Apollo is an Agentic Language Model, built with a neuro-symbolic architecture to enable continuous evolution from human feedback. Apollo unlocks fine-tuning for AI agents, resulting in ever-improving performance for conversational agents.
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About Apollo
Apollo is an Agentic Language Model, replacing the transformer with a neuro-symbolic architecture to enable conversational agents that can learn and evolve from human feedback. Apollo unlocks fine-tuning for AI agents, resulting in ever-improving performance for conversational agents that can work on behalf of any company. Developed over the past six years in collaboration with 60,000 human agents, Apollo outclasses traditional LLMs across agentic use-cases. Apollo is trained on a neuro-symbolic language that constitutes both descriptive and procedural ("agentic") data, replacing autoregressive inference with neuro-symbolic reasoning, which is better suited for agents. This method relies on obtaining a structured interaction state, achieved through sensory data collected to produce a symbolic, parameterized representation of each interaction.
Apollo enables companies of any kind to deploy fine-tuned agents, versions of Apollo that are fine-tuned on a specific task, and can continuously improve with human feedback. They further feature fine-grained control, the capacity to adhere to the deploying company’s policies and a white-box view of their decision-making and reasoning. They offer superior tool use, steerability and overall performance over LLM Agents.
