AI Automation Engineer & Technical Producer for Oncology Podcast Factory

@SahilRao1247

As an AI Creative Professional with a deep understanding of content and podcasting, I am confident in my ability to build and scale an AI Automation system that meets your needs. Drawing from my experience with platforms like Canva AI, Adobe Firefly, Photoshop AI, and more. I'll implement a meticulously designed workflow to monitor relevant oncology sources through RSS/APIs or similar tools such as Make, Zapier, n8n. This would then feed into the rightful LLMs to summarize and script short, accurate, patient- and clinician-focused episodes for your specific needs. Moreover, my proficiency extends to audio services which aligns perfectly with your project requirements from TTS tech & voice cloning to publishing 5-10 micro-shows daily on various podcast platforms and potentially social/video channels. Additionally, basic data/reporting dashboards for tracking downloads, engagement and content performance can be facilitated. With my holistic understanding of data pipelines and clear communication style, I ensure your workflow operates at its best efficiency which aligns beautifully with your vision of keeping the human touch light yet high-quality (10-20%) which will be human supported but not dominant rather powered by the prowess of automation.

@jayprakashyadav

As a highly experienced Web Developer, I have the skills to design and build your vision of an end-to-end automated oncology audio network. My grasp of recent technologies including my implementation work with Open AI API in generating auto articles align significantly with your project needs. Although I don't have specific experience related to podcasts, my general knowledge about content automation and web development can be fully transferred and applied to deliver robust solutions tailored to your requirements. Let me explain how I intend to approach this project. To start, I'll focus on building a minimum viable product (Phase 1 MVP) factory that involves continuous monitoring of relevant oncology sources, script generation through LLMs, voice cloning for quality audio production, automated publishing, basic analytics/reporting and maintaining a meticulous human review layer. As an AI enthusiast, working with platforms like Make, Zapier or n8n feels intuitive however I am flexible enough to use your preferred tools. In scaling the project, I’ll emphasize on incorporating user feedback referring patient/clinician focus details while retaining the desired efficiency level. My 15+ years in the Web development sphere enables me to adapt quickly to pertinent fields. By combining my skills as a Web developer with your plethora of oncology knowledge, we can create something that really makes a difference in the lives of many.

@moizam1

As an AI Automation Engineer and Technical Producer, your project aligns perfectly with my skillset, experience, and deep understanding of content automation. Over the past 4 years, I've honed my skills in JavaScript and Python to build intelligent systems that enhance productivity and deliver measurable results. For your Oncology Podcast Factory, my expertise in AI tools such as LLMs and content pipelines would be invaluable in monitoring oncology journals accurately, summarizing episodes, generating high-quality audio through text-to-speech/voice cloning tools. I'm experienced in implementing data-driven solutions for web development, a skill that you need to set up basic analytics and reporting for tracking downloads, engagement, and content performance. Choosing me means not only choosing professionalism but also effectiveness. We'll start by building an MVP factory during Phase 1 that's functional and easily scalable. I'd leverage automation platforms like Make, Zapier or n8n as well as custom scripts where necessary to ensure smooth workflows and minimal human effort while maintaining high quality. My commitment to continuous learning ensures I stay up-to-date with the latest AI technologies to meet your evolving needs.

@himanshus642

As an AI Automation Engineer with 10+ years of experience, I specialize in building end-to-end automated content systems—perfect for your oncology podcast factory. I combine expertise in LLMs, prompt engineering, and content pipelines with hands-on experience in podcast and audio workflows to create efficient, scalable production systems. I focus on smart automation with strong quality control, keeping the human review layer minimal yet effective (10–20%). Using tools like Make, Zapier, n8n, or custom scripts, I can build a scalable MVP that grows seamlessly as your global audience expands.

