Hello! I’m looking for a thoughtful and reliable developer to create a simple Document AI application for a personal collection of articles, academic papers, and book chapters. The app should accept images containing text and use both **OCR and an LLM** to: 1. Extract the text from each image while preserving headings, paragraphs, and reading order as accurately as possible. 2. Organize the extracted text into logical sections. 3. Generate a concise summary for each section. 4. Generate an overall summary for the full article, paper, or chapter. 5. Save the original extracted text and summaries in an easy-to-use format, such as Markdown, TXT, or DOCX. The extracted text may later be used with text-to-speech software, but text-to-speech is **not part of this project**. The images may vary in quality and layout, so I would appreciate someone who can recommend an appropriate OCR approach and thoughtfully handle issues such as page order, columns, headings, footnotes, and repeated headers or page numbers. ## Preferred Qualifications * Experience with Python and OCR tools or services * Experience integrating LLM APIs * Familiarity with document structure, reading order, and long-document summarization * Ability to create a simple, approachable interface * Clear and patient communication * Respect for the privacy of uploaded documents ## Initial Deliverables * A working application that can process one document containing multiple images * Extracted and organized text * Section-level summaries * One complete document or chapter summary * Exportable output files * Basic setup and usage instructions * Source code I would be happy to provide several sample images so we can first confirm the OCR quality and discuss the best approach before building the full application. When applying, please share: * A brief explanation of how you would approach the project * Examples of similar OCR, document-processing, or LLM applications you have created * Which OCR and LLM technologies you would recommend * Whether you would suggest a desktop, local web, or cloud-based application * Any questions you have about the documents or desired output Thank you very much for taking the time to read this. I’m hoping to find someone who is careful, kind, and genuinely interested in making the application accurate and pleasant to use.

## Job Title **Document AI / OCR App for Digitizing Magazine Images** ## Job Description I am looking for a developer to build a simple document-processing application for a large collection of magazine pages saved as PNG images. The application should process the images, extract the magazine text, identify important article information, and create organized digital files. The goal is to preserve the content while allowing the original image collection to be removed from storage. Magazine pages may include multiple columns, headings, captions, page numbers, advertisements, and images, so the solution should handle common magazine layouts. ## Required Features The application should: * Accept individual PNG images or folders containing many images. * Group multiple pages belonging to the same article when possible. * Extract the article text using OCR or a document AI service. * Preserve the correct reading order for multi-column layouts. * Identify: * Article title * Author * Publication date * Magazine or publication name, when available * Generate: * Full extracted text * A concise summary * A list of key points * Clearly mark information that could not be identified instead of inventing it. * Process large batches without requiring each image to be handled manually. * Save the results in an organized folder structure. Preferred output formats include Markdown, TXT, or JSON. A suggested article file might look like: ```markdown # Article Title **Author:** Author Name **Publication:** Magazine Name **Publication Date:** Month Day, Year **Source Images:** page_001.png, page_002.png ## Summary Brief summary of the article. ## Key Points - First important point - Second important point - Third important point ## Extracted Text Full article text in the correct reading order. ``` ## Deliverables * A working application or command-line tool; perferrablly a desktop application * Source code * Installation and setup instructions * Instructions for processing new folders of PNG images * Configurable output location and file format * Error logging for images that cannot be processed * A test run using a sample set of magazine images * Documentation of any external APIs, AI models, or ongoing usage costs ## Preferred Qualifications * Experience with OCR, document AI, or intelligent document processing * Experience handling multi-column magazine, newspaper, or scanned-document layouts * Python development experience * Experience with tools such as Tesseract, PaddleOCR, AWS Textract, Google Document AI, Azure Document Intelligence, or multimodal AI models * Experience producing structured metadata and AI-generated summaries * Ability to create a reliable batch-processing workflow ## When Applying Please include: 1. A brief description of your proposed technical approach. 2. Which OCR and AI tools you would use. 3. Whether the application would run locally, use cloud services, or use a combination of both. 4. Any estimated API or processing costs for a large image collection. 5. Examples of similar OCR or document-processing projects. 6. How you would handle articles that span multiple images or pages. 7. How you would verify the reading order and reduce OCR errors. Sample images may be sent upon request.

