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Speech-to-text for AI medical scribes: Why clinical vocabulary breaks generic STT
TL;DR: Generic STT engines fail in clinical environments because language model probability overrides correct acoustic detection of medical terms, substituting phonetically plausible but clinically wrong candidates silently. The result corrupts drug names, dosages, and diagnoses before the LLM ever sees them. Before selecting an STT engine for a medical scribe, verify four things: whether vocabulary biasing works at inference time without fine-tuning, whether async diarization accurately separates clinician and patient audio, whether the model holds up on noisy consultation recordings rather than clean read-speech, and whether the vendor's data training policy covers PHI by default on your plan.
Migrating from self-hosted Whisper to a managed speech-to-text API
TL;DR: Self-hosting Whisper's true cost rarely sits in the model weights. GPU idle time, VRAM leaks under parallel load, and the engineering hours spent maintaining CUDA dependencies and diarization pipelines are where the bill compounds. For teams processing under roughly 3,000 hours per month, assuming 20% of one US FTE at $150K loaded annual cost, a managed API is cheaper, though the break-even shifts materially against your actual labor cost. Above that threshold, the decision depends on your DevOps overhead and whether audio accuracy on real-world recordings matters for downstream systems like CRM sync and coaching scores.
Migrating from AssemblyAI to Gladia: A step-by-step switching guide
TL;DR: Switching from AssemblyAI requires four concrete changes: update one auth header, remap batch endpoints, adjust the JSON response schema, and resample audio for WebSocket connections. Multiple customers independently report completing these in under a day with a rollback abstraction layer in place. The bigger structural difference is cost model: a production stack with diarization, sentiment, entities, and summarization runs $0.30/hr on AssemblyAI's Universal-2 tier because each feature is metered separately, versus a bundled base rate. This guide covers the exact parameter mappings, payload diffs, WebSocket reconfiguration, and a zero-downtime cutover strategy.
Audio-to-LLM: From audio to structured intelligence in one API call
Published on May 5, 2026
by Anna Jelezovskaia
TL;DR: Gladia's Audio-to-LLM runs transcription, diarization, and LLM analysis in a single POST request. Pass a 'prompts' array, get structured outputs back in one webhook. No pipeline to build or maintain. Pick from 400+ model choices, with a free tier including 10 hours/month.
Every call, meeting, and voice interaction your product handles contains decisions waiting to be made. Most teams extract only a fraction of it – because turning audio into structured intelligence, such as action items, summaries, and so on – requires a pipeline you have to build, maintain, and pay for twice.
Our new feature, Audio-to-LLM, closes that gap. Transcription, diarization, and LLM analysis happen in a single POST request. You get one webhook with everything. Read on to see how it works and how to use it.
The two-hop problem of LLM pipelines
Here’s an approach you’re familiar with by now: pipe audio through an STT engine, collect a wall of text, send it to an LLM to generate structured outputs for your users.
This creates a pipeline you own forever — mismatched context windows, no structured output contract, and two vendors to debug when something breaks. Pre-built summarization features help at the margins, but the moment you need custom extraction schemas, compliance checks, or domain-specific Q&A, you're back to building the second hop yourself.
For teams that want to ship faster, reduce infrastructure work, or add audio intelligence without maintaining a separate LLM pipeline, Audio-to-LLM gives you a simpler path.
What is Audio-to-LLM?
Audio-to-LLM is an audio intelligence feature that lets you run custom prompts directly on top of a pre-recorded transcript, as part of the same audio processing job.
Instead of building this yourself:
Audio → STT provider → transcript → LLM provider → prompt result → your app
You can use Gladia for the whole workflow:
Audio → Gladia → transcript + LLM outputs
You configure the transcription request, add your model and prompts, and receive the results in the same JSON response as the transcript.
This means you can build workflows such as call scoring, compliance checks, CRM note, meeting summaries, action item extraction, and more use cases described below.