@Microlent

Hello, What specific oncology journals do you want to monitor, and how frequently should the episodes be published? I can build a high-volume automated podcast system that summarizes and scripts content from sources like PubMed and ASCO. With over 5 years of experience in AI tools, automation platforms (like Zapier and Make), and podcast workflows, I’ve successfully deployed content automation systems that optimize production and distribution. For Phase 1, I’ll create a minimum viable product that focuses on seamless content sourcing and efficient audio generation. My preferred stack includes LLMs for summarization, text-to-speech solutions for audio, and analytics dashboards to track performance. I am confident that I can streamline your podcast factory to engage patients and clinicians effectively. Let’s discuss this project in detail! Best Regards, Jenifer

@patrickag1214

Hello, I can build an automated oncology podcast network that efficiently delivers high-quality, hyper-specific content. The root of this solution lies in seamlessly integrating AI-driven summarization, voice cloning, and automated distribution to major platforms. I have extensive experience developing content automation systems, including a recent project where I designed a podcast production pipeline that scaled to publish daily episodes while maintaining a high standard of quality. My approach combines the latest in AI tools, such as LLMs for content generation and automation platforms like Zapier for streamlined workflows. For Phase 1, I would establish a robust content pipeline that monitors oncology journals via APIs, uses LLMs to create scripts, and integrates text-to-speech technology to produce audio files. I’ll set up automated publishing to platforms like Spotify and Apple, ensuring the process is efficient and user-friendly. Key metrics for downloads and engagement will be tracked through basic dashboards for ongoing performance evaluation. What specific oncology journals are your priority for monitoring? Are there particular KPIs you want to focus on for analytics? I’m ready to kick off this project and help position you as a thought leader in oncology. Let’s discuss your vision further.

@nicolasalexandro

Hey — saw your post about building an automated oncology podcast factory. The tricky part here is making a reliable pipeline that goes from journal → summary → script → voice → auto-publish, without you getting dragged into heavy manual review every day. Quick question before I suggest an approach: Do you already have any preferred tools for TTS/voice cloning (ElevenLabs, PlayHT, etc.), or are you open to choosing whatever integrates best with the automation layer (Make/Zapier/n8n + custom scripts)? I’ve built AI-driven content pipelines before (LLM summarization → scripted formats → TTS → auto-publish + dashboards), including multi-channel distribution and light human QA loops. If you can share any existing workflow notes, links to Inception Point AI setups you like, or a rough Phase 1 spec, I’ll review and outline a lean MVP factory that can reliably ship 5–10 micro-shows per day and scale from there.

@Maxxionn

Hi , I’ve carefully reviewed your job post and it’s clear you’re looking for someone with solid experience in Music, Audio Services, AI Content Creation, Automation, AI (Artificial Intelligence) HW/SW, AI Chatbot Development, Audio Editing, AI Development, Sound Design and Audio Production. This is exactly within my core expertise, and I’m confident I can deliver reliable, high-quality results. Rather than rushing into assumptions, I prefer to understand the project properly. I’d appreciate your clarification on a few points: Is the job description complete, or are there additional requirements or expectations? Do you already have any work completed, or will this be built entirely from scratch? Do you have a preferred timeline or deadline in mind? Why you can confidently work with me: Successfully completed 250+ major projects across different industries Maintained 100% positive feedback over the last 5–6 years Earned 100+ recent 5-star reviews, showing long-term client satisfaction I focus on clear communication, clean execution, and on-time delivery I work as a full-time freelancer and am available 9 AM – 9 PM (Eastern Time), ensuring fast responses and consistent progress. Due to client confidentiality, I share relevant work samples only in private chat. Let’s start a conversation so I can show you similar work and suggest the best approach for your project. Looking forward to working with you. Best regards, Arsalan Khan

@adriann05

Hi there, I understand that your main goal is to create a high-volume, automated oncology audio network to establish thought leadership through daily micro-podcasts. In my previous role, I successfully developed an automated podcast production pipeline that increased episode output by 50% while maintaining a 95% listener retention rate. Additionally, I integrated AI-driven content summarization tools that reduced script preparation time by 40%, allowing for rapid deployment across multiple platforms. To implement the requested features, I would first build a minimal viable product (MVP) that utilizes LLMs for summarization and text-to-speech tools for audio generation. I will also set up automation workflows using platforms like Zapier to streamline publishing and analytics, ensuring a smooth process for producing 5–10 episodes daily. I would be happy to discuss your needs and get started right away. Best regards, Adrian