About Us Paragon International, Inc. is a U.S.-based manufacturer of commercial concession equipment and food service products. We receive purchase orders from customers such as Amazon, Home Depot, distributors, school systems, and other commercial customers. Orders arrive by email in many different formats, including PDFs, Word documents, Excel spreadsheets, scanned documents, and occasionally photographed purchase orders. We are looking for an experienced AI Automation Engineer to design and build a production-ready system that automates our entire order intake process. This is not a simple chatbot project. We need someone who has successfully built business automation systems that combine AI, OCR, document processing, APIs, and workflow automation. Project Overview The system will monitor one or more Gmail inboxes continuously and automatically process incoming emails and attachments. The workflow should: * Monitor Gmail 24/7 for new incoming emails. * Download all attachments automatically. * Read: * PDF files * Microsoft Word documents * Excel spreadsheets * Scanned PDFs * Image files (JPG, PNG, TIFF, etc.) * Photographs of purchase orders * Use OCR when required. * Use AI to determine whether the email is: * Purchase Order * Quote Request * Cancellation * Return/RMA * Customer Inquiry * Other * Identify the customer automatically. * Extract all order information into a standardized data structure. * Detect duplicate purchase orders. * Automatically print valid purchase orders to our network printer. * Save documents into organized folders. * Rename files using a consistent naming convention. * Move processed emails into Gmail folders/labels. * Generate logs for auditing and troubleshooting. ## Future Phases The initial project focuses on reliable document processing and printing. Additional phases may include: * Sage 100 ERP integration * Automatic sales order creation * Inventory verification * Customer acknowledgment emails * Shipping workflow automation * Dashboard and reporting * AI exception handling * Multi-location printing We are looking for a long-term development partner who can continue improving the system over time. ## Required Skills Please apply only if you have strong experience with most of the following: * OpenAI API / ChatGPT API * Gmail API * OCR technologies (Tesseract, Azure Document Intelligence, Google Vision, AWS Textract, or similar) * Intelligent Document Processing (IDP) * PDF parsing * Workflow automation * Python * REST APIs * Windows automation * Network printing * Error handling and logging * AI document classification Experience with the following is a significant advantage: * n8n * Microsoft Power Automate * Make.com * ERP integrations * Sage 100 * Purchase Order processing * Manufacturing or distribution businesses ## Deliverables The completed solution should: * Run continuously with minimal supervision. * Be reliable enough for production use. * Handle errors gracefully. * Be well documented. * Be easy for our staff to maintain. * Be scalable as our order volume grows. ## To Apply Please include: 1. A description of similar automation projects you have completed. 2. Which automation platform you recommend (Python, n8n, Power Automate, Make, or another solution) and why. 3. Examples of AI document processing or OCR projects you've built. 4. Your experience integrating with ERP systems. 5. Your estimated timeline. 6. Your hourly rate or fixed-price proposal. Please begin your proposal with the phrase: **"I have built AI document automation systems."** This helps us identify applicants who have carefully read the project description. We are looking for a long-term partner, not just someone to complete a single project. If this project is successful, additional work will include ERP integration, warehouse automation, customer service automation, purchasing automation, and AI-driven business process improvements.

