Definition
Hybrid AI
Deliberately splitting workloads across frontier models, self-hosted open source models, and fully sealed environments.
From the episode “Tokenomics Will Wreck Your Margins: The AI Model Routing Workflow That Cuts Spend”Hear it on the podcast1 episode
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Related terms in AI Fundamentals
Hallucination ToleranceThe acceptable error rate for AI outputs in a given system context. In consumer chatbots, high hallucination tolerance is acceptable — wrong answers are annoying but not dangerous. In healthcare eligibility determinations, medication routing, or financial compliance, the tolerance is zero — errors create legal liability, financial harm, or patient safety risks. Designing for zero hallucination tolerance requires architectural decisions (human-in-the-loop checkpoints, output traceability, auditability standards) rather than simply selecting better models. Softheon's target: 99.9999999% accuracy — exceeding air traffic control standards.Hallucination-Free AIAn AI system constrained to answer only from a specified data set, with no access to the broader internet or general training data. Eliminates invented or inaccurate responses by removing the source of uncertainty — the model can only cite what actually exists in the collection.Human-centered AIAn approach to building AI that puts people's needs, values, and wellbeing first throughout design and deployment.Humane AIAI designed to strengthen human connection and meaning rather than maximize engagement.HyperparameterA setting chosen before training begins, like learning rate or model size, that shapes how a model learns.InferenceWhen a trained model is put to work on new inputs, making predictions or generating answers. It is what happens each time you use an AI product.Inference CostsThe computational expense generated when an AI model processes user queries. High inference costs signal heavy, active user engagement.Inference Engine (AI Context)In the context of Loman.ai, a system that draws contextual inferences from every customer interaction and stores them as a persistent diner profile. Rather than simply transcribing what the caller says, the inference engine captures implicit signals: background noise suggesting children or a social context, dietary patterns suggested by order history, regular timing patterns, and other behavioral cues. These inferences are used to personalize future interactions (surfacing likely orders, suggesting relevant upsells) and will eventually enable personalized outreach. The term distinguishes this layer of contextual reasoning from simple speech transcription or menu lookup - it is the layer that makes the AI agent feel like it knows the customer rather than merely recognizing their phone number.