@Hemantt841

Thank you for the detailed project description. The idea of creating a high-volume, automated oncology audio network caught my attention. As a software developer with over 7 years of experience, I believe I can help you achieve your goal of becoming a global thought leader in oncology through daily micro-podcasts. For this project, I would approach it in the following key steps: - Research and integrate AI tools such as LLMs for summarizing and scripting episodes - Use automation platforms like Zapier for monitoring oncology journals and sources - Implement text-to-speech/voice cloning tools for generating audio - Set up automated publishing of micro-shows across podcast platforms - Develop basic analytics and reporting dashboards for tracking performance In a similar project, I built an automated content generation system for a healthcare blog, resulting in a 50% increase in engagement and reach. I prefer using tools like Zapier for automation and natural language processing for content creation. For Phase 1 of this project, I would focus on building a minimum viable product (MVP) factory that can generate and publish a few micro-shows per day. As we scale, we can optimize the automation processes and improve content quality. I am genuinely interested in your project and would like to know more about your target audience and content strategy to ensure the podcast factory a

@aliyanali786

Hi, my approach is to create an automated pipeline that monitors sources like PubMed/ASCO/NEJM, uses LLMs to generate accurate micro-episode scripts, converts them to high-quality audio (e.g., ElevenLabs), and auto-publishes 5–10 daily episodes across major platforms. A light human review layer ensures medical accuracy, and a simple analytics dashboard tracks performance. Preferred stack: OpenAI/Claude + n8n automation + Python backend + premium TTS + centralized podcast hosting. Phase 1 (MVP): build core pipeline and publish consistently. Phase 2: scale into topic-specific shows and performance optimization. I assure you the system will be built professionally and scalable from day one. I also offer a money-back guarantee if the project is not completed on time. Best regards, Aliyan

@rashipo

I can build a fully automated oncology micro-podcast system that monitors key journals (PubMed, ASCO, NEJM) via RSS/APIs, summarizes content with LLMs, scripts short, accurate episodes, and generates high-quality audio with TTS/voice cloning. The system will automate publishing 5–10 micro-podcasts daily across Spotify, Apple, YouTube, and other platforms, while keeping human review light (10–20%). I will also implement basic analytics to track engagement and downloads. For Phase 1, I propose an MVP pipeline covering source monitoring, LLM summarization, TTS generation, and automated publishing. Phase 2 scales to additional platforms, advanced analytics, and workflow optimization. Stack: Python/Node.js, n8n/Make, and ElevenLabs or similar TTS tools.

@uzairbz

How will you ensure clinical accuracy and regulatory compliance at a scale of 10 episodes per day while maintaining a 15% human touch-point? Scaling oncology content is a verification challenge, not just a volume problem. Moving from raw PubMed data to high-fidelity audio requires a pipeline that handles complex medical terminology, drug nomenclature, and multi-platform distribution without manual file handling. Challenge: Standard RSS-to-LLM workflows fail in clinical contexts because general-purpose models hallucinate trial data. You do not need a simple summarizer; you need a RAG (Retrieval-Augmented Generation) architecture that validates script outputs against the original DOI to ensure p-values and dosages are never misrepresented. Execution Plan: 1. Ingestion: n8n/Python monitors PubMed/ASCO APIs for new publications. 2. Processing: GPT-4o with clinical-specific system prompts + RAG verification layer. 3. Audio: ElevenLabs API for high-fidelity voice cloning. 4. Distribution: Captivate/Transistor API for RSS; Remotion for automated YouTube visuals. 5. Quality Control: Custom Retool dashboard for rapid human sign-off. I architect high-throughput AI pipelines that treat content as structured data, ensuring 99.9% reliability. Shall we discuss the validation logic required to prevent hallucinations in your clinical summaries?

@ProfSoftStudio

Hi there, Your vision for an automated oncology audio network is a perfect use case for a modular AI pipeline. I specialize in building "Content Engines" that balance high-volume output with the 10-20% human oversight required for medical accuracy. I have extensive experience in LLM prompt engineering and workflow automation, specifically focusing on transforming technical data into digestible media. I understand that in oncology, "almost right" is wrong—I build guardrails to ensure every automated insight is grounded in your provided sources. I'm ready to scale this to 10 micro-shows a day while keeping your manual workload to a minimum. Are you available for a brief call to discuss the source-filtering logic? Best regards.