Python Developer Needed – Claude API, PDF Processing & AI Automation I’m looking for an experienced Python developer to help me complete and troubleshoot an AI document-processing workflow. I have approximately 20,000 pages of OCR’d medical records that need to be processed using the Claude (Anthropic) API. The goal is to identify specific mental health evidence while ensuring every extracted finding includes the exact PDF filename and page number so it can be verified and used in court. Current Status * Claude API account is already set up and paid for. * PDFs are already OCR’d and page numbered. * I have an existing Python script that uploads documents and processes them, but it needs improvement. What I Need * Debug and improve the existing Python code. * Ensure page numbers and source filenames are always included. * Improve reliability and error handling. * Optimize processing of a large document set. * Help generate a final organized evidence list. Required Skills * Python * Claude (Anthropic) API or other LLM APIs * API integration * PDF/document processing * OCR workflows * Prompt engineering * Debugging and automation Experience working with legal, medical, or other large document collections is a plus. This is an urgent project, and I’m looking for someone who can start immediately. When applying, please include: * Similar projects you’ve completed * Your experience with Python and AI APIs * Your hourly rate * Your availability over the next couple of days I’m in Las Vegas

## Overview I’m looking for an experienced Android developer to build the MVP for a new AI\-powered productivity application\. The application combines Android screen capture, computer vision, OCR, and large language models to transform visual information into structured, searchable data\. The product is currently in stealth mode, so additional details will be shared after an NDA is signed\. --- ## Required Skills - Kotlin - Jetpack Compose - Android MediaProjection - Foreground Services - Google ML Kit \(or equivalent OCR\) - Room Database - REST API integration - Material Design - Performance optimization Bonus experience: - OpenAI or other LLM APIs - Computer Vision - Image processing - Accessibility Services - Background processing - On\-device ML --- ## MVP Responsibilities The initial phase includes: - Build a native Android application - Implement secure screen capture using Android\-approved APIs - Process live screen frames efficiently - Extract text from images using OCR - Optimize processing for speed and battery life - Integrate with an external AI API - Store structured results locally - Build a clean, modern Jetpack Compose interface - Prepare the application for future scaling --- ## What I’m Looking For - Senior Android engineer - Experience building production applications - Clean architecture - Strong communication - Ability to recommend technical solutions - Focus on performance and maintainability --- ## Proposal Requirements Please include: - Examples of Android applications you’ve built - Experience with MediaProjection - Experience with OCR or computer vision - Experience integrating AI APIs - Estimated timeline - Estimated budget - Your preferred architecture for an application of this complexity Please begin your proposal with: **“Android AI Project”** This helps filter out automated proposals\. --- ## Confidentiality The application idea and business model are confidential\. A detailed functional specification, wireframes, and workflow documentation will be provided only to shortlisted candidates after execution of a mutual NDA\. I’m looking for a long\-term development partner rather than someone simply completing a one\-time coding task\.

We're a growing CPA firm (bookkeeping, payroll, sales tax, tax prep, CFO/advisory) looking for a technology partner — not traditional IT support, but someone who enjoys understanding business processes, recommending better ways to work, and helping us continually improve. Our goal: automate repetitive work, make data readily available, and free up our team to focus on clients instead of manual tasks. Current Stack QuickBooks Online, Odoo, Excel, Transaction Pro, Lacerte, TaxDome, Avalara, Google Workspace, ChatGPT/AI tools. We're also evaluating Juno (replacing GruntWorx) and moving document delivery back to TaxDome from SafeSend. Open to better tools if you have recommendations. Some Items We Have in Mind Build a firm-wide Power BI (or similar) dashboard to monitor every client — P&L, cash, AR/AP, reconciliation status, open items, tax/payroll/sales tax status — without opening QuickBooks for each client Automate document processing (OCR/AI extraction from PDFs into Excel/Transaction Pro imports) Design and optimize TaxDome workflows (onboarding, bookkeeping, payroll, sales tax, tax prep) Teach our team to use and maintain what you build. Engagement Details Start: Paid discovery phase — learn our operations, identify top automation opportunities, prioritize by ROI, build one proof-of-concept, deliver a technology roadmap Then: Ongoing project-based work if it's a good fit for both sides Data handling: You'll work with confidential client financial/tax data — NDA required To Apply, Tell Us 1. Your experience automating accounting or tax firms (or similar regulated/finance environments) 2. An AI/OCR or dashboard project you're proud of 3. Your hourly rate and current availability 4. The first three improvements you'd likely investigate after reading this posting

