Skill UI

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

A comprehensive Java SDK for building robust, server-side call automation workflows using Azure Communication Services (ACS). It allows developers to implement sophisticated IVR systems, dynamic call routing, interactive media playback (Text-to-Speech, audio files), speech and DTMF recognition, call recording management, and participant handling. Ideal for building sophisticated customer service bots or automated notification systems.

A comprehensive Java SDK for building robust, server-side call automation workflows using Azure Communication Services (ACS). It allows developers to implement sophisticated IVR systems, dynamic call routing, interactive media playback (Text-to-Speech, audio files), speech and DTMF recognition, call recording management, and participant handling. Ideal for building sophisticated customer service bots or automated notification systems.

A comprehensive guide for diagnosing and fixing common Deepgram API errors. Covers HTTP error codes, WebSocket issues, optimal audio preparation using ffmpeg, and implementing robust retry patterns for reliable Speech-to-Text integration.

A comprehensive workflow for production-grade, pre-recorded Speech-to-Text (STT) transcription using the Deepgram API. This skill supports transcribing audio from URLs or local files, offering advanced features such as speaker diarization, automatic summaries, sentiment analysis, and topic detection. It also includes robust batch processing methods for handling large datasets.

This skill provides comprehensive methods to optimize Deepgram API usage and manage costs. It implements budget-aware transcription pipelines that preemptively check projected spending against set limits, utilizes audio preprocessing techniques (like silence removal via ffmpeg) to minimize billable minutes, and includes API calls to monitor current usage and spending against defined budgets. Ideal for cost-sensitive, production-grade deployments.

Implements best practices for secure audio transcription using Deepgram. This guide covers PII redaction (built-in and custom patterns), secure audio upload to S3 with encryption, managing data retention policies, and ensuring compliance with major regulations like GDPR and HIPAA. It provides a comprehensive pipeline for handling sensitive audio data in enterprise applications.

Collects comprehensive, sanitized debug evidence for Deepgram support and troubleshooting. This bundle gathers detailed system environment information (Node.js, Python), performs live API connectivity tests, analyzes audio file metadata using ffprobe, and logs request/response data. Essential for preparing detailed support tickets and reproducing bugs related to Deepgram integration.

This guide provides minimal working examples for Deepgram speech-to-text transcription. It demonstrates how to transcribe audio from a URL or a local file using both TypeScript and Python. Users can learn basic API patterns, implement advanced features like diarization (speaker identification), and compare various model options (e.g., nova-3, nova-2). Ideal for initial integration and testing.

This guide establishes a robust local development environment for Deepgram integrations. It covers setting up test fixtures using sample audio, mocking API responses for isolated unit testing, configuring watch-mode development servers, and running comprehensive unit and integration tests using Vitest. Use this setup when initiating a Deepgram feature or requiring a reliable, fast development cycle.

This guide provides advanced techniques to optimize Deepgram API performance. Learn how to preprocess audio using ffmpeg (resampling, noise reduction), select optimal transcription models (speed vs. accuracy), and implement efficient streaming or parallel batch processing to achieve high throughput and low latency.