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Train an agent on your use case. Use it like a model - over chat, realtime voice, or a phone number.

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Why realtime agents fail

Speech is solved. Telephony is solved. The layer that runs the conversation is where projects stall.

Prompts lose the plot

Every top model drops ~39% from single-turn to multi-turn - mostly unreliability, not capability. Take a wrong turn and the conversation never recovers. Prompt-tuning plateaus around 85%: nothing in a 100-page prompt is addressable, so nothing is testable.

Big models miss the beat

Humans swap turns in ~200ms; past ~700ms a caller hears a machine. The LLM is ~70% of that budget, and frontier models spend 1.1-1.4s before their first token - the whole budget, on one hop.

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What Unpod runs

Playbooks

Conversations as checkpoints and outcomes, not prose. Every step is addressable - so it can be simulated, fixed, and regression-tested.

Context layer

Each turn gets only the context it needs - so the agent stays fast on a third-party model, or faster on a fine-tuned ~1B SLM trained on your workflows. Swap either way without touching the conversation.

Sessions

Pass a session id and the agent keeps its state - across turns, and across calls.

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Use it like a model

Same trained agent, three ways in. Your orchestration, speech, and transport stay where they are.

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Start building

Getting Started

Your first request, in about 2 minutes.
Try itPlaygroundBuild and hear an agent in the browser. No account needed.

GitHub

Open source, MIT. Self-host the full stack.