Convert product specification documents into multi-format marketing assets

Your product specification document lands in your inbox at 9 AM on a Tuesday. It's dense, technical, forty pages long. The marketing team needs three things by Friday: punchy product copy, a hero image, and a thirty-second demo video. Your design and video teams are booked solid. Someone's going to stay late this week, or something ships incomplete. This is the bottleneck that kills velocity at scaling companies. Specification documents contain everything you need: features, benefits, use cases, technical details. But getting that information into marketing-ready formats requires jumping between tools, context-switching between specialists, and manually reshaping the same information three times over. Each handoff introduces delays and inconsistency. What if you could upload a spec document and have polished marketing assets appear thirty minutes later, with zero manual intervention? That's what this workflow does. You'll build a pipeline that reads your specification document, extracts the key selling points, generates marketing copy in multiple formats, creates a product image that matches your brand voice, and produces a short explainer video. All automated. All consistent. All ready to publish.

What You'll Build - A document upload trigger that accepts PDF or text specification sheets and starts the workflow automatically

• An AI extraction step using Claude Opus 4.6 that analyses the spec and pulls out features, benefits, and use cases in structured JSON
• A copywriting stage using GPT-4o that generates three variants of marketing copy (short form, medium form, long form) tailored to different channels
• An image generation step via Midjourney that creates a product hero image based on the extracted positioning
• A video generation step via Runway that produces a sixty-second demo video showing the product in action
• A final assembly stage that packages all assets into a single output folder with metadata and asset links
• Total time from upload to finished assets: 25-35 minutes; manual equivalent: 4-6 hours across three team members

Prerequisites - OpenAI account with API access (free tier sufficient for testing; paid account recommended for production); you'll need your API key from https://platform.openai.com/account/api-keys

• Anthropic Claude API access (paid; around £0.15 per use for this workflow); API key from https://console.anthropic.com/account/keys
• Midjourney account (paid subscription; £10-30/month depending on usage tier) or API access through a third-party provider like Replicate
• Runway account (free tier limited to 10 credits/month; paid tier from £12/month); you'll need API access or the web interface
• An orchestration platform; this guide uses n8n (self-hosted free or cloud paid), but Zapier or Make work equally well
• Basic familiarity with REST APIs, JSON, and webhook configuration
• A cloud storage account (Google Drive, AWS S3, or similar) to store input specifications and output assets
• Estimated setup time: 60 minutes for the full pipeline, assuming accounts are already created

The Automated Workflow

Step 1:

File Upload Trigger and Parsing Your workflow starts when a specification document lands in a designated folder. Use n8n's cloud file trigger or connect directly to Google Drive via webhook. The trigger watches a Google Drive folder. When a new PDF or text file appears, it captures the file ID and passes it to the next step.

Copy{ "event": "fileCreated", "folderId": "your-google-drive-folder-id", "mimeTypes": ["application/pdf", "text/plain"], "webhookUrl": "https://your-n8n-instance.com/webhook/spec-upload"
}

Once triggered, use Claude Opus 4.6 via the Anthropic API to extract text from the PDF. If your file is already plain text, skip this step. For PDFs, you can use a simple extraction library (Python's PyPDF2 or a cloud service like AWS Textract), or pass the base64-encoded PDF directly to Claude's vision capabilities.

Copycurl -X POST https://api.anthropic.com/v1/messages \ -H "x-api-key: $ANTHROPIC_API_KEY" \ -H "content-type: application/json" \ -d '{ "model": "claude-opus-4.6", "max_tokens": 4000, "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Extract the product name, category, top 5 features, primary use cases, and target audience from this specification document. Return as JSON." }, { "type": "document", "source": { "type": "base64", "media_type": "application/pdf", "data": "base64-encoded-pdf-content" } } ] } ] }'

The output from Claude is structured JSON with extracted metadata:

Copyjson
{ "product_name": "CloudSync Pro", "category": "Data Management", "features": [ "Real-time synchronisation across 50+ cloud platforms", "Military-grade encryption", "Automated conflict resolution", "Granular access controls", "Audit logs for compliance" ], "use_cases": [ "Enterprise data centralisation", "Regulatory compliance automation", "Hybrid cloud management" ], "target_audience": "Enterprise IT teams, compliance officers, data architects", "key_benefit": "Eliminates data silos and manual sync workflows"
}

Error handling: If the PDF is corrupted or unreadable, Claude will return an error. Catch this and send a Slack alert to your team with the original file name; they can then manually review it or re-upload a corrected version. Set a retry limit of 1 to avoid wasting API calls on permanently broken files.

Step 2:

Generate Marketing Copy Feed the structured data from Step 1 into GPT-4o to generate three versions of marketing copy: a tweet-length summary (280 characters max), a LinkedIn post (1200 characters), and a landing page headline plus subheading.

