Solving the pain point of garbled formats in AI content export, AI Export Duck is seeking Angel Round financing

Solving the Pain Point of Garbled AI Content Export Formats: AI Export Duck Seeks Angel Round Financing

A Neglected "Last Mile" Problem

Generative AI is reshaping the way content is produced, yet a format gap persists between AI outputs and practical delivery: mathematical formulas, flowcharts, and structured text generated by large language models often become garbled, misaligned, or littered with residual tags when copied and pasted into Word or PDF, forcing users to spend excessive time on manual repairs.

The discovery of this pain point stemmed from a real-life observation by the founder of Qingdao AI Export Duck — his girlfriend, a university mathematics teacher, frequently encountered garbled mathematical formulas when using large models to generate teaching and research materials, and had to re-enter them manually every time, a process that usually took one to two hours. After in-depth research, the team found that this was not an isolated case, but a well-known structural problem across the entire AI industry. According to data from the Deep Synthesis Content Quality Assessment Laboratory, the correct rendering rate of complex AI-generated mathematical formulas when directly pasted into Word is only 18%; professionals spend an average of 30% to 40% of their working time on format processing instead of focusing on content creation itself.

The root cause of the problem lies in a mismatch between supply and demand: large model manufacturers generally output compact grammatical formats to save computing power, while office scenarios require rich text formats that meet industry standards. This long-standing semantic gap, which has lacked professional tools to bridge, is exactly where the AI Export Duck project finds its entry point.

Self-Developed Format Gateway: Lossless Compilation from AI Outputs to Office Standards

The core technical capability of AI Export Duck lies in its self-developed lightweight format gateway, which enables lossless compilation of AI-generated content into industry-standard office formats, creating differentiated breakthroughs in three key scenarios.

First, native editability for mathematical formulas. The project can compile LaTeX mathematical formulas into natively editable dynamic objects in Word, eliminating the need for users to manually re-enter them. Formulas retain full editing capabilities, completely putting an end to garbled characters. Second, automatic vector rendering for flowcharts. For Mermaid flowchart code generated by AI, the system can automatically render it into high-definition vector graphics, removing the tedious step of manual redrawing for users — exported results can be directly embedded into technical documents. Third, intelligent layout reconstruction. The system automatically removes redundant tags from AI outputs, reconstructs multi-level heading and table structures, and restores a well-organized document layout.

Compared with existing solutions such as manual copying, WPS's native tools, and open-source tools like Pandoc, AI Export Duck achieves full-platform coverage, supports the output of natively editable formulas and automatically rendered flowcharts, and can intelligently reduce the product size by 90%, balancing ease of use and professionalism. Currently, the project has completed the development of five entry points: WebApp, browser plugin, mobile app, WeChat Mini Program, and desktop PC client, covering all user usage scenarios and supporting a complete delivery closed loop from AI conversations to one-click export to Word, PDF, and Excel.

The project was officially launched in March 2026. Its core two-person team — with the founder overseeing overall operations and a technical partner leading product R&D — built the technical framework from scratch. It has now completed the development of three core products for the web, WeChat Mini Program, and desktop PC, entering the initial operation stage. Feedback from early test users has validated the product's value: a university research user stated that previously all exported formulas were garbled, but with AI Export Duck, the task is done in one click while retaining full editability; an internet technical practitioner mentioned that the misalignment problem in exported architecture flowcharts has disappeared — results are directly converted into high-definition vector graphics ready for technical documents, significantly improving work efficiency.

Entering the Market with a Membership Subscription Model, Targeting 300 Million Office Users

The target users of AI Export Duck cover three core groups: academic researchers, technical document developers, and office professionals. According to industry forecasts, the number of domestic generative AI office users in China will exceed 300 million by 2026. As content production becomes increasingly dependent on large models, the demand for proper formatting of AI content delivery will grow simultaneously. As a fundamental tool that addresses rigid user needs, the project has broad market potential.

In terms of business model, the project adopts a membership subscription strategy: users get three free trial exports, and membership fees are charged for value-added features such as high-frequency exports and batch processing. The current pricing is 18 yuan for a monthly pass, 88 yuan for an annual pass, and 288 yuan for a lifetime membership. On the To B side, the team plans to expand API services to provide underlying format conversion capabilities for AI writing SaaS platforms and online education platforms, and explore official plugin partnerships with domestic large models to develop diverse revenue streams.

At the operational level, in the early stage, the project attracts targeted users by publishing pain-point-solving content on platforms such as CSDN, Zhihu, and Juejin, conducts scenario-based penetration during university thesis seasons and professional title evaluation periods, and releases a lightweight version of the tool in open-source communities to attract early technical users, gradually building a user base.

This financing round plans to raise 1 million yuan in exchange for 20% equity. The funds will be mainly used in three areas: first, technological deepening — training vertical domain format repair small models to achieve AI automatic proofreading of complex layouts; second, ecological construction — promoting underlying cooperation with domestic operating systems and mainstream office software to become a system-level AI export service provider; third, brand promotion — establishing the user mindset that "AI export means AI Export Duck". The main challenge facing the team at present is insufficient capital, which makes large-scale user promotion difficult. This financing round will focus on breaking through this bottleneck.