Use Cases

Building high quality, up-to-date data ready for use in decision making models

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  Training

  Training an AI model involves exposing it to large datasets, enabling it to learn patterns and perform specific tasks accurately by adjusting its internal parameters

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  Fine Tuning

  Fine-tuning involves adapting a pre-trained model by retraining it on your specific data, improving accuracy and performance for your unique application or task

   

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  RAG

  Integrating external knowledge sources, to enable accurate, context-rich AI responses that combine pretrained models with dynamic, real-time information retrieval

Using high quality data to make the optimal inferences and decisions

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  Inference LLMs

  Process of generating a response from a trained LLM by processing user queries or prompts, delivering context-aware, intelligent, and relevant outputs

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  Reasoning LLMs

  Analyze and connect information to interpret context, draw logical conclusions, and generate coherent, task-relevant outputs for complex decision-making

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  Agentic AI

  Autonomous systems that perceive their environment, make informed decisions, and take goal-directed actions with minimal or no human intervention