Browse by Category
Find tools organized by use case and functionality
Voice AI
Voice AI tools provide the infrastructure to process, interpret, and generate spoken human language. These tools typically function as end-to-end applications for voice interaction or as developer APIs that handle specific stages of the voice pipeline, such as converting speech to text (ASR), understanding intent (NLU), or converting text back into lifelike speech (TTS). Examples Text-to-Speech (TTS): Generating human-quality audio from written text for content creation or accessibility. Speech-to-Text (ASR): Transcribing meetings, podcasts, or medical notes in real-time. Voice Cloning: Creating digital replicas of a specific person's voice for dubbing or personalization. Audio Translation: Automatically translating spoken audio from one language to another while preserving the original speaker's tone.
7 tools
Vision AI
Vision AI tools offer infrastructure to build computer vision applications or exist as standalone applications that solve specific visual problems using AI. These tools enable machines to "see" by processing, analyzing, and understanding images and video streams. Examples Object Detection: Identifying and locating specific items (e.g., cars, defects, products) within an image or video. OCR (Optical Character Recognition): Extracting editable text from scanned documents or street signs. Image Generation: Creating entirely new visual assets from text descriptions. Facial Recognition: Verifying identity or analyzing demographics for security and personalization.
9 tools
LLMs (Large Language Models)
LLM tools encompass the foundational models and platforms used to generate, summarize, and transform text. These are the engines behind modern AI, trained on vast datasets to understand and predict language patterns. Tools in this category include proprietary model providers, open-source model hubs, and fine-tuning platforms. Examples Text Generation: Drafting emails, marketing copy, or creative stories. Code Generation: Writing and debugging software code based on natural language prompts. Summarization: Condensing long documents or meeting transcripts into concise bullet points. Translation: Converting text fluently between dozens of languages.
7 tools
VLMs (Vision-Language Models)
VLMs are multimodal tools that bridge the gap between text and visuals. Unlike standard Vision AI (which sees) or LLMs (which read), VLMs can reason across both formats simultaneously. They allow users to input images and ask questions about them, or input text to guide visual tasks. Examples Visual Question Answering (VQA): Uploading a photo of a broken appliance and asking, "How do I fix this?" Image Captioning: Automatically generating descriptive alt-text for images for SEO or accessibility. Document Understanding: Analyzing complex PDFs that contain both charts and text to extract insights. Video Search: Searching through video archives using natural language queries like "Find the moment the dog jumps."
6 tools
Data Annotation Tools
Data annotation tools are platforms used to label and structure raw data (images, text, audio, video) so it can be used to train AI models. These tools provide the "ground truth" that teaches AI systems what they are looking at or reading. Examples Bounding Box Labeling: Manually drawing boxes around cars in images to train self-driving cars. Sentiment Labeling: Tagging customer reviews as "Positive," "Negative," or "Neutral" to train sentiment analysis models. Segmentation Masks: Pixel-perfect coloring of objects (like tumors in medical scans) for precise computer vision training. RLHF Platforms: Interfaces for humans to rank AI responses, teaching the model which answer is better.
6 tools
Agent Infrastructure
Agent infrastructure refers to the frameworks, toolkits, and orchestration layers used to build "AI Agents"—systems that can reason, plan, and execute multi-step tasks autonomously. Unlike a simple chatbot that answers a question, an agent uses this infrastructure to use tools, browse the web, and complete goals. Examples Agent Frameworks: Libraries that help developers chain together LLM prompts, memory, and tools (e.g., LangChain). Memory Systems: Databases that allow agents to remember past interactions and user preferences over long periods. Tool Use / Function Calling: Protocols that enable an AI to "click" buttons, send emails, or query APIs on its own. Multi-Agent Orchestration: Systems that manage teams of specialized agents (e.g., a "Coder" agent and a "Reviewer" agent) working together.
13 tools
Evals
Evals are tools and methodologies used to test, measure, and validate the performance of AI models. Since AI output can be unpredictable, these tools help developers ensure their applications are accurate, safe, and reliable before deploying them to users. Examples LLM-as-a-Judge: Using a highly intelligent model (like GPT-4) to grade the answers of a smaller, faster model. Hallucination Detection: Tools that specifically check if an AI's answer is factually incorrect or made up. Bias & Safety Testing: Automated stress-testing to ensure the model refuses to generate toxic or harmful content. Performance Benchmarking: Comparing model speed (latency) and cost across different providers.
6 tools
RAG (Retrieval-Augmented Generation)
RAG tools provide the pipeline to connect an AI model to your private data (documents, databases, wikis). Instead of relying only on what the AI was trained on, RAG tools "retrieve" relevant facts from your company's data and "augment" the AI's answer, ensuring accuracy and context. Examples Vector Databases: Specialized storage that allows AI to search data by "meaning" rather than just keywords. Document Parsers: Tools that chop up messy PDFs and spreadsheets into clean chunks of text for the AI to read. Hybrid Search: Search engines that combine keyword matching with AI semantic search for better results. Knowledge Graph Creation: Mapping connections between data points (e.g., "CEO" is linked to "Company") to help the AI understand relationships.
10 tools
MCP Servers (Model Context Protocol)
MCP Servers are a standardized way to connect AI assistants to external systems and data sources. They act as universal adapters that expose data (like files or database rows) and tools (like "create calendar event") to an AI client in a format the AI can easily understand and use securely. Examples File System Access: A server that lets an AI safely read and write files in a specific local directory. Database Connectors: Servers that allow an AI to query a SQL database to answer questions like "How many users signed up today?" API Gateways: A server wrapping a service like Slack or GitHub, letting the AI send messages or open pull requests via a standard protocol. Read-Only Resources: Servers that simply provide context, such as feeding live stock market logs or server status reports to the AI.
5 tools
AI Code Editors
AI Code Editors are intelligent workspaces where developers write, test, and debug software. Unlike traditional text editors, these tools are embedded with AI that understands the context of the entire project. They act as "pair programmers," helping developers write code faster by predicting intent, fixing errors automatically, and allowing users to edit software using plain English commands rather than complex syntax. Examples Smart Autocomplete: Predicting and completing entire functions or logic blocks as the developer types (e.g., "ghost text"). Natural Language Editing: Highlighting a block of code and typing "Make this code cleaner and add comments" to instantly rewrite it. Chat with Codebase: A sidebar interface where you can ask, "Where is the authentication logic defined?" and get an instant link to the file. Auto-Debugging: Automatically analyzing a crash error and suggesting the exact line of code needed to fix it.
10 tools