DeepTagger
DeepTagger is a no-code, AI-powered document processing platform that turns any documents (PDFs, images, Word, etc.) into structured, usable data through an intuitive “highlight-and-label” interface. You upload your files; highlight the pieces of data you care about; train the model via examples rather than templates; then run predictions, export results, and refine accuracy. It handles complex/nested structures (e.g., line items within invoices, tables within tables), supports scanned documents and low-quality images via strong OCR, and offers features like splitting multi-document PDFs, intent/context understanding, and position-aware extraction (so if the same phrase appears many times, DeepTagger can distinguish which instance to pull). Pricing is usage-based with a free tier processing up to 200 documents; higher tiers unlock features like batch prediction, nested schemas, priority support, multi-tenant architecture, and enterprise-grade compliance.
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ManyPI
ManyPI is a modern web data extraction and API generation platform that turns any website into a type-safe, structured API with schema definition, extraction, transformation, and synchronization built into one system, enabling developers and data teams to reliably gather clean JSON data without building custom scrapers. Its AI-powered workflow lets users specify a site and the fields they need, automatically defines a schema with risk assessment, generates a production-ready API in seconds, and delivers structured data through a RESTful, developer-friendly interface with SDKs, type safety, and predictable JSON responses. ManyPI supports scalable extraction tasks, global infrastructure for performance and uptime, and integration into existing apps or pipelines via code or dashboard, and it also provides visual schema building and connectors for no-code platforms like Zapier and Make, so workflows can automate data collection, enrichment, and reporting without heavy engineering.
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Data Donkee
Data Donkee is an AI-powered web extraction platform that enables users to collect structured data from websites using natural language instead of traditional coding. It centers on an AI Web Agent that allows users to describe their data requirements in plain English and optionally define the desired output using JSON schema, after which the platform automatically builds a custom scraper. It is designed to eliminate common web scraping challenges such as maintaining fragile code, handling constantly changing websites, and scaling data collection across large or complex sources. It emphasizes consistent and reliable extraction, aiming to minimize inaccurate results while supporting dynamic site structures and large datasets. Its workflow is streamlined into three main steps: users describe the data they need, the AI generates the extraction logic, and the platform delivers clean, structured data ready for analysis or integration.
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NuExtract
NuExtract is a large language model specialized in extracting structured information from documents of any format, including raw text, scanned images, PDFs, PowerPoints, spreadsheets, and more, supporting over a dozen languages and mixed‑language inputs. It delivers JSON‑formatted output that faithfully follows user‑defined templates, with built‑in verification and null‑value handling to minimize hallucinations. Users define extraction tasks by creating a template, either by describing the desired fields or importing existing schemas—and can improve accuracy by adding document, output examples in the example set. The NuExtract Platform provides an intuitive workspace for designing templates, testing extractions in a playground, managing teaching examples, and fine‑tuning settings such as model temperature and document rasterization DPI. Once validated, projects can be deployed via a RESTful API endpoint that processes documents in real time.
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