Hugging Face: .
