Show HN: Zeus – Claude Code for AI voice agents

Today, we’re excited to announce the launch of ThunderPhone v3, featuring Zeus - a first-of-its-kind fully agentic harness for voice AI.

Phone calls influence over a trillion dollars of consumer spending in the US alone every year, but the existing ways businesses handle phone calls have serious limitations.

Human-powered call centers are very expensive, making many use cases unaffordable and costing businesses billions in operations costs to handle routine matters over the phone.

Human-powered call centers also have significant other problems, including:

For many use cases, AI presents a much better solution. It costs a fraction of what call center labor does, can be scaled up and down at will, follows the same rules on every call, and can have its rules changed in a heartbeat.

These advantages have driven adoption of AI-powered calls for years, but the results have typically been unsatisfying for customers. Phone AI systems often mishear callers, answer in unhelpful ways, and conduct conversations in unnatural and unpleasant ways.

These problems were even worse a few years ago, and have been improved upon with the latest generation of generative-AI-powered systems, but there is still a lot of room for improvement because of the limitations of current voice AI stacks.

A large part of the problem has been the convergence of the industry on the “three-step-pipeline”: phone AI that handles calls by turning user audio into text with a transcription model, generating a response to that text with an LLM, and finally turning the LLM’s text response into audio with a TTS (text-to-speech) model.

Unfortunately, this approach has some very big problems, including the two biggest:

Because of these tradeoffs, it’s a very tricky task to pick the right transcription model and LLM for any given type of call powered by a 3-step pipeline. Some transcription models are faster but make more mistakes, and some struggle with particular languages or short utterances; each flaw matters more on some calls than others. And any 3-step pipeline based stack inevitably has some issues with audio understanding mistakes from the transcription model and instruction-following mistakes and other odd behavior from the LLM - even when the best possible transcription model and LLM for that task are selected.

The three-step pipeline, because of its turn-based nature, also makes it quite hard to make conversations feel natural for the user.

The newer architecture of speech-to-speech models is increasingly popular, but presents different tradeoffs. Speech-to-speech models typically feel much more natural in conversations, and sometimes (though not always) are more accurate at audio understanding than a three-step pipeline. However, speech-to-speech models still suffer tremendously from unreliable performance on instruction following and tool calls, and erratic behavior generally, making them risky for business phone calls.

At ThunderPhone, we’ve taken a different approach entirely, one that offers superior performance to either 3-step pipelines or to speech-to-speech models.

We do this by doing 2 things: running a complex, configurable architecture that uses significantly more than 3 models at once to power any given call, and by optimizing that system dynamically with AI to squeeze the best possible performance out of every call.

To begin with, we’ve found that getting AI phone calls to behave reliably takes a lot more models than a typical 3-step pipeline.

To ensure that callers are heard correctly, we typically run multiple, or even many, transcription models at once, and often also pipe audio directly to one or more LLMs - sending it as audio, and not as text, has its own disadvantages but can be a powerful complement to text-based transcripts.

On the LLM side, we also combine multiple models, doing things like running a faster and a slower LLM in the same conversation to combine speed and accuracy, as well as switching which model handles each conversation turn dynamically based on the state of the conversation at that time.

And finally, we also combine multiple models to optimize conversation handling performance for things like noise cancellation and identification, speaker separation, start and end of turn detection, and more - far exceeding the performance of any single model on those tasks as well.

We’re also increasingly fusing speech-to-speech models with other models on the side to combine the naturalness of the speech-to-speech models with the reliability of text-based models.

We also offer an extensive voice library covering 62 languages, and agents switch languages mid-call when the caller does. Platform voices are powered by Inworld and are the least expensive option; premium voices from Cartesia and ElevenLabs v4 Turbo cost a few cents a minute more. Voices trade naturalness against stability: the more natural-sounding ones are more prone to mispronunciations and hallucinations, especially on numbers and letters. Zeus helps you pick the right tradeoff for your calls.

We combine open-source and commercial models and fine-tune them where it helps.

Zeus configures every piece for your use caseThunderPhone v3Caller audioHearingTranscription modelTranscription modelTranscription modelAudio straight to LLM

ThinkingFast LLMSlower LLMModel picked turn by turnSpeech-to-speech

SpeakingPlatform voicesPremium voices62 languages, switches mid‑call

Agent voiceConversation handlingNoise cancellation and identification · Speaker separation · Start and end of turn detection

used on some calls

With these approaches, we’ve gotten AI voice agents to make significantly fewer mistakes than any three-step or speech-to-speech system we’ve seen to date.

ThunderPhone already holds the top score on Big Bench Audio, 99.4%, and handles thousands of real customer calls for businesses. More benchmarks are coming, including public versions of our own internal evals.

Moreover, with this complex architecture, we’re able to intelligently trade off between quality and reliability, latency (response speed), and cost of calls, optimizing for the needs of every business use case.