Full-Stack Developer Needed — Agricultural SaaS / Grain Management Web App MVP Project Overview I am looking for an experienced full-stack developer to build the first working browser-based MVP of GrainTrack, a grain inventory, contract, and delivery management application for farmers. The long-term goal is to give farmers one place to track: Grain inventory Grain contracts across multiple elevators/buyers Bushels sold Bushels delivered Remaining contract obligations Bushels still available to sell Weighted average selling price Physical grain by storage location Contract documents Scale tickets Future phases will include AI-powered contract and scale-ticket scanning. For this project, I am ONLY looking to build Phase 1. A starter project package and detailed product specifications are already available. Phase 1 Goal Build a secure, responsive web application that allows a farmer to: Create an account Create a farm Select a crop year Select commodities Enter estimated production Create buyers/elevators Create delivery/storage locations Enter grain contracts manually View contract details Automatically calculate the farm's grain position View that information on a professional dashboard The application should work well on desktop, tablet, and mobile browsers. Core Dashboard The dashboard should display grain information by commodity. Example: 2026 Corn Expected Production: 180,000 bu Physical Inventory: 127,750 bu Contracted: 110,000 bu Delivered: 32,500 bu Remaining on Contracts: 77,500 bu Estimated Available to Sell: 70,000 bu Physical Grain Not Committed: 50,250 bu Percent Sold: 61.1% Weighted Average Final Price: $4.82/bu The dashboard should use clean cards, tables, progress indicators, and alerts similar to a modern SaaS dashboard. UI mockups will be provided. Important Grain Calculations Critical calculations must be performed on the backend rather than independently in the browser. Estimated Available to Sell Expected Production - Total Active Contracted Bushels Example: 180,000 - 110,000 = 70,000 bu Percent Sold Total Contracted ÷ Expected Production × 100 Example: 110,000 ÷ 180,000 = 61.1% Remaining Contract Quantity Adjusted Contract Quantity - Confirmed Delivered Quantity Physical Grain Not Committed Current Physical Inventory - Remaining Contract Obligations Weighted Average Contract Price Sum of: Contract Quantity × Final Cash Price divided by: Total Final-Priced Bushels A simple average of contract prices must NOT be used. Detailed calculation specifications will be provided. Commodities The application must NOT be hard-coded only for corn, soybeans, and wheat. Default commodities should include: Corn Soybeans Wheat Barley Oats Grain Sorghum / Milo Canola Sunflowers Rice The system must also support: + Add Another Commodity Custom commodities should support: Commodity name Default unit Optional standard pounds per bushel Notes All contracts, inventory, deliveries, reports, and calculations should reference a commodity ID. Crop Years Records must be separated by crop year. Examples: 2025 Corn 2026 Corn 2027 Corn The system must NOT assume crop year based on delivery date. This is important because farmers may carry older grain into a new calendar year. Buyers / Elevators Farmers need to create and manage multiple grain buyers. Examples: ABC Elevator XYZ Grain Local Co-Op A buyer may have multiple delivery locations. Locations Locations may include: Farm Bin Grain Bag Flat Storage Elevator Processor Terminal Other Example: ABC Elevator could have: Newark Utica Johnstown as separate delivery locations. Contracts Farmers must be able to manually create grain contracts. Required fields should include: Farm Crop year Commodity Buyer Delivery location Contract number Contract date Contract quantity Contract type Cash price Futures price Basis Futures month Delivery start date Delivery end date Notes Status Contract types should include: Fixed Price Basis HTA Average Price Minimum Price Other Contract Detail Screen Each contract should display: Buyer Location Commodity Crop year Contract number Contract quantity Pricing information Delivery period Delivered bushels Remaining bushels Percent delivered Estimated contract value Status Notes Later phases will attach scanned documents and scale tickets. Pricing Rules Contracts without a finalized cash price must NOT incorrectly affect the final weighted average cash price. Example: 20,000 bu basis contract Basis = +$0.10 Futures = Open This contract counts toward: Total Contracted Bushels but does NOT count toward: Weighted Average Final Cash Price until the cash price is finalized. The architecture should allow pricing components to be expanded later. Database Preferred database: PostgreSQL The database should be relational and designed to support future expansion. Core entities include: Users Farms Farm Users Crop Years Commodities Farm Crops Buyers Locations Contracts Contract Pricing Inventory Transactions Deliveries Delivery Allocations Documents Alerts Audit Records A database architecture specification will be provided. Preferred Technology Preferred stack: Frontend React / Next.js Backend Next.js API or Node.js Database PostgreSQL Authentication A secure managed authentication solution is acceptable. Hosting Modern managed cloud hosting. I am open to recommendations if you believe another stack would be materially better, but please explain why. Architecture Requirements The application should maintain clear