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| Pricing | Free | Paid |
| Capabilities | 8 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Converts spoken audio into text using on-device speech recognition models that never transmit audio data to external servers. The implementation leverages browser-native Web Speech API or local inference engines (likely ONNX Runtime or TensorFlow Lite) to perform acoustic-to-phoneme mapping and language modeling entirely within the user's device sandbox, eliminating cloud transmission overhead and ensuring audio payloads remain under user control.
Unique: Implements device-local speech recognition using ONNX or TensorFlow Lite models rather than streaming audio to cloud APIs, ensuring zero audio transmission and enabling offline operation while maintaining reasonable accuracy through model quantization and on-device optimization
vs alternatives: Eliminates the privacy and compliance risks of cloud-based transcription (Otter.ai, Google Docs Voice Typing) by keeping all audio processing local, though at the cost of 5-10% lower accuracy due to smaller model sizes
Transforms raw transcribed text into semantically structured markdown by detecting natural speech patterns (pauses, emphasis, topic shifts) and converting them into markdown syntax (headers, lists, bold/italic, code blocks). The system likely uses NLP-based sentence segmentation, keyword extraction, and heuristic rules to infer document structure from spoken discourse patterns, outputting valid markdown that integrates directly with note-taking ecosystems.
Unique: Applies semantic parsing to detect speech-to-structure patterns (topic shifts, enumeration cues, emphasis markers) and automatically generates markdown hierarchy without requiring manual tagging or post-processing, differentiating from competitors that output plain text requiring manual formatting
vs alternatives: Eliminates the reformatting step that competitors like Otter.ai require by intelligently inferring markdown structure from speech patterns, enabling direct integration with markdown-based workflows like Obsidian without intermediate editing
Provides streaming transcription output as the user speaks, displaying partial results that update incrementally as new audio frames are processed. The implementation uses a streaming speech recognition pipeline (likely attention-based RNN or Conformer architecture) that processes audio chunks and emits intermediate hypotheses, allowing users to see text appear in real-time and make corrections before finalizing the note.
Unique: Implements streaming speech recognition with incremental markdown formatting updates, allowing users to see both transcription and structure emerge in real-time rather than waiting for post-processing, with built-in correction UI for immediate error fixing
vs alternatives: Provides live feedback and correction capabilities that cloud-based competitors like Otter.ai offer, but with local processing ensuring no audio leaves the device, trading some latency for complete privacy
Exports transcribed and formatted notes to multiple target formats and platforms including markdown files, Obsidian vault integration, Notion API sync, and plain text. The system implements format-specific adapters that handle platform-specific metadata (Obsidian frontmatter, Notion block structure, Notion database properties) and provides direct API integrations or file-based exports depending on the target platform.
Unique: Provides native integrations with markdown-first note-taking platforms (Obsidian, Logseq) and Notion via platform-specific adapters that preserve metadata and formatting, rather than generic file export, enabling seamless workflow integration without manual reformatting
vs alternatives: Directly integrates with popular markdown ecosystems that competitors like Otter.ai treat as secondary, making Cleft the natural choice for users already invested in Obsidian or Logseq workflows
Indexes transcribed notes locally using a full-text search engine (likely SQLite FTS or similar embedded solution) to enable fast keyword-based retrieval without cloud indexing. The system builds an inverted index of note content, timestamps, and metadata, allowing users to search across all captured notes with sub-second latency entirely on their device.
Unique: Implements local full-text indexing using embedded database engines rather than cloud search services, enabling instant search across all notes without network latency or external dependencies, while maintaining complete data privacy
vs alternatives: Provides search capabilities comparable to Otter.ai's cloud-based indexing but with zero latency and no data transmission, making it ideal for users who need fast retrieval without sacrificing privacy
Detects and labels different speakers in multi-speaker audio (meetings, interviews, group discussions) by analyzing voice characteristics and assigning speaker labels to transcribed segments. The implementation likely uses speaker embedding models (x-vectors or similar) to cluster voice patterns and assign consistent speaker IDs, then organizes note content by speaker for easier reference and attribution.
Unique: Implements local speaker diarization using voice embedding models without transmitting audio to cloud services, enabling speaker identification while maintaining privacy, with optional speaker enrollment for improved accuracy on known participants
vs alternatives: Provides speaker identification comparable to Otter.ai's premium features but with local processing ensuring audio never leaves the device, making it suitable for confidential meetings and regulated environments
Maintains precise timestamp mappings between transcribed text segments and original audio, enabling users to click on any note text to jump to that point in the recording. The implementation stores segment-level timing metadata (start/end timestamps for each sentence or phrase) and provides playback controls synchronized with note content, allowing users to verify transcription accuracy by reviewing the original audio.
Unique: Maintains segment-level timestamp mappings between transcribed text and audio, enabling click-to-play verification and audio-backed transcripts without requiring cloud storage or external services, supporting local-first workflows with full auditability
vs alternatives: Provides timestamp-based navigation and audio verification comparable to Otter.ai but with local audio storage ensuring no audio transmission, making it suitable for confidential or regulated content requiring source verification
Enables voice note capture and transcription entirely offline, storing notes locally and automatically syncing to cloud platforms (Notion, Obsidian Sync, etc.) when network connectivity is restored. The implementation uses local-first architecture with conflict-free replicated data types (CRDTs) or similar patterns to handle offline edits and ensure consistency when syncing, allowing users to work without interruption regardless of connectivity.
Unique: Implements offline-first architecture with automatic sync-on-reconnection using CRDT-based conflict resolution, enabling seamless note capture and editing without network dependency while maintaining consistency with cloud platforms, differentiating from cloud-dependent competitors
vs alternatives: Enables voice capture in offline environments where cloud-based competitors like Otter.ai are completely unavailable, with automatic sync ensuring no manual intervention required when connectivity is restored
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
Cleft scores higher at 40/100 vs LlamaIndex at 40/100. Cleft also has a free tier, making it more accessible.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities