The unit economics regarding customer support bots using frontier models from OpenAI and OpenAI won't ever make sense. Research labs reinvest gains into larger models that feature increasingly large token traces, while cheap labor is extremely prevalent in contact centers. As a result, I believe fine-tuning company-specific models (with less than 100 billion parameters) to accomplish 8-10 set tasks will become more and more common. Today, my project features an EffectTS REST API, an MCP Server of that REST API, a custom "AcmeBox Bench" evaluation inspired by Sierra's tau2-bench, a React-based chat widget, and a custom pipeline for continuously improving a Liquid LFM2.5-8B-A1B MoE self-hosted model to better serve AcmeBox, the HelloFresh competitor. All of this works creates a self-improving hardness for an 8 billion parameter model to eventually compete with much larger and more performant models.
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