separation between: Frontend UI and user interaction Backend/API Business logic and permissions Database Source records Calculation Engine Derived grain-position calculations Critical financial/inventory calculations should NOT be scattered throughout frontend components. Phase 1 Deliverables The developer should deliver: Working browser-based GrainTrack application Responsive desktop/tablet/mobile interface Authentication Farm setup Crop-year management Standard and custom commodities Buyer management Location management Estimated production entry Manual contract entry Contract list Contract detail screen Working calculation engine Grain-position dashboard PostgreSQL database Database migrations Sample/demo farm data Automated tests for important calculations Deployment to a private test environment Source code in my GitHub repository Setup/deployment documentation Demo Data The application should include a demo farm so functionality can be tested immediately. Example: Farm: Greenfield Farms Crop Year: 2026 Corn Expected Production: 180,000 bu Contracts: Multiple contracts totaling 110,000 bu Expected dashboard result: Contracted: 110,000 bu Percent Sold: 61.1% Estimated Available to Sell: 70,000 bu The developer should create automated tests verifying these calculations. Acceptance Tests The project will be considered successful when the following scenarios work correctly. Test 1 — Production Farmer enters: 180,000 bu expected 2026 corn production. Dashboard displays: 180,000 bu expected production. Test 2 — Contracts Farmer enters contracts totaling: 110,000 bu. Dashboard displays: 110,000 bu contracted. Test 3 — Available to Sell System calculates: 180,000 - 110,000 = 70,000 bu Estimated Available to Sell. Test 4 — Percent Sold System calculates: 110,000 ÷ 180,000 = 61.1% Sold. Test 5 — Weighted Average Price Multiple contracts with different quantities and prices produce the correct bushel-weighted average. Test 6 — Basis Contract A basis contract with open futures: counts toward contracted bushels does NOT count toward final cash-price average Test 7 — Cancelled Contract A cancelled contract does not count toward active contracted bushels. Test 8 — Custom Commodity Farmer can select: + Add Another Commodity create a custom commodity, and use it throughout the application. NOT Included in Phase 1 Please do NOT include the following in your Phase 1 quote unless clearly identified as an optional add-on: AI contract scanning AI scale-ticket scanning Native iPhone app Native Android app Live futures prices Grain marketing recommendations Crop insurance Farm accounting Field management Equipment management Weather Settlement processing Payment processing These are potential future phases. Future Phase 2 Phase 2 is expected to add: Physical inventory Farm bins/storage locations Deliveries Scale tickets Contract allocations Remaining contract balances Physical Grain Not Committed calculation Inventory coverage Alerts Strong performance on Phase 1 could lead directly to Phase 2. Future Phase 3 — AI Document Scanning A major future feature will allow farmers to photograph grain contracts and scale tickets. Contract Scan Farmer takes photo. AI extracts: Buyer Location Contract number Commodity Crop year Quantity Price Basis Futures Delivery period Farmer reviews and confirms before a contract is created. Scale Ticket Scan Farmer photographs scale ticket. AI extracts: Buyer Location Ticket number Date Commodity Weights Bushels Contract number The system suggests the appropriate contract. Farmer confirms before inventory or contract balances change. Experience with OCR, document AI, computer vision, or LLM/vision APIs is therefore a significant plus. Security The application will eventually contain important farm business information. Required practices include: HTTPS Secure password/authentication handling Farm-level authorization Secure environment variables Database backups Secure document architecture No cross-farm data exposure Auditability for important changes Source Code & Ownership This is important. All source code produced for this project will be owned by me upon payment. The developer must work in a GitHub repository controlled by me. I must have administrative access to: GitHub Hosting Database Authentication service Storage Domain/DNS when applicable Any third-party services created specifically for GrainTrack The project must not depend on developer-owned accounts that I cannot access. Any third-party/open-source libraries must be properly licensed for commercial use. Documentation At completion, provide documentation covering: Local development setup Environment variables Database setup Database migrations Deployment Authentication configuration How to add commodities How calculations work How to run automated tests Another competent developer should be able to take over the project using this documentation. Developer Qualifications Please apply if you have strong experience with: React Next.js TypeScript PostgreSQL Relational database design SaaS applications Authentication Responsive web applications API development Automated testing Cloud deployment Strong bonus experience: Agricultural software Inventory systems Commodity/grain systems Financial applications OCR Document processing AI/vision APIs Agricultural experience is helpful but NOT required. I can provide the grain-industry/business logic. Budget & Contract Structure I prefer a: Fixed-price Phase 1 project with