Loqua vs Unstructured: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Loqua and Unstructured — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Loqua
FlowMind Technology Inc.
Desktop voice typing that turns speech into clean, structured text in any app, plus screenshot questions and voice editing.
Key features
- Global Shortcut Dictation: One shortcut invokes Loqua in any app and drops text straight at the cursor, with no window switching or waiting.
- Real-Time Cleanup: Filler words are removed, repetition is cut and phrasing is refined as you speak, so what lands on screen is ready to send.
- Automatic Structure: Loqua hears the structure in your speech and builds lists, headings and hierarchy on its own instead of making you dictate formatting.
- Mid-Sentence Translation: Speak one language and get natively phrased output in nearly 100 target languages, switching language mid-sentence.
- Capture to Ask: Select a table, chart or any screen region, speak a question about it, and get an answer, analysis, translation or summary in place.
- Ask & Edit: Highlight an existing draft, product description or note and revise it by voice rather than retyping.
- Per-App Context Intelligence: Tone and formatting adapt to the app you are writing in, available on the Pro plan.
- Privacy Defaults: Zero cloud data retention, on-device history storage, no training on user data, user-controlled dictation history, and GDPR compliance.
Best for
- Clearing a Message Backlog: Dictate Slack, email and comment replies at speaking speed instead of typing them one by one.
- Drafting Documents Hands-Free: Speak a structured draft into Notion, Google Docs or Word and get headings and lists built automatically.
- Cross-Language Correspondence: Reply to a partner or customer in their language by speaking your own.
- Understanding an Unfamiliar Screen: Capture a dense chart, table or error dialog and ask what it means without leaving the app.
- Revising Copy by Voice: Highlight a product description or draft paragraph and speak the edit you want applied.
- Coding Notes and Commit Messages: Dictate into a terminal, VS Code or IntelliJ where typing context-switches away from the code.
Unstructured
Unstructured
Open-source ETL platform that converts complex documents into structured data for LLMs and GenAI workflows.
Key features
- Multi-format Ingestion: Supports a broad set of input types (PDF, HTML, DOCX, PPTX, XLSX, EPUB, images, emails, CSV/TSV, compressed archives) to ingest documents from varied sources and normalize them for downstream processing.
- Modular Bricks and SDKs: Provides reusable, open-source building blocks (bricks) and language SDKs to assemble custom preprocessing pipelines for parsing, cleaning, and transforming document content.
- Pipeline Orchestration & Enrichments: Routes data through dynamic transformation pipelines that perform partitioning, enrichment, metadata extraction, and content normalization to produce structured outputs tailored for LLMs.
- Layout Parsing & Chipper Model: Includes layout and document structure analysis (layout parsing) to extract tables, figures, headings, and positional context from complex page layouts for accurate content segmentation.
- Chunking & Embedding Preparation: Implements intelligent chunking and embedding generation workflows to create LLM-friendly segments and vectors, improving retrieval, RAG, and semantic search performance.
- Hosted API & Local Libraries: Offers a hosted Unstructured API (API keys required) for cloud-based processing alongside open-source local libraries for on-prem or custom deployments, enabling flexible integration models.
- Enterprise Platform Capabilities: Provides production-grade Platform features—continuous ingestion, monitoring, partitioning strategies, and scalability—targeted at enterprise workflows and compliance needs.
- File-type Analytics & Metrics: Collects analytics on processed document types and transformation success to help operators measure ingestion quality and pipeline performance.
- Convert documents to structured data (supports PDFs, HTML, Word, images, tables, graphs)
- Modular components ("bricks") for building custom preprocessing pipelines
- Dynamic transformation and enrichment pipelines for routing and improving data quality
- Partitioning and chunking to prepare content for LLM consumption
- Embedding support and integration points for vectorization
- Layout parsing and inference models (separate inference repository)
- Python SDK and libraries (unstructured, unstructured-api, unstructured-inference)
- Containerized deployment options (Dockerfile present in repo) and Makefile-driven install
- Apache-2.0 open-source licensing for core libraries
- Enterprise Platform for production-grade workflows, continuous automated processing and scaling
Best for
- Preparing LLM Training & RAG Corpora: Clean, partition, and chunk large collections of PDFs, manuals, and reports into semantically coherent passages and embeddings for retrieval-augmented generation and model fine-tuning.
- Automated Document Ingestion for Knowledge Bases: Continuously ingest and transform new documents (contracts, policies, manuals) into structured records for searchable knowledge bases and Q&A assistants.
- Table and Figure Extraction for Data Pipelines: Parse complex tables, figures, and embedded images from financial reports or scientific papers to convert them into structured datasets for analytics or downstream models.
- Compliance and Contract Analysis: Extract clauses, metadata, and named entities from legal and regulatory documents to populate contract management systems and support compliance workflows.
- Invoice/Receipt Processing: Normalize and extract line-items, totals, dates, and vendor information from invoices and receipts to automate AP workflows and accounting ingestion.
- Migration of Legacy Documents: Convert large legacy document collections (scanned PDFs, archived emails, disparate formats) into structured, searchable formats to modernize enterprise data stores.
- Prototype to Production Pipelines: Use open-source bricks to prototype document parsing locally, then scale to the Unstructured Platform for continuous, monitored production processing with enterprise controls.
- Preprocessing document corpora to create high-quality input for retrieval-augmented generation (RAG) pipelines
- Extracting tables, figures, and structured fields from PDFs and scanned documents
- Continuous ingestion and enrichment of enterprise documents for knowledge bases
- Generating embeddings and chunked passages for semantic search over documents
- Receipt, invoice, and financial filings parsing (example pipelines and archived repos exist)
- Building document Q&A or chatbot applications using cleaned, structured document content