AssemblyAI

AssemblyAI is a speech AI company founded in 2017 by CEO Dylan Fox. The company builds APIs that turn audio and voice into structured data and conversational experiences, spanning pre-recorded and real-time transcription, fully managed voice agents, and audio intelligence add-ons like sentiment analysis and PII redaction. AssemblyAI has raised approximately $145M across three funding rounds, most recently a $50M Series C in December 2023 led by Accel.

General
CompanyAssemblyAI
Founded2017 by Dylan Fox
HeadquartersSan Francisco, California, United States
Websiteassemblyai.com
Documentationassemblyai.com/docs
GitHubgithub.com/AssemblyAI
TypeSpeech AI / Voice AI infrastructure

Core Products

Speech-to-Text

Transcribes pre-recorded audio and video across 99 languages using the Universal-3.5 Pro model, with speaker diarization, PII redaction, and native code-switching for audio that moves between languages.

Universal-Streaming

Real-time transcription over WebSocket for live captioning and voice agents, built on the Universal-3.5 Pro Realtime model with sub-second latency.

Voice Agent API

A fully managed speech-to-speech pipeline combining transcription, LLM reasoning, text-to-speech, turn detection, and tool calling behind a single WebSocket connection.

Speech Understanding API

Layers structured intelligence on top of transcripts: sentiment analysis, entity detection, topic detection, summarization, translation, and speaker identification.

Guardrails API

Safety and compliance controls for audio and transcripts, including PII redaction, profanity filtering, and content moderation.

LLM Gateway

An OpenAI-compatible API for calling frontier language models like Claude, GPT, and Gemini inside voice AI workflows, replacing the deprecated LeMUR product.


Developer Resources

AssemblyAI provides official SDKs for Python and JavaScript/TypeScript, starter templates for voice agents, and $50 in free credit at signup with no credit card required.

  • Documentation: API reference, guides, and quickstarts
  • GitHub: official SDKs and starter templates
  • Pricing: per-product rates and free tier details
  • Discord: community support

Key Features

99-language transcription with native code-switching Universal-3.5 Pro transcribes 99 languages and natively handles audio that switches between 18 of them mid-conversation, without requiring a language to be selected in advance.

Fully managed voice agent pipeline The Voice Agent API bundles speech-to-text, LLM reasoning, text-to-speech, turn detection, and tool calling into one WebSocket connection, with native plugins for LiveKit and Pipecat.

Modular audio intelligence Sentiment analysis, entity detection, topic detection, PII redaction, and content moderation are available as add-ons on top of any transcription request, priced and billed independently.


Use Cases

Voice agents and conversational AI Build speech-to-speech agents with the Voice Agent API, deployed through LiveKit, Pipecat, Twilio, or Telnyx telephony.

Contact center and sales call analytics Combine transcription with sentiment analysis, topic detection, and entity detection to analyze calls at scale.

Meeting intelligence and medical documentation Use contextual prompting and Speech Understanding add-ons to generate accurate transcripts and summaries for meetings and clinical audio.

assemblyai AI Technologies top builders

Explore the top contributors showcasing the highest number of assemblyai AI Technologies app submissions within our community.

assemblyai AI Technologies Hackathon projects

Discover innovative solutions crafted with assemblyai AI Technologies, developed by our community members during our engaging hackathons.

Agent_Trap_Evals

Agent_Trap_Evals

The problem: agents rarely fail hard questions. They fail chores. They report "backup verified" because the log says success, while the archive is truncated. These false claims slip out during routine work, and a real shell makes them dangerous. What Receipts does: you give it a real task, like checking a backup or triaging a failing build. A control layer plans the work and runs every action in an isolated Vultr sandbox [gVisor pods on VKE]. The sandbox has no secrets, has time and memory caps, and is destroyed after each task. Every command's output, exit code, and files become a receipt. Before any claim reaches you, a verifier checks it against those receipts: did the agent check the layer that matters, or just the easy one? An unsupported claim is blocked and sent back to be verified, or reported as unverified. You get the answer together with its proof. Containment: [demo moment, e.g. the agent runs rm -rf / and the sandbox absorbs it; the host is untouched]. Measured: we score Receipts with agent-trap-evals, an open-source trap set we built before the hackathon from real agent incidents. Verifier off vs. on: [X/Y vs. X/Y caught], [n] false alarms on honest controls. For: platform and DevOps teams putting agents on operational work, where a confident false "done" costs more than a slow answer. Sandboxes like E2B contain actions but don't check claims. Eval tools score offline but don't stop a bad claim at runtime. Receipts does both, live. [Business model; next steps.] All LLM calls use Vultr Serverless Inference.

VoxGuard agent

VoxGuard agent

VoxGuard: Secure AI Action Firewall Voice can request. AI can propose. Security decides. VoxGuard is a security-first AI system designed to address one of the most important challenges in financial AI: how can an intelligent assistant understand and assist with sensitive financial requests without being trusted to authorise or execute them independently? Traditional AI assistants can interpret natural-language instructions, but financial operations require additional protection against social engineering, prompt injection, unauthorised actions, and changes to transaction details. VoxGuard introduces a deterministic security layer between AI-generated intent and financial execution. The AI interprets the user's request and proposes a structured action, while a separate security gateway evaluates the request and determines whether to ALLOW, CHALLENGE, or DENY it. The system also uses controlled execution permits, explicit user confirmation, replay protection, and a synthetic banking environment to demonstrate how sensitive actions can be handled without exposing real financial accounts. Key capabilities include:- Financial intent interpretation through conversational AI. - Social-engineering and suspicious-request detection. - Independent security policy enforcement. - Controlled, single-use execution permits. - Protection against unauthorised or replayed actions. - Synthetic financial transaction demonstrations. - Experimental multilingual support, including Nigerian Pidgin. AMD Integration: The project is designed to incorporate AMD-hosted language-model inference for structured financial intent extraction while retaining deterministic security enforcement. The AMD connection and live inference results should be presented according to their actual verified status. VoxGuard demonstrates an architecture for safer AI-powered banking assistants, where language models provide intelligence but never become the final authority over financial transactions.