When AI Becomes the Sales Dialer: Inside Muse’s Paid Outbound Calling Engine

Introduction

Muse, the conversational‑AI startup known for its chat‑first assistants, announced a bold expansion: a paid outbound‑calling capability that can place real‑time phone calls to U.S. businesses. At first glance the offering looks like a simple “AI‑dialer‑as‑a‑service,” but under the hood it stitches together several high‑throughput subsystems—SIP signaling, PSTN gateways, large‑language‑model (LLM) inference, neural text‑to‑speech (TTS), automatic speech recognition (ASR), and a stateful dialogue manager. In this editorial we de‑construct each layer, surface the performance trade‑offs, and expose why many engineering teams stumble when they try to replicate Muse’s architecture.

Deep Architectural Breakdown

1. Telephony Transport Layer

Muse’s outbound engine is built on the Session Initiation Protocol (SIP) for call setup and Real‑Time Transport Protocol (RTP) for media streaming. The service does not own a physical PSTN connection; instead it leverages SIP trunking providers (e.g., Twilio Elastic SIP Trunking, Bandwidth, or Vonage) that expose a SIP‑to‑PSTN gateway. The flow is:

1. Call Initiation – Muse’s orchestration service sends a SIP INVITE to the trunk provider, embedding a
   Copy

   Contact

   header that points to a media relay (a TURN‑style RTP proxy).
2. Media Path – Once the remote party answers, the provider allocates a PSTN circuit and bridges the RTP stream to the media relay.
3. Bidirectional Audio – The relay forwards inbound audio to Muse’s ASR pipeline and pushes synthesized audio back to the call.

Because RTP is UDP‑based, Muse must implement packet loss concealment and jitter buffers on the media relay to guarantee sub‑20 ms mouth‑to‑ear latency for the AI speaker.

2. AI Orchestration Stack

The heart of the system is a micro‑service graph that processes audio in near‑real time:

The pipeline is event‑driven using a lightweight message bus (NATS JetStream). Each stage publishes its output to a subject that the next stage subscribes to, enabling horizontal scaling and back‑pressure handling.

3. Memory Model & State Management

Outbound sales calls are intrinsically stateful: the AI must remember the prospect’s name, previous objections, and the current sales stage. Muse stores per‑call state in a Redis‑based CRDT that replicates across the orchestration cluster. The state schema includes:

{
  "call_id": "string",
  "prospect": {"name": "string", "company": "string"},
  "dialogue_history": ["user utterance", "assistant response"],
  "sales_stage": "enum[INTRO, DISCOVERY, CLOSE]",
  "metadata": {"last_intent": "string", "timestamp": "int"}
}

Because the dialogue manager runs on a stateless LLM service, the CRDT guarantees eventual consistency even if the LLM instance crashes mid‑call. The system snapshots the state to S3 every 30 seconds for post‑mortem analysis and compliance.

4. Latency Budget & Real‑Time Guarantees

Outbound calling is unforgiving: a noticeable lag makes the AI sound robotic. Muse targets an end‑to‑end latency of ≤ 300 ms from the moment a prospect speaks to the moment the AI replies. The budget is allocated as follows:

• Network RTT (SIP + RTP): 30‑50 ms (same‑region datacenter to SIP provider)
• ASR processing: 80‑120 ms (streaming inference)
• NLU / Intent extraction: 30‑50 ms (lightweight token‑level classification)
• LLM generation: 150‑250 ms (batched, quantized inference)
• TTS synthesis: 40‑70 ms (pre‑warm GPU kernels)

If any stage exceeds its budget, the media relay injects silence padding to avoid abrupt cuts, but this degrades user experience. Muse therefore implements dynamic load shedding: when GPU utilization > 85 % it throttles new call admissions and returns a graceful

503

5. Compute Dynamics & Autoscaling

Muse runs on a Kubernetes cluster with node pools dedicated to each pipeline stage:

• ASR nodes: 4× A100 per pod, each handling ~30 concurrent streams.
• LLM nodes: 8× A100 per pod, leveraging tensor parallelism to serve up to 100 concurrent sessions.
• TTS nodes: 2× RTX 3090 per pod, optimized for low‑latency batch synthesis.

The Horizontal Pod Autoscaler (HPA) monitors per‑stage queue length and GPU memory usage, scaling pods in 30‑second increments. Cost analysis (see trade‑off table) shows that GPU‑heavy stages dominate OPEX, which is why Muse charges per‑minute usage rather than a flat subscription.

