AI Video Workflowg Automation

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@SmartCodeDev

Hello, I can design and implement this end-to-end video processing pipeline as a fully automated, self-hosted workflow that runs reliably on your Windows VM. I will build a modular system that watches your NAS, ingests new videos, and processes them through a controlled pipeline using tools like ffmpeg, Python services, and queue-based orchestration for stability and traceability. For AI processing, I will integrate a local LLM running in your Windows VM for orchestration, combined with optional external APIs when needed, to perform scene segmentation, sentiment analysis, and chapter marker generation in a structured JSON format. The upload layer will map processed assets, metadata, and AI outputs into your platform’s required structure and submit them via REST or GraphQL endpoints with retry logic and full audit logging. I will ensure the system is fully observable, with stage-by-stage logs, error reporting, and a single-command or scheduled trigger execution flow using a workflow orchestrator like n8n, Temporal, or a custom Python pipeline depending on your preference. The final solution will be fully self-hosted, resilient, and designed so the entire pipeline runs unattended while still giving you full control and monitoring over every stage.

@ashinnd84

Hello there, we are a team of senior Full Stack Web and Mobile App Developers, Designers and we can do this project in no time. Thanks Ashish Kumar.

@himanshus642

As a highly experienced Full Stack Developer specializing in AI Development and Workflow Automation, I am confident in my ability to create the adaptable and efficient AI Video Workflow Automation system you're looking for. With over 5 years of experience, I have worked on varied projects, building powerful solutions using Python, REST APIs, among others. In addition to my technical expertise, I am well-versed with creating self-hosted infrastructures and managing local LLMs in Windows VMs, which aligns perfectly with your preference. Ensuring a comprehensive approach, I will not only encode your videos into multiple bitrates and transcode them but also incorporate various AI's to improve the overall efficiency further. My experience with Chatbots and OpenAI can assist with your sentiment analysis and scene segmentation needs. Choosing me for this project guarantees you a milestone-driven approach to ensure optimal integration of all elements while maintaining the clarity of command execution throughout the entire workflow. By hiring me, you'll be roping in a dedicated professional who can easily identify and resolve potential issues, manage clean documentation through proper logging and minimize manual intervention every step of the way. Let's discuss how we can make your project a success together!

@manoval09

Hi, You need an automated pipeline that discovers videos on your NAS and pushes them to a target platform — the tricky part is usually reliable file-watching plus graceful handling of large uploads without choking the network. I'd build this in Python using `watchdog` for real-time NAS directory monitoring and `ffprobe` (via `ffmpeg-python`) to validate each file before upload. The upload layer would use chunked multipart transfers with retry logic — the exact implementation depends on the target platform's API (YouTube Data API v3, Vimeo, or a custom endpoint), so clarifying that upfront is the most important step. I'd wire the whole thing into a `systemd` service or cron job with structured logging to a rotating file so you can trace exactly what ran, what failed, and why. Before writing a line of code: what's the destination platform? That single answer determines 30% of the work. Once confirmed, I can have a working file-discovery-to-upload proof-of-concept in your hands within 24 hours. Best regards, Val

@souvikbasu

The tricky part here isn't the encode/upload — it's the "no API" constraint requiring AI-driven browser automation to navigate the platform UI. My approach: • Encode: Python FFmpeg wrapper with H.264/H.265 adaptive bitrate profiles, triggered by watchdog monitoring your NAS folder • Legal docs: Named entity extraction to match video files to their corresponding model releases/licenses, flag missing docs before upload • AI analysis: PySceneDetect for scene segmentation + local Ollama LLM (running in your Windows VM) for sentiment scoring and chapter marker generation • Upload: Playwright-based browser automation with vision-guided element detection to handle the platform without an API — navigates, fills fields, and confirms upload • Orchestration: One-command pipeline with structured JSON logs per stage Key question: What platform are you uploading to? That determines how I build the browser automation layer.

@utsav007pandya

Hello, Sir. Lets connect and get started. I completely understand your requirement and I’m confident in building a fully automated self-hosted video processing pipeline for your workflow. I can handle FFmpeg encoding, AI-based analysis, legal document matching, metadata handling, and automated upload flow inside a Windows VM environment. Recently, I worked on a similar automation system involving video processing, AI workflows, local LLM integration, metadata handling, and automated platform actions with logging and monitoring. My approach: ✅ Automate NAS video detection and processing pipeline ✅ Encode videos with FFmpeg into required formats/length ✅ Integrate AI for sentiment analysis and scene segmentation ✅ Match and validate legal/release documents automatically ✅ Build automated upload workflow with logging and error handling ✅ Configure fully self-hosted setup with local LLM support As a professional Full Stack & Automation Developer, I focus on stable workflows, scalable architecture, secure automation, and clear progress updates with on-time delivery. Lets connect on chat to discuss workflow, AI stack, and timeline further. Thanks.