The downside of all of this, of course, is complexity. Optimizing all of these pieces is a lot of work! Until Zeus, we were doing this in two ways:

However, we realized that the pre-baked stacks were rarely the exact right fit for any given use case - just like 3 sizes of off-the-rack suit wouldn’t fit most customers perfectly.

The enterprise stacks we were hand-tuning, on the other hand, were incredibly high-performance for their use cases. The downside was that they took a lot of effort from us to get right, but we eventually realized that we were doing similar optimization processes for every enterprise stack.

We then realized that we could train an AI to do that job for us. Thus, Zeus was born.

With v3, Spark, Bolt and Storm are retired. Every agent now runs on one unified v3 architecture, and Zeus tunes it to your use case.

SparkOptimized for cost

BoltOptimized for speed

StormOptimized for intelligence

Plus custom-rolled stacks for enterprise customers

ThunderPhone v3

One unified v3 architectureCost

Speed

Intelligence

Zeus tunes it to your use case

Zeus is the fully agentic harness for voice AI: an AI agent that builds, tests and tunes your voice agents for you - think of it as Claude Code for your phone calls.

Zeus is able to configure the modular ThunderPhone architecture to your exact use case, maximizing performance of the voice agent for your exact business needs and priorities, whether that’s maximizing conversions, minimizing hangups, reducing the cost of each call, or some combination of all of the above and more.

Zeus does this with a “full closed loop” approach, where it helps you with the full life cycle of a voice agent:

You decide how much autonomy Zeus gets: have it ask before every change, or let it fire away.

We’ve been heavily inspired in how we’ve created Zeus by working closely with Claude Code and Codex as software engineers. Unfortunately, tools like Claude Code and Codex are significantly worse at configuring and optimizing voice agents than they are at building standard software, for a few reasons:

With Zeus, we’ve overcome these problems to achieve a Claude Code-like experience by giving Zeus access to several important sources of signal:

Instead of blindly ingesting marketing claims about the power of different AI models for voice AI applications, we run extensive internal evals on data from real calls, measuring which models for each part of the stack (transcription, LLM, etc.) perform best on REAL calls at any given time, and their tradeoffs against each other. (e.g. model A is faster, model B is smarter)

We’ve developed extensive practical knowledge about how to best optimize voice AI systems over the last 2 years of building them, and have also given Zeus extensive “training” on that knowledge in the form of prompting, guardrails, tools, etc.

We’ve outfitted Zeus with a variety of call-specific tools, including the ability to spot real issues in production calls, and the ability to simulate calls in a variety of challenging situations. The combination of these allows Zeus to “close the loop” and configure voice agents and confirm they actually work, the way a coding agent runs the tests before it calls code done.

Your own calls. Zeus learns from how your real conversations go, and backtests new configurations against your past calls before it proposes them.

With this approach, we’ve found Zeus to already be a much easier and more flexible way to get outstanding performance out of phone calls for a variety of use cases.

Among other advantages, allowing Zeus to optimize phone calls also leads to cost efficiency. Zeus takes into account both the least expensive stack necessary to get the performance needed on each use case, and the unit economics and business needs of each use case, meaning it’s no longer necessary to overpay for an overly powerful stack to get simple calls to work well.

Zeus automatically optimizes calls for cost using one of three strategies:

ThunderPhone uses cost-plus pricing: you pay our vendors’ prices for what each call uses, plus our hosting cost, plus our margin. Our margin is 17% of what you pay on calls that cost us 1¢ a minute or less, rising gradually to 25% on calls that cost us 15¢ a minute or more. We’re bootstrapped and profitable, not venture funded, so unlike many of our competitors, thin margins are a choice we can make without a VC breathing down our necks.

17%25%at 1¢/min or lessat 15¢/min or more0%10%20%30%0¢5¢10¢15¢20¢What a call costs us, per minute17%25%17% at 1¢/min or less · 25% at 15¢/min or moreOur per-minute prices don’t include telephony. We encourage you to bring your own carrier, which adds no ThunderPhone charge. We can also supply US phone numbers for $1/month each, with carrier minutes from Telnyx passed through at list rate (0.35¢ a minute inbound, 0.5¢ outbound) under the same cost-plus terms.

Zeus itself is free for the time being, but will eventually cost money. We intend to eventually release a lightweight Zeus that stays free, usage-based and/or subscription pricing for the full-fledged Zeus, and the option to bring your own OpenAI or Anthropic key. Simulated calls Zeus runs are billed like any other test call.

With the optimizations our system is able to make, as well as vendor price cuts that reach your calls automatically, we believe you’d be hard-pressed to save money on your calls compared to ThunderPhone pricing even if you roll your own stack from scratch.

We’re also able to offer even lower pricing for enterprise use-cases with more extensive optimization, including fine-tuning models for specific high-volume calls - prices can be 1c/min or even lower. We also offer other options to enterprise customers, including custom implementations and integrations, custom compliance and data residency needs,