moar

Your documents. AI ready.

Artificial IntelligenceChrome ExtensionsProductivity

▲ 95 votes1 commentsLaunched May 6, 2026

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No more "file too large." moar extracts real structure from any document and delivers clean, right-sized Markdown or CSV for every AI tool you use including ChatGPT, Claude and Gemini. Up to 95% token savings. Zero loss of meaning. Supports nine formats: PDF, DOCX, PPTX, XLSX, CSV, TXT, MD, JSON and HTML. Files up to 50 MB each. moar is built from the ground up to be 100% private. Your documents never leave your device.

AI Analysis

📝 Summary

moar transforms documents into AI-ready formats by extracting real structure and outputting clean Markdown or CSV. It supports PDF, DOCX, PPTX, XLSX, CSV, TXT, MD, JSON, HTML (up to 50MB per file), saves up to 95% tokens with zero meaning loss, and runs 100% locally for full privacy. It solves key pain points like 'file too large' errors, token inefficiency, structure loss in AI tools (ChatGPT, Claude, Gemini), and cloud privacy risks. USP is local processing combined with high compression and broad format support, delivering efficient, secure AI workflow integration and productivity gains without data leaving the device.

📈 Market Timing

In 2025-2026, AI adoption is surging with heavy reliance on LLMs for document-heavy tasks, driving demand for efficient preprocessing, token optimization, and local/privacy-first solutions. Local compute and parsing tech have matured, user concerns over data leaks and cloud costs are rising, and economic pressure favors tools reducing API expenses. This aligns perfectly with RAG/Agent trends. Excellent Timing.

✅ Feasibility

High. Technical difficulty is manageable using established client-side parsing libraries in a Chrome extension; no server infrastructure needed due to local execution. Low dev/operation costs, minimal supply chain or compliance risks (data never leaves device aids GDPR alignment), strong scalability per-user, and good team fit for AI tooling specialists. Potential challenge is maintaining accuracy across complex layouts but core concept is proven.

🎯 Target Market

Main segments: Tech-savvy knowledge workers, AI power users, researchers, legal/finance professionals, consultants (ages 25-45, high digital literacy). Industries: Technology, professional services, academia, content creation. Geographic focus: North America, Europe, East Asia. Estimated TAM for AI productivity tools ~$100B, SAM for document-AI prep ~$10B, SOM for local/privacy tools ~$500M. Core pains: token limits, structure loss, upload barriers, privacy fears. High willingness to pay for time/token-saving premium features.

⚔️ Competition

Medium. Direct competitors: 1. LlamaParse (llamaindex.ai), 2. Unstructured (unstructured.io), 3. Docling (ds4sd.github.io/docling), 4. Marker (github.com/VikParuchuri/marker). Advantages: 100% local/privacy (vs mostly cloud competitors), Chrome extension convenience, explicit 95% token savings and zero-loss structure focus across 9 formats. Disadvantages: Likely less enterprise-scale features or accuracy on highly complex layouts than specialized cloud parsers; newer entrant with smaller ecosystem.

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