Copycurl -X POST https://api.openai.com/v1/chat/completions \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4o", "messages": [ { "role": "system", "content": "You are an expert B2B product copywriter. Write compelling, benefit-focused copy that speaks to technical decision-makers. Avoid jargon. Be specific." }, { "role": "user", "content": "Create three versions of marketing copy for this product:\n\nProduct: CloudSync Pro\nKey Features: Real-time synchronisation, Military-grade encryption, Automated conflict resolution\nKey Benefit: Eliminates data silos and manual sync workflows\nTarget Audience: Enterprise IT teams\n\n1. Tweet (max 280 chars): \n2. LinkedIn post (1200 chars max):\n3. Landing page headline and subheading:\n\nReturn as JSON with keys: tweet, linkedin_post, landing_page_headline, landing_page_subheading" } ], "temperature": 0.8, "max_tokens": 1500 }'

Expected output:

Copyjson
{ "tweet": "Stop syncing data manually. CloudSync Pro connects 50+ cloud platforms in real-time with military-grade encryption. One dashboard, zero silos.", "linkedin_post": "Enterprise teams waste 15+ hours per week on manual data synchronisation. CloudSync Pro automates it all: real-time sync across 50+ platforms, military-grade security, intelligent conflict resolution. IT teams cut sync overhead by 80%. Worth a look.", "landing_page_headline": "Unify Your Cloud Data in Real-Time", "landing_page_subheading": "Connect 50+ platforms, eliminate manual syncing, and secure everything with enterprise-grade encryption."
}

Store these outputs in a JSON file that you'll reference in the next steps. Error handling: If the LLM response is malformed JSON, validate and retry with a simpler prompt. Set max retries to 2.

Step 3:

Generate Product Image via Midjourney Use the extracted features and benefit statement to create a detailed Midjourney prompt, then submit it to the Midjourney API (either via Discord webhook or through a service like Replicate that wraps the Midjourney API). First, construct a visual brief from your extracted data:

Copycurl -X POST https://api.openai.com/v1/chat/completions \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4o", "messages": [ { "role": "system", "content": "You are a creative director. Write a detailed, visual Midjourney prompt that captures the essence of a product in one image. Include style, mood, and specific visual elements. Keep it under 150 words." }, { "role": "user", "content": "Create a Midjourney prompt for a product image for CloudSync Pro, a real-time data synchronisation platform for enterprises. Key visuals: multiple interconnected cloud systems, smooth data flow, security/encryption theme, modern tech aesthetic. Return only the prompt text, no other commentary." } ], "max_tokens": 200 }'

Then submit to Midjourney via Replicate (which handles the API wrapping):

Copycurl -X POST https://api.replicate.com/v1/predictions \ -H "Authorization: Token $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "version": "e5582d29d72f4efc2d46d765eb721900263e8ff1073bce2b5e16e78ffcde153c", "input": { "prompt": "Modern enterprise data sync platform interface. Multiple cloud platforms connected with glowing data streams, flowing securely between systems. Cyberpunk aesthetic, blues and purples. Sleek, professional, high-tech. 4K, cinematic lighting." } }'

Replicate returns a prediction ID; poll it until the image is ready:

Copycurl -X GET https://api.replicate.com/v1/predictions/{prediction_id} \ -H "Authorization: Token $REPLICATE_API_TOKEN"

Once complete, you'll get a URL to the generated image. Download it and store it in your output folder. Error handling: Image generation can fail if the prompt is too vague or requests unsafe content. If you get a failure, GPT-4o should catch this and regenerate the prompt automatically. Retry up to 2 times before escalating to manual review.

Step 4:

Generate Demo Video via Runway For the video, use Runway's Gen-3 Alpha model to create a short product demo. First, construct a detailed scene description from your extracted features:

Copycurl -X POST https://api.openai.com/v1/chat/completions \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4o", "messages": [ { "role": "system", "content": "You are a video director. Write a detailed, shot-by-shot description for a 60-second product demo video. Include specific visuals, transitions, and pacing. Be cinematic but clear." }, { "role": "user", "content": "Write a scene description for a 60-second demo video for CloudSync Pro. The video should show: (1) a dashboard with multiple cloud platforms, (2) real-time data flowing between them, (3) security indicators lighting up, (4) a team member reviewing the sync results with satisfaction. Make it professional and engaging." } ], "max_tokens": 300 }'

Then submit to Runway's API:

Copycurl -X POST https://api.runwayml.com/v1/video_generations \ -H "Authorization: Bearer $RUNWAY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gen3a_turbo", "prompt": "Modern SaaS product demo. Dashboard shows CloudSync Pro interface with five cloud platform logos (AWS, Google Cloud, Azure, Dropbox, Salesforce). Real-time data flows as animated arrows between platforms, colour-coded by sync status. Security padlock icons light up green as data syncs. Final shot: professional woman at desk smiles, gives thumbs up. Modern office setting, professional lighting, 60 seconds, 1080p.", "duration": 60, "resolution": "1080p" }'

Runway returns a generation ID. Poll for completion:

Copycurl -X GET https://api.runwayml.com/v1/video_generations/{generation_id} \ -H "Authorization: Bearer $RUNWAY_API_KEY"

Once the video is ready (usually 2-5 minutes), store the URL. Error handling: Video generation is slower and more resource-intensive than images. If it fails, it's often due to GPU availability on Runway's end. Implement exponential backoff: wait 30 seconds, then retry. If it fails twice, send a Slack notification and schedule a manual retry for 1 hour later.