Why use Audio-to-LLM
One API call vs. a multi-service pipeline
A DIY pipeline means stitching together an STT service, transcript storage, an LLM service, prompt management, output parsing, and error handling across multiple vendors.
With Gladia's Audio-to-LLM, it's a single POST request. Transcription, diarization, and LLM analysis happen server-side. You get one webhook with everything.
If you want a specific output structure, you define it in the prompt.
Extract the customer issue, resolution, sentiment, and next action.
Return valid JSON with the keys: issue, resolution, sentiment, next_action.
Transcript quality is the foundation
LLM output is only as good as the transcript it reads. If your transcription engine struggles with accents, background noise, or multilingual speakers, no amount of prompt engineering can fix a garbled input. Powered by Solaria ASR, Gladia's production pipeline delivers:
1–3% error rate (WER) across 100+ languages with native code-switching
Best diarization accuracy on the market, powered by pyannote’s Precision-2
You can check our latest accuracy benchmarks here, with an open-source normalization library available to reproduce these results on your audio.
Model flexibility without vendor lock-in
The model field accepts any of 400+ models, available via OpenRouter. Start with GPT-5.4 Nano for high-volume, low-cost extraction. Switch to Claude Opus for complex reasoning. Switch to Llama 4 Maverick for cost optimization. No code change, just a config value.
Model Pricing Comparison
Model
Strength
Input (per 1M tokens)
Output (per 1M tokens)
GPT-5.4 Nano
Fast, cheap, high-volume extraction
$0.26
$1.76
GPT-5.4
Strong general reasoning
$3.25
$19.50
Claude Opus 4.7
Complex analysis, long outputs
$6.50
$32.50
Llama 4 Maverick
Cost-optimized open model
$0.20
$0.78
Total cost of ownership
Building and maintaining your own transcription + LLM pipeline at scale means GPU provisioning, autoscaling logic, model updates, and ongoing engineering maintenance.
At 10,000 hours/month, that adds up to significant infrastructure and headcount costs. Gladia's all-inclusive async rate starts at $0.50/hr ($5,000/month at 10k hours), with Audio-to-LLM token costs on top, but no infrastructure to manage and no second vendor to integrate.
That, plus a sub-24-hour integration to production with our API.
How is this different from an LLM gateway?
Some STT vendors offer an LLM gateway: a unified API for calling multiple language models from providers like OpenAI, Anthropic, Google, or others.
That is useful if your main problem is model access.
Audio-to-LLM solves a different problem: building an audio intelligence workflow from end to end.
With a generic LLM gateway, developers stillneed to transcribe the audio first, retrieve or store the transcript, format the transcript for the model, include speaker context if needed, send a second request to the LLM endpoint, handle failures, and map the LLM response back into the audio workflow.
With Gladia’s Audio-to-LLM, transcription and LLM analysis arepart of the same job. You send the audio once, define your prompts in the transcription request, and receive the transcript, diarization, and LLM outputs together.
Capability Comparison
Capability
Generic LLM gateway approach
Gladia Audio-to-LLM
Primary purpose
Unified access to multiple LLMs
Audio-native transcription + LLM analysis
Workflow
Transcribe first, call LLM second
One audio processing job
Transcript handling
Developer formats and sends transcript manually
Handled inside the request
Speaker context
Often must be formatted manually
Can be used alongside diarization in the same workflow
Multiple prompts
Requires orchestration in your app
Supported in the audio request
Output format
Prompt-dependent
Prompt-dependent
Best for
General LLM access, chat completions, agents
Turning calls, meetings, and recordings into structured audio intelligence
If you already have a mature transcript-to-LLM pipeline, an LLM gateway can be a good fit. If you want to ship audio intelligence without maintaining that pipeline yourself, Audio-to-LLM gives you a simpler path.
Use cases for Audio-to-LLM
Audio-to-LLM applies wherever recordings need to become structured data. Here are the highest-impact patterns across contact centers and meeting assistants.
Note: All of these are runnable as multiple prompts in a single API request.
Contact centers
Most CCaaS providers sample 1–5% of calls manually. With Audio-to-LLM, every recording becomes structured data with no additional pipeline work.
Use Case Examples
Use case
Example prompt
Impact
Agent QA & Scoring
“Score this call 1–10 on greeting, resolution, upsell. Return JSON.”
100% call coverage vs. 2% manual
Compliance Monitoring
“Did the agent read the required disclosure? Flag deviations.”
Automated, per-call, auditable
Contact Reason Extraction
“Classify into one of 30 categories, extract primary product.”
Feeds IVR optimization and workforce planning
Sentiment & Escalation
“Was there a frustration point? Did the agent de-escalate?”
Meeting recorders generate hours of audio that most users never revisit. Audio-to-LLM turns every session into a structured artifact your product can act on — without a separate analysis pipeline.
Meeting Use Case Examples
Use case
Example prompt
Impact
Action Item Extraction
“List every action item with owner and deadline. Return JSON.”
Replaces manual note review
Decision Log
“What decisions were made and who made them? Return structured list.”
Auditable record per meeting
Topic & Agenda Coverage
“Which agenda items were covered? What was skipped or deferred?”
Feeds meeting analytics and follow-up workflows
Participant Engagement
“Who spoke most? Who asked questions? Flag silent participants.”
Surfaces collaboration patterns
Follow-up Email Draft
“Write a follow-up email summarizing outcomes and next steps.”
Eliminates post-meeting busywork
Risk & Blocker Detection
“Did any participant raise a blocker, risk, or unresolved concern?”
Proactive project health monitoring
What the code looks like
from gladiaio_sdk import GladiaClient
client = GladiaClient(api_key="YOUR_GLADIA_API_KEY").prerecorded()
result = client.transcribe(
"https://storage.example.com/call.mp4",
{
"diarization": True,
"audio_to_llm": True,
"audio_to_llm_config": {
"model": "openai/gpt-5.4-nano",
"prompts": [
"Score this call on: greeting, issue ID, resolution. Return JSON.",
"Did the agent read the required TCPA disclosure? Yes/No + quote.",
"Write a 3-sentence CRM disposition note.",
],
},
},
)
import { GladiaClient } from"@gladiaio/sdk";
const client = new GladiaClient({ apiKey: "YOUR_GLADIA_API_KEY" });
const result = await client.preRecorded().transcribe(
"https://storage.example.com/call.mp4",
{
diarization: true,
audio_to_llm: true,
audio_to_llm_config: {
model: "openai/gpt-5.4-nano",
prompts: [
"Score this call on: greeting, issue ID, resolution. Return JSON.",
"Did the agent read the required TCPA disclosure? Yes/No + quote.",
"Write a 3-sentence CRM disposition note.",
],
},
},
);
One call. Transcription, diarization, and three LLM outputs, returned together.
Difference between Audio-to-LLM and Summarization
Gladia's summarization feature generates a quick overview in one of three preset formats: general, concise, or bullet points. No prompts, single toggle, good for fast recaps.
Audio-to-LLM picks up where summarization ends: custom extraction schemas, domain-specific analysis, multiple structured outputs from the same recording, full choice of model. Both features run in the same API request, so you can combine them without any additional integration work if needed.
Pricing and availability
Audio-to-LLM add-on is usage-based. Pricing depends on the model used and the amount of LLM processing required, with Gladia adding a platform fee on top of the model cost.
This gives developers the flexibility to choose the right model for their use case, from cost-efficient extraction to more advanced reasoning.
You will find more information on pricing per model and invoicing in our docs.
Start building
Audio-to-LLM is designed for developers who want to move from raw transcripts to production-ready audio intelligence faster.
Whether you’re building call center analytics, meeting workflows, or sales intelligence, you can now transcribe and analyze audio in one place. Gladia is also backed by enterprise-grade security and compliance (SOC 2, GDPR, HIPAA, ISO 27000), with dedicated EU and US clusters so your data stays in the region you choose.
Try it with your own recordings and see what you can build with one API call.
