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DeepTagger Screenshot
DeepTagger

DeepTagger

From Documents to Structured Data with Interactive Labelling

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408 Upvotes

About

Deep Tagger is an AI-powered platform designed to automate data extraction from various document types using an intuitive "Highlight-and-Label" interface. It allows users to train custom AI models without coding or complex templates, leveraging a core LLM for deep understanding and contextual interpretation. The platform supports complex documents with nested structures, accepts multiple file formats (PDFs, Word Docs, JPG, PNG, Text files), and offers a free tier for processing up to 200 pages. It aims to transform documents into structured, actionable data, accelerating workflows and simplifying data capture.


Color Palette

Background White

#FFFFFF

70%

Text Black/Dark Grey

#000000

20%

Accent Blue

#007bff

10%

Typography

Modern Sans-serif

Heading

Aa

Modern Sans-serif

Body

Aa

Design Review

The design of the Deep Tagger landing page is exceptionally clean, modern, and professional, aligning well with the product's promise of simplicity and automation. The predominant use of white space creates a sense of clarity and ease, making the content highly readable and visually appealing. The typography, a consistent modern sans-serif, contributes to a crisp and accessible user experience for both headings and body text. An accent blue is strategically used for calls to action, links, and visual highlights within the interactive GIFs, effectively guiding the user's eye without overwhelming the minimalist aesthetic. The animated GIFs demonstrating the 'Highlight-and-Label' process are particularly effective, providing clear visual cues of the product's intuitive usability. Overall, the design reinforces trust and efficiency, making the complex task of data extraction appear straightforward and user-friendly.

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