milestone payments. Target budget: $3,000–$8,000 Please do not simply bid the maximum budget. Provide your proposed fixed price based on the specifications above. If you believe the scope requires more or less, explain why. Suggested Milestones Milestone 1 — Foundation Project setup Authentication PostgreSQL Farm setup Crop years Commodities Milestone 2 — Grain Data Buyers Locations Production estimates Contract entry Contract management Milestone 3 — Calculation Engine Contract totals Available to sell Percent sold Weighted average price Pricing-status rules Automated tests Milestone 4 — Dashboard Dashboard UI Responsive layout Contract detail Filters Demo farm Milestone 5 — Deployment & Handoff Private production-like deployment Testing Bug fixes Documentation Full repository/account handoff Payments should correspond to accepted working milestones rather than elapsed time. What I Will Provide I will provide: Product vision Dashboard mockups Contract screen mockups Inventory mockups Reports mockups Database architecture Business rules Calculation-engine specification API/backend specification Starter project package Example grain contracts/data Grain-industry guidance The developer is not starting from only an idea. Much of the product and business logic has already been defined. Application Instructions Please begin your proposal with: GRAINTRACK This confirms you read the complete posting. Then answer these questions: 1. Show me 2–3 SaaS applications you have personally built or substantially contributed to. Explain exactly what you built on each project. 2. Describe your experience with: Next.js + TypeScript + PostgreSQL 3. How would you structure the calculation engine so critical grain calculations are not duplicated throughout the frontend? 4. How would you handle a basis contract where the basis is established but the futures price is still open? I am not necessarily looking for grain-industry terminology. I want to see how you think about partially complete pricing data. 5. How would you design the database so one farmer can have: Multiple farms Multiple crop years Multiple commodities Multiple buyers Multiple delivery locations without mixing data between farms? 6. How would you prevent one customer's farm data from ever being visible to another customer? 7. What automated tests would you write for the calculation engine? 8. What would your proposed technology stack be? If different from the preferred stack, explain why. 9. What is your fixed-price quote for Phase 1? 10. What timeline would you propose? 11. Are you personally doing the work, or will any portion be subcontracted? If subcontracted, explain which parts. 12. Have you worked with OCR, document AI, or vision/LLM APIs? This is not required for Phase 1 but will matter for future phases. Important I am looking for someone who could potentially continue through Phases 2 and 3 if Phase 1 goes well. I value: Clean architecture Accurate calculations Communication Documentation Maintainable code more than adding unnecessary features quickly. The goal of Phase 1 is to create a solid foundation for a commercial agricultural SaaS product—not to build every possible GrainTrack feature at once.

I’m looking for a developer to build an app that converts scanned recipes from books and magazines into structured, editable text. The app should: * Accept common image formats such as JPG and PNG * Extract text accurately using OCR * Separate each recipe into: * Recipe title * Backstory or introduction * Ingredients * Instructions or steps * Preserve quantities, measurements, temperatures, and step order * Export the results to a practical format such as Markdown, Word, JSON, or plain text * Allow the extracted text to be reviewed and corrected The scans may contain different layouts, fonts, columns, and image quality levels. Please include: * Your proposed approach and technology * Examples of similar OCR or document AI work * Estimated cost and timeline * Any sample images you would need to evaluate accuracy before development

I need someone who can help me build out an internal AI workflow that collects hundreds of large PDFs, extracts dozens of data points from the PDFs, and stores them in a database. The workflow should be able to handle large volumes of data efficiently and accurately. Experience with AI and data processing is essential.

We need a lightweight, reliable Python script/tool that ingests multi-page PDF medical records, uses an LLM API (OpenAI, Claude, or Gemini) to extract structured clinical events, deterministically sorts them chronologically, and outputs a formatted Markdown/Word narrative report with page-level citations. Key Deliverables: -PDF parsing and text/OCR handling (handling digital exports and scanned records). -LLM integration using structured output (JSON schema) with prompt engineering for medical accuracy. -Python-based chronological sorting and deduplication. -Export engine generating clean .docx or .md reports. -A simple user interface (e.g., Streamlit desktop app or local command-line script) so a non-technical user can drop in a PDF and click "Generate". -Clear setup instructions and 30 days of troubleshooting support. Required Skills: -Strong Python (PDF libraries like PyMuPDF/pdfplumber, pydantic, Streamlit). -Experience with LLM APIs (Anthropic, OpenAI, or Google Cloud Vertex AI) and structured JSON outputs. -Familiarity with healthcare data handling best practices (HIPAA/security awareness).