GEO (Generative Engine Optimization) is optimizing content for AI-powered search engines like ChatGPT, Perplexity, and Google AI Overviews. Unlike traditional SEO which focuses on keywords and backlinks, GEO focuses on clear, authoritative, structured content that AI systems can understand and cite.
AI consulting focuses on strategy, architecture, and implementation of machine learning systems. This includes identifying where AI creates value, designing data pipelines, building predictive models, and integrating AI into existing workflows. It's not just writing code—it's solving business problems with intelligent systems.
RAG (Retrieval-Augmented Generation) is an AI architecture that combines a knowledge base with a language model. Instead of relying only on what the AI was trained on, RAG retrieves relevant documents from your data and uses them to generate accurate, grounded responses. This is how you build AI that actually knows your business.
It depends on complexity. A focused pilot—like anomaly detection for a single data stream—can be built in 4-8 weeks. More comprehensive systems involving multiple data sources, custom models, and integrations typically take 3-6 months. The key is starting with a well-defined problem and iterating.
Not always. Modern approaches like transfer learning and few-shot learning can work with limited data. For many business applications, you need enough data to represent the patterns you want to detect—sometimes that's thousands of records, sometimes hundreds. The quality and relevance of data matters more than raw quantity.
Elysium Fields AI works across industries where data-driven decisions matter: environmental monitoring, manufacturing, professional services, healthcare, finance, and technology. The underlying patterns—anomaly detection, knowledge retrieval, predictive analytics—apply broadly. Domain expertise is built through collaboration.
Yes. Solutions can be architected to keep sensitive data local using on-premise models and private infrastructure. Not everything needs to go to the cloud. The right approach depends on your security requirements, regulatory constraints, and performance needs.
Predictive analytics uses historical data and machine learning to forecast future outcomes. Instead of reacting to problems after they occur, you anticipate them. Applications include equipment failure prediction, demand forecasting, anomaly detection, and risk assessment.