The FutureStack Perspective

Mubashir Arham – Founder & Chief Architect, FutureStack

When I first heard about Muse’s outbound‑calling API, my instinct was to ask: “How are they keeping the conversation fluid while juggling a massive LLM on the fly?” The answer lies in disciplined separation of concerns and a ruthless focus on latency.

Common Pitfalls

1. Monolithic Audio‑LLM Pipelines – Teams often try to run a single “speech‑to‑text‑to‑response‑to‑speech” model in one container. The result is a 1‑second+ round‑trip that shatters the illusion of a live human. Muse’s micro‑service graph, backed by NATS, keeps each stage lightweight and independently scalable.
2. Stateful LLM Instances – Persisting conversation history inside the LLM process leads to memory bloat and hard‑to‑debug crashes. By externalizing state to a CRDT‑backed Redis, Muse can recycle LLM containers without losing context.
3. Ignoring Telephony Realities – Many AI‑first startups forget that the PSTN is a noisy, latency‑sensitive medium. Muse’s investment in a TURN‑style RTP relay and jitter buffers is the quiet hero that prevents audio glitches.

Why It Matters for FutureStack

At FutureStack we are building a Composable AI Runtime that lets any SaaS embed LLM‑driven interactions—chat, voice, or video—without re‑inventing the plumbing. Muse’s architecture validates three design tenets we champion:

• Protocol‑First Design – Start from SIP/WebRTC, then layer AI.
• Stateless Compute, Stateful Store – Keep inference pods disposable; keep dialogue in a fast, replicated KV store.
• Latency‑First Autoscaling – Use queue‑length metrics, not CPU %, to trigger scaling.

Teams that skip these steps end up with flaky demos that never survive production traffic. Muse’s paid model also forces a cost‑signal back to the developer, encouraging judicious usage—something we embed in FutureStack’s billing primitives.

In short, Muse has turned a “nice‑to‑have” feature into a first‑class product by engineering for the hard constraints of telephony, not just for the elegance of LLMs.

Code / Implementation Blueprint (Python)

Below is a minimal, production‑grade snippet that demonstrates how to stitch together the three core APIs Muse would expose:

import asyncio
import httpx
import base64
from typing import AsyncGenerator

# -------------------------------------------------------------------
# Configuration – replace with your Muse credentials and Twilio SIP URL
# -------------------------------------------------------------------
MUSE_API_KEY = "sk_live_****************"
MUSE_BASE_URL = "https://api.muse.ai/v1"
TWILIO_SIP_URL = "sip:your-trunk@sip.twilio.com"

# -------------------------------------------------------------------
# Helper: stream audio from a live call (RTP packets are captured via
# a media relay that forwards raw PCM frames to this coroutine).
# -------------------------------------------------------------------
async def audio_stream() -> AsyncGenerator[bytes, None]:
    """Yield 20 ms PCM chunks (16 kHz, mono, 16‑bit)."""
    while True:
        # In a real deployment this would read from a UDP socket.
        pcm_chunk = await read_rtp_packet()
        yield pcm_chunk

# -------------------------------------------------------------------
# Step 1 – Initiate a call via Twilio (SIP INVITE) and bind the media
# relay to the Muse streaming endpoint.
# -------------------------------------------------------------------
async def place_call(to_number: str) -> str:
    async with httpx.AsyncClient() as client:
        resp = await client.post(
            f"{MUSE_BASE_URL}/calls",
            headers={"Authorization": f"Bearer {MUSE_API_KEY}"},
            json={
                "destination": to_number,
                "sip_endpoint": TWILIO_SIP_URL,
                "media_callback_url": "https://your.service/callback",
                "pricing": {"currency": "USD", "rate_per_min": 0.12},
            },
        )
        resp.raise_for_status()
        return resp.json()["call_id"]

