Why Real-World AI Is Harder Than the Benchmark | EliseAI’s Mario Martone

EliseAI treats LLMs as one tool among many, building internal evals and mixed-model architectures because real-world scale punishes even 90%-accurate AI systems.

• Scale breaks accuracy: EliseAI handles ~5 million phone calls/month (~60 million/year); a 90%-accurate LLM means an unbearable volume of real failures affecting housing and healthcare.
• Not all-LLM: EliseAI mixes LLMs, traditional machine learning, and custom neural networks (e.g., for renewal-risk prediction) because LLMs struggle with determinism and probability modeling.

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