Guardrails API

The Guardrails API adds safety, privacy, and compliance controls to AssemblyAI transcripts and audio. It covers PII redaction in both transcript text and the underlying audio, profanity filtering, and content moderation, applied as add-ons to a transcription request.

General
Developer	AssemblyAI
Type	Safety and compliance API
License	Commercial API
Documentation	assemblyai.com/products/guardrails

Core Features

PII text redaction: removes personally identifiable information from transcript text.
PII audio redaction: redacts personally identifiable information directly in the audio file.
Profanity filtering: filters profane language out of transcript output.
Content moderation: flags or filters unsafe or policy-violating content.

Pricing

Guardrails features are billed as add-ons on top of the base Speech-to-Text rate:

Add-on	Rate
Profanity Filtering	+$0.01/hour
PII Text Redaction	+$0.08/hour
Content Moderation	+$0.15/hour

Source: assemblyai.com/pricing

Use Cases

Regulated industries: healthcare and financial services use cases that require PII redaction before storing or sharing transcripts.
Customer support and contact centers: content moderation and profanity filtering on recorded or live calls.
Compliance workflows: redacting sensitive information from audio archives before retention or sharing.

Ecosystem and Integrations

Guardrails add-ons apply to output from both Speech-to-Text and Universal-Streaming, and can be combined with Speech Understanding add-ons on the same request.

Read the Guardrails docs to add safety and compliance controls to an existing transcription request.

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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.

PatchArena

PatchArena is a security-patching agent built for the AMD Developer Hackathon: ACT III. Fixing a vulnerability is slow because someone has to reproduce the bug, write a patch, and prove it works without breaking anything else. PatchArena automates that loop and runs it as a tournament. Given a vulnerability report and a Python repository, it first writes an exploit test that must fail on the original code. It then asks an open-weight coder model, self-hosted on AMD GPUs with ROCm and vLLM, to generate many candidate patches in parallel, each with a different fixing strategy. Every candidate runs in its own sandbox and must pass four checks: it runs, it blocks the exploit, the full existing test suite stays green, and it leaves tests and CI files untouched. The smallest verified patch becomes a pull request with the evidence attached. If no candidate survives, PatchArena flags the issue for a human instead of guessing. A live dashboard shows candidates racing and failing in real time, along with model throughput. Every pull request still needs human review before merge.

Tablekeeper: AI-Built Restaurant Reservations

Tablekeeper is a restaurant reservation system with one hard rule: a table is never double-booked. It was built entirely by a "dark factory" of three AI coding agents sharing one BAND room, using Claude Code with the claude-sonnet-5 model. No human wrote any application code. The Planner reads the task and spec, writes a requirements checklist and assigns work items. The Implementer writes the code and tests, builds the Docker image, runs the official harness and posts evidence. The Reviewer writes no application code: it rebuilds the container and re-runs every check itself, attacking the service with replayed idempotency keys, double-booking attempts and daylight-saving changes, then accepts or rejects. Stage 1 is a JSON API for restaurants, tables, availability and reservations. Stage 2 adds a browser website (search by restaurant, date and party size, book, look up by reference code, cancel) and combinable tables for bigger parties. Both stages pass the official harness in isolated mode (stage 1: 120/120, stage 2: 25/25 shipped checks), our own suite of 282 tests passes, and each image starts in a fresh container with no network. The repository includes the agent mandates, FACTORY.md describing how the factory works and catches bad work, and the full BAND room export. We are honest about limits: no per-seat git history, no visual inspection of the site, and stages 3 and 4 were not attempted.

HandSpeak

SignBridge is a real-time AI-powered sign language interpretation system designed to make communication more accessible between sign language users and people who do not understand sign language. Using a camera, the system detects hand and body movements, analyzes gesture sequences, and converts recognized signs into natural-language text and speech. The project will leverage AMD GPU acceleration to improve real-time computer vision and AI inference performance, allowing the system to process video with low latency. Beyond individual signs, SignBridge aims to recognize sequences of gestures and reconstruct meaningful phrases, making communication more natural. The project combines accessibility, computer vision, AI, and high-performance AMD computing to create a practical assistive technology solution.

Jnanam AI Voice Innovators

Jnanam AI Voice Service Agent is a multilingual voice interface that connects natural conversations with real service workflows. Our idea is to help users discover services, check availability, and make bookings by speaking naturally in English, Hindi, or Telugu. For example, a user could say, “I need an electrician tomorrow,” and the agent would clarify the requirements, check available options, and request explicit confirmation before creating a booking. Built using AssemblyAI’s voice-agent capabilities, the solution introduces a reusable Jnanam AI middleware layer between client applications and the voice platform. This layer manages conversation context, coordinates tool calls, connects to application APIs, and streams responses back to the user. The client application retains control of its business logic, service data, availability, and bookings. Our initial demonstration focuses on a service marketplace, using tools for service discovery, availability checks, and booking creation. The broader vision is to make this voice layer adaptable to customer support, appointment scheduling, and other business workflows. We aim to reduce dependence on complex screens and forms, make digital services more accessible across languages, and help businesses turn spoken requests into useful actions with clear user confirmation.