# -------------------------------------------------------------------
# Step 2 – Stream audio to Muse, receive generated speech, and push it
# back to the media relay.
# -------------------------------------------------------------------
async def handle_call(call_id: str):
    async with httpx.AsyncClient(timeout=None) as client:
        # Open a bidirectional streaming endpoint
        async with client.stream(
            "POST",
            f"{MUSE_BASE_URL}/calls/{call_id}/stream",
            headers={"Authorization": f"Bearer {MUSE_API_KEY}"},
            json={"model": "llama2-70b-quant4"},
        ) as stream:
            # `stream.aiter_bytes()` yields synthesized PCM from Muse
            synth_iter = stream.aiter_bytes()
            async for inbound_pcm in audio_stream():
                # Send inbound audio to Muse (base64‑encoded for JSON payload)
                await client.post(
                    f"{MUSE_BASE_URL}/calls/{call_id}/audio",
                    headers={"Authorization": f"Bearer {MUSE_API_KEY}"},
                    json={"audio": base64.b64encode(inbound_pcm).decode()},
                )
                # Pull synthesized response (if any) and forward to RTP relay
                try:
                    synth_pcm = await asyncio.wait_for(synth_iter.__anext__(), timeout=0.1)
                    await send_to_rtp_relay(synth_pcm)
                except asyncio.TimeoutError:
                    continue

# -------------------------------------------------------------------
# Entry point – place a call and start the streaming loop.
# -------------------------------------------------------------------
async def main():
    call_id = await place_call("+1-415-555-0123")
    await handle_call(call_id)

if __name__ == "__main__":
    asyncio.run(main())

Explanation

• Copy

  place_call

  registers the outbound call with Muse, which in turn creates a SIP INVITE to Twilio.
• Copy

  audio_stream

  abstracts the RTP → PCM conversion; in production you would use
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  aiortc

  or a custom GStreamer pipeline.
• The bidirectional streaming endpoint (
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  /calls/{id}/stream

  ) returns synthesized PCM in real time, enabling a tight feedback loop.
• All network I/O is asynchronous, ensuring the 300 ms latency budget is respected.

Empirical Trade‑Off Table

Key takeaway: Muse’s pricing reflects the full‑stack value—low‑latency media handling, GPU‑accelerated LLM inference, and regulated telephony. Building a comparable stack in‑house typically costs more in engineering time than in cloud spend.

Future Industry Trajectory (18‑36 Months)

1. Quantized, Multi‑Modal LLMs for Voice‑First Apps – Expect a wave of 4‑bit or 8‑bit LLMs that can ingest audio embeddings directly, eliminating the separate ASR step. This will shave ~50 ms off the latency budget and reduce GPU memory pressure.
2. Edge‑Hosted Media Relays – With 5G‑enabled edge compute, media relays will migrate closer to the end‑user, cutting network RTT to < 10 ms. Companies like Cloudflare and Fastly are already piloting WebRTC‑to‑PSTN edge functions.
3. Composable AI Telephony Platforms – The market will consolidate around API‑first platforms that expose modular primitives (dial‑out, intent‑extraction, sentiment, compliance). Muse’s “pay‑per‑minute” model will become the baseline pricing scheme, prompting startups to differentiate on customizable dialogue policies and domain‑specific fine‑tuning.
4. Regulatory‑Driven Auditing Layers – As AI‑driven sales calls proliferate, regulators will demand real‑time consent capture and explainability logs. Future architectures will embed a policy engine that can pause or redirect a call if a compliance rule is triggered.
5. Hybrid Cloud‑On‑Prem Deployments – Enterprises with strict data‑sovereignty requirements will run the LLM/TTS stack on‑prem while still leveraging SaaS SIP trunks. This hybrid model will be facilitated by standardized container images (e.g., OCI‑compliant) and gRPC‑based media contracts.

In the next three years the line between “AI assistant” and “dialer” will blur. Companies that architect for low latency, stateful dialogue, and regulated telephony today—just as Muse has—will capture the emerging market for AI‑powered outbound engagement.

Conclusion

Muse’s paid outbound‑calling API is more than a novelty; it is a reference implementation of how to fuse real‑time telephony with large‑scale LLM inference. By dissecting the SIP transport, the streaming ASR‑LLM‑TTS pipeline, and the stateful CRDT store, we see why latency stays under 300 ms and why the service can safely charge per‑minute usage.

For engineering teams building similar capabilities, the lessons are clear:

• Treat telephony as a first‑class citizen – don’t bolt voice on top of a chat API.
• Separate state from compute – use a fast KV store to survive pod churn.
• Scale on queue depth, not CPU – latency‑driven autoscaling prevents silent degradation.
• Expose cost signals early – a pricing model that reflects true compute encourages responsible usage.

FutureStack will continue to monitor Muse’s evolution, integrating the best practices into our own composable AI runtime. The future of outbound AI is here, and it rings loudly.