@sagarr1999

Hi, I can design and build your fully automated self-hosted video processing and upload pipeline with AI-driven analysis and compliance handling. I've 9+ years of experience building automation systems, media processing pipelines, and AI-integrated backend workflows using tools like FFmpeg, Python, and REST/GraphQL integrations. Here is what I will do: Build an end-to-end automated pipeline that detects new videos from your NAS and processes them through encoding, AI analysis, and upload without manual intervention. Implement FFmpeg-based transcoding (H.264/H.265 with adaptive bitrates) and enforce your required duration constraints (13–20 minutes) during processing. Integrate a self-hosted AI layer (Windows VM compatible) for scene segmentation, metadata extraction, sentiment analysis, and compliance tagging, including legal document detection and validation. I’ll also design structured logging for every stage, with clear failure alerts for missing legal documents or analysis issues, ensuring full traceability across the workflow. 10 days free support after delivery Milestone-based implementation with staged pipeline delivery (encode → AI → upload automation) Best regards,

@shahidhabib791

Hello, I can build a fully automated, self-hosted video processing pipeline that runs on your NAS + Windows VM with minimal manual input and full observability. The system will automatically detect new videos, process them through FFmpeg, run AI analysis, verify legal documents, and upload everything to your target platform. Each file will first be transcoded into H.264/H.265 adaptive formats with multiple bitrates, and trimmed/structured to meet your 13–20 minute requirement. Then a compliance layer will search your internal/external storage for matching licences, model releases, or rights documents and attach them as metadata. Missing documents will be clearly flagged in logs. Next, a local LLM (running in your Windows VM) will perform scene segmentation and sentiment analysis to generate chapters and compliance notes. Optional external AI calls can enhance accuracy if needed. Finally, the system will package video + metadata + AI insights and upload via REST/GraphQL with retry logic and validation checks. The entire workflow will run via a single command or scheduled trigger, with detailed logs for each stage and clear error reporting for compliance or processing failures. best regards Habib Ullah

@Anika1802

With over 5 years of experience and a portfolio that boasts successful web and mobile app development, SaaS platforms, AI, and business automation solutions, I believe I am uniquely positioned to undertake your AI Video Workflow Automation project. My expertise in AI analyses that can run sentiment analysis and scene segmentation perfectly fits your needs; making it possible for you to easily generate chapter markers and compliance notes. Another crucial aspect of this project is the self-hosted infrastructure preference. Having worked in this environment previously, I understand the importance of local monitoring capability while ensuring seamless automation. Additionally, my strong grasp of API Development and Third-Party Integration can guarantee full automation without API as required by your platform.

@mitra36

As someone who's passionate about developing and implementing AI solutions, I understand the importance of creating efficient workflows. My company, Mitraa Technology, specializes in just that--combining automation and AI to deliver robust, precise software. With our expertise in Python and infrastructure automation tools, we're well-equipped to build your self-hosted video workflow. We'll seamlessly integrate LLM into your Windows VM and use the power of ffmpeg and other AIs to ensure quick, accurate video encoding and intelligent scene segmentation. Through sentiment analysis, we can effectively generate chapter markers for enhanced accessibility. Choose us for this project, and you'll have a comprehensive video workflow pipeline that executes consistently without any manual intervention. Your satisfaction is our priority as we strive for long-term client partnerships through quality solution delivery.

@MilaVasy

Hello. Our small web development team has found your task very interesting (since it is related to AI automation). We would like to help. We are node.js developers with experience in n8n + WhatsApp / Telegram / google sheets API (we've developed AI chatbot secretary for medical clinic). Also we've done SMTP mailing and notifications via n8n. We are sure that we can handle video conversion / generation / preparation. Feel free to contact us!

@Royalitservices

Being a Full-Stack Developer and Creative Technologist, I possess wide-ranging skills that are ideal for your AI Video Workflow Automation project. I have substantial experience in creating robust and efficient digital solutions, and I am very comfortable with C++ Programming and Python -- two languages vitally important for this project. The scope of my work includes not just the technical functionalities but also thoughtful visual design and communication. With my unique blend of technical foundation and creativity, we can achieve a pipeline that is high-performing, functionally powerful, aesthetically appealing, and conversion-focused - fulfilling all your critical requirements. One of the core aspects you mentioned is the need for one-command/trigger-based execution of the entire workflow without any manual intervention - this functionality is something I'm very familiar with and skilled at delivering. Not only will I make this system self-hosted using a Windows VM with clear logs showing each stage but also ensure it has easy-to-interpret error messages - like flagging missing legal documents or AI analysis failure.

@kritika600

Hello, I have experience with Python automation, Django, React, MySQL, and AI-based workflows. I can build a self-hosted end-to-end video processing pipeline with FFmpeg transcoding, AI analysis, metadata handling, legal document validation, and automated uploads. I understand the requirement for a fully automated workflow with detailed logging, monitoring, and local/self-hosted AI integration in a Windows VM environment. I am available to start immediately and discuss the complete workflow in detail. Thank you.

@edigaga

Hi, I can help build the end-to-end video workflow automation pipeline. For the first version, I would focus on: - watching/scanning NAS folders for new video files - transcoding files into required H.264/H.265 adaptive formats - matching licence/model release/right documents where possible - preparing metadata and upload package - logging failures clearly for manual review - building the pipeline in a modular way so upload providers can be added safely I would start with a controlled MVP around one folder structure and one upload target, then expand after testing real files. I am bidding competitively because this is a new Freelancer profile, but I can start immediately. Estimated delivery: 10 days for the first automation pipeline. Questions: 1. What NAS system/folder structure are you using? 2. Which video platform is the upload target? 3. What adaptive bitrate profiles does your player require?