| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Analyzes screen recordings to automatically identify and extract discrete user actions as individual steps. The AI detects UI interactions, navigation changes, and state transitions to segment the recording into logical tutorial steps without manual frame-by-frame editing.
Generates natural language descriptions for each detected step using AI. The system analyzes what happened in each step and creates clear, concise written instructions that explain the action to end users.
Maintains version history of guides and enables easy updates when processes or UI changes. Allows creators to track changes, revert to previous versions, and publish updates without recreating guides from scratch.
Adds interactive components to guides such as clickable hotspots, expandable sections, and interactive walkthroughs that allow users to follow along step-by-step. Transforms static guides into more engaging, hands-on learning experiences.
Automatically captures and annotates screenshots from the screen recording, adding visual indicators like arrows, highlights, and callouts to emphasize important UI elements. Reduces manual annotation work by intelligently identifying elements that need highlighting.
Transforms a complete screen recording into a fully formatted, shareable tutorial guide in a single operation. Combines step detection, description generation, and screenshot annotation into one end-to-end workflow that produces a ready-to-publish guide.
Provides an interface to manually edit and customize AI-generated guides, including reordering steps, modifying descriptions, adjusting annotations, and refining content. Allows creators to correct AI mistakes and tailor guides to specific needs.
Enables guides to be shared via shareable links and embedded directly into websites, help centers, or documentation portals. Provides multiple distribution options without requiring additional hosting or setup.
+4 more capabilities
Accepts uploaded documents (PDF, TXT, Markdown) and automatically chunks them into semantic segments, then embeds each chunk using Vercel AI SDK's embedding models (supporting OpenAI, Anthropic, or local models). The pipeline stores vectors in a vector database (likely Pinecone or similar) with metadata linking back to source documents, enabling semantic search without manual preprocessing.
Unique: Integrates Vercel AI SDK's unified embedding interface, allowing seamless switching between OpenAI, Anthropic, and local embedding models without changing application code. Built on Vercel's serverless infrastructure, eliminating separate vector DB management for small-to-medium knowledge bases.
vs alternatives: Faster to deploy than LangChain + manual vector DB setup because it's a pre-configured template with Vercel's infrastructure baked in; more flexible than Pinecone's native UI because it's code-based and customizable.
Converts user search queries into embeddings using the same model as document ingestion, then performs vector similarity search against the indexed corpus. Returns ranked results ordered by cosine similarity score, with optional filtering by document metadata (source, date, category). Implements re-ranking via cross-encoder or LLM-based relevance scoring to improve result quality beyond raw vector similarity.
Unique: Leverages Vercel AI SDK's streaming capabilities to return search results progressively while re-ranking happens in parallel, improving perceived latency. Supports multi-model search (query with GPT-4, rank with Claude) without manual orchestration.
vs alternatives: More accurate than Elasticsearch keyword search for conceptual queries; faster to implement than building custom re-ranking logic because the template includes LLM-based relevance scoring out of the box.
AI Dashboard Template scores higher at 57/100 vs Wizardshot at 46/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Collects user feedback on search results and chat responses (thumbs up/down, explicit ratings, corrections). Analyzes feedback to identify low-quality results, hallucinations, and missing documents. Provides recommendations for improving RAG quality (e.g., re-chunking documents, adjusting similarity thresholds, adding new documents). Supports A/B testing of different RAG configurations.
Unique: Integrates feedback collection directly into the chat and search UIs with minimal friction (single-click ratings). Automatically correlates feedback with RAG configuration (model, chunk size, prompt) to identify which changes improve quality.
vs alternatives: More actionable than generic user satisfaction surveys because it captures feedback in context; more efficient than manual quality audits because it scales to thousands of interactions.
Tracks when documents were last updated and notifies administrators when documents exceed a configurable age threshold (e.g., 'notify if any document is older than 6 months'). Supports scheduled re-indexing of documents and tracks which documents have been updated since the last index. Provides a dashboard view of document freshness and allows marking documents as 'verified' or 'outdated'.
Unique: Tracks document freshness as a first-class concept in the RAG pipeline, enabling administrators to identify and update stale documents before they degrade search quality. Template includes configurable freshness thresholds and automated notifications.
vs alternatives: More proactive than reactive error handling because it identifies stale documents before they cause poor search results; simpler than full document versioning systems because it focuses on freshness rather than change tracking.
Implements a conversational interface where user messages trigger a retrieval-augmented generation (RAG) pipeline: (1) embed the user query, (2) retrieve relevant documents from the vector database, (3) construct a prompt with retrieved context, (4) stream the LLM response token-by-token to the client. Uses Vercel AI SDK's streaming primitives to handle backpressure and connection management, enabling real-time chat without buffering entire responses.
Unique: Uses Vercel AI SDK's `streamText()` primitive with built-in retrieval hooks, allowing developers to inject custom document retrieval logic without managing streaming state manually. Automatically handles backpressure and connection cleanup, reducing boilerplate compared to raw fetch + ReadableStream.
vs alternatives: Simpler than LangChain's streaming because it's purpose-built for Vercel's serverless environment; more responsive than buffered responses because tokens are sent as they're generated, not after full completion.
Provides a web UI for administrators to view indexed documents, monitor embedding status, delete or re-index documents, and adjust search parameters (e.g., similarity threshold, chunk size). Built with React/Next.js, it connects to backend APIs that manage the vector database and document storage. Includes analytics on search queries, user engagement, and document coverage.
Unique: Integrates with Vercel AI SDK's backend utilities to provide real-time indexing status and streaming logs, allowing admins to monitor long-running operations without polling. Built on Next.js App Router, enabling server-side data fetching and incremental static regeneration for performance.
vs alternatives: More user-friendly than raw vector database UIs (e.g., Pinecone console) because it abstracts database-specific concepts; more integrated than separate admin tools because it's part of the same codebase and shares authentication.
Provides a unified interface for switching between embedding models (OpenAI, Anthropic, Cohere, local models) without changing application code. The abstraction layer handles model-specific API calls, response parsing, and dimension normalization. Supports batch embedding for efficient processing of multiple documents and caching of embeddings to reduce API costs.
Unique: Vercel AI SDK's embedding abstraction automatically handles rate limiting, retries, and cost tracking across providers. Supports dynamic model selection at runtime, enabling A/B testing of embedding models without deployment.
vs alternatives: More flexible than LangChain's embedding interface because it includes cost tracking and batch optimization; simpler than managing multiple embedding SDKs because it's a single unified API.
Constructs system and user prompts that include retrieved documents as context, with configurable formatting (e.g., markdown, XML tags, structured JSON). Implements prompt templates that guide the LLM to cite sources, avoid hallucination, and stay within the knowledge base scope. Supports dynamic prompt adjustment based on query type (factual, analytical, creative) and document relevance.
Unique: Includes built-in prompt templates optimized for RAG that automatically format retrieved documents and inject citation instructions. Supports conditional prompt branches based on document relevance scores, enabling adaptive prompting without manual logic.
vs alternatives: More sophisticated than simple string concatenation because it handles edge cases (empty results, conflicting sources) and includes guardrails; more flexible than fixed prompts because templates are parameterized and composable.
+4 more capabilities