Step 5:

Assemble and Deliver Assets In the final step, gather all outputs (copy, image, video) and package them into a structured folder in Google Drive or AWS S3. Create a JSON metadata file that ties everything together:

Copyjson
{ "workflow_id": "spec-to-assets-2026-03-15-001", "input_document": "CloudSync_Pro_Spec_v2.pdf", "product_name": "CloudSync Pro", "generated_at": "2026-03-15T14:32:00Z", "assets": { "copy": { "tweet": "Stop syncing data manually. CloudSync Pro connects 50+ cloud platforms in real-time with military-grade encryption. One dashboard, zero silos.", "linkedin_post": "Enterprise teams waste 15+ hours per week on manual data synchronisation...", "landing_page_headline": "Unify Your Cloud Data in Real-Time" }, "image": { "url": "https://storage.googleapis.com/your-bucket/cloudsync-hero-20260315.png", "format": "PNG", "dimensions": "2048x1024" }, "video": { "url": "https://storage.googleapis.com/your-bucket/cloudsync-demo-20260315.mp4", "duration_seconds": 60, "resolution": "1080p" } }
}

Upload this metadata file and all assets to your output folder, then send a notification (email or Slack) to the marketing team with a direct link to the folder. Error handling: If any asset failed to generate, mark it as null in the metadata and include a note for manual follow-up. Never send an incomplete asset without flagging it.

The Manual Alternative

If you want to test this workflow in pieces before fully automating it, start with just the copy generation. Upload a spec document to ChatGPT, paste the key details, and ask it to generate the three copy variants. This takes about 10-15 minutes and requires no setup. Next, take the extracted features and use the Midjourney web interface (or another image generator like DALL-E 3 or Stable Diffusion 3.5) to create a product image manually. Write your own detailed prompt based on the extracted benefits, iterate on the image 2-3 times, and download the final version. Budget 20-30 minutes for this. For video, the manual path is more time-intensive. You'd either script a demo video and film it yourself, or hire a freelancer to produce it. The manual route typically costs £200-500 per video and takes 5-10 business days. Runway's automated approach compressed that to a single API call. Once you've done the manual version once or twice, you'll understand the data flow well enough to connect all the pieces into a full automation.

Pro Tips 1.

Rate limiting and batching: Claude and OpenAI both rate-limit API calls. The workflow above makes three API calls to OpenAI (text extraction via Vision, copy generation, prompt generation) and one to Claude. Stagger these calls by 2 seconds each to stay well below rate limits. If you scale to processing 10 spec documents per day, batch the text extraction step; send multiple documents to Claude in a single message (using the documents array) to reduce round-trips by 70%.

2. Cost optimisation: Use GPT-4o mini instead of GPT-4o for the copy generation step if you're processing high volume. It's 50% cheaper and produces nearly identical copy quality. Reserve GPT-4o for the Midjourney prompt generation, where nuance matters more. For image generation, always start with a base64-encoded screenshot or reference image in your Midjourney prompt; this dramatically improves consistency and reduces iteration loops.

3. Error monitoring and alerts: Set up a Slack webhook in n8n that fires whenever any API call returns a non-2xx status code. Include the step name, error message, and timestamp. Example webhook payload:

Copy{ "channel": "#automation-errors", "username": "Spec-to-Assets Workflow", "text": "Error in Step 3 (Midjourney image generation)", "attachments": [ { "colour": "danger", "fields": [ { "title": "Error", "value": "Image generation timed out after 300 seconds", "short": false }, { "title": "Input Spec", "value": "CloudSync_Pro_Spec_v2.pdf", "short": true } ] } ]
}

4. Scaling and bottlenecks: The slowest step is video generation on Runway; it typically takes 3-5 minutes. If you process 10 specs per day, videos will queue up. Mitigate this by using Runway's concurrency settings (if your plan supports it) or by spreading submissions across the day rather than processing all specs at once.

5. Security for API keys: Never hardcode API keys in your n8n workflow. Use n8n's credential system to store them securely, or inject them as environment variables at runtime. If you're self-hosting n8n, encrypt the credentials database.

6. Iteration and feedback loops: After the first week of running this workflow, ask the marketing team which assets needed the most manual refinement. If it's copy, adjust the GPT-4o system prompt to match your brand voice more closely. If it's images, provide Midjourney with a reference image URL or a style guide. If it's video, you might find that Runway generates visuals that don't quite match your brand aesthetic; in that case, consider using a shorter, more specific prompt or switching to a scripted video template for consistency.

Cost Breakdown