@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 I will use LLM and n8n for creating the automation system

@amadorjay

Hello, I will design and build an automated oncology podcast factory. This system will produce and publish 5-10 micro-podcasts daily. My goal is to ensure high-quality content while maintaining efficiency. I have extensive experience in building similar content automation systems. I’ve developed platforms that monitor medical journals and generate audio content using AI tools. My work has helped healthcare professionals deliver timely information to their audiences. **Solution Approach:** - Implement a robust RSS/API integration for monitoring oncology journals. - Utilize LLMs for summarizing and scripting patient-centric episodes. - Integrate text-to-speech and voice cloning tools for high-quality audio output. - Automate publishing processes across major podcast and social platforms. - Set up basic analytics dashboards for tracking performance metrics. - Maintain a streamlined human review process to ensure quality. **Smart Questions:** - What specific oncology sources do you want prioritized in the monitoring system? - How do you envision the balance between automation and human oversight? - What metrics are most critical for measuring content performance? I’m ready to get started on this project. Let’s create a leading oncology audio network together.

@novatech2210

Hello! I've been recommended by a Freelancer Recruiter. Nice to meet you. I've just completed a similar AI automation project for a healthcare client who needed to streamline their content creation process. I'm the perfect fit for your Oncology Podcast Factory as I've extensive experience with AI tools, automation platforms, and podcast workflows, including integrating LLMs for content generation, text-to-speech/voice cloning, and publishing across major platforms. My expertise spans AI content pipelines, automation tools like Make and Zapier, and basic data/reporting dashboards. In my previous project, I reduced manual work by 70% for a content creator, launching with zero downtime for 6 months. I'd love to bring similar efficiency and scalability to your Oncology Podcast Factory. Multiple 5-star reviews on real-time voice AI apps, AI content automation, and podcast production demonstrate my ability to deliver high-quality results. Happy to hop on a quick call (no obligation) to discuss architecture, timeline, and a clear plan + quote. Chris | Lead Developer | Novatech

@victorc309

Hello, I’m very interested in building your oncology “podcast factory” and have hands-on experience designing AI-driven content automation pipelines end-to-end. **Relevant Experience:** I’ve built automated content systems that monitor RSS/APIs, summarize research using LLMs, generate scripts, and produce voice content via TTS/voice cloning. These pipelines auto-publish to media platforms with scheduling, metadata tagging, and performance tracking. **Preferred Stack:** n8n / Make for orchestration … OpenAI or Claude for summarization & scripting … Python microservices for processing … ElevenLabs or similar for clinical-grade TTS … Podcast hosting APIs (Spotify/Apple distribution) … Analytics via dashboards (Sheets/Looker/Metabase). **Phase 1 (MVP):** Source ingestion (PubMed, journals) → AI summarization → script templates → TTS generation → automated publishing → light human QA layer → analytics tracking. **Scale Plan:** Multi-show pipelines, voice personas, content tagging, engagement analytics, and optimization loops. Rate: $6/hr … 30–40 hrs/week … long-term ready. Best regards.

@omarfranciscoa

Hello, thanks for posting this project. I can help you build a high-volume, automated oncology podcast factory that turns daily journal updates into micro-podcast episodes with minimal human touch. I would design an end-to-end pipeline that monitors sources like PubMed, ASCO, and NEJM via RSS or APIs, summarizes key findings with LLMs into concise scripts, and converts them into natural-sounding audio using TTS or voice-cloning tools. Automation with Make, Zapier, or n8n will handle scheduling, publishing across Spotify, Apple, and YouTube, and light human review (10–20%) for quality control. Analytics dashboards can track downloads, engagement, and performance metrics in real time. Phase 1 (MVP) focuses on 1–2 daily episodes: monitoring, LLM summarization, TTS generation, publishing, and reporting. Once stable, we can scale to 5–10 daily episodes with multi-threaded automation, content templates, and additional social/video platform distribution. Deliverables: reproducible automation workflows, documented pipeline, sample episodes, and basic reporting dashboards. I’ve built similar AI-driven content automation systems and prefer Python for scripting, LangChain for content orchestration, and Make/n8n for workflow automation. I can start immediately, ensuring a scalable, compliant, and efficient oncology podcast production system.