Platform for Creating and Publishing SEO-Optimized Content

To streamline internal content production and boost online visibility, we developed an in-house platform that automates the entire process of content generation, optimization, and publishing.

The system starts by crawling selected source websites using a function on AWS Lambda. The collected raw content is processed by a number of GPT-based agents integrated into Make. These agents rewrite the material, paying special attention to SEO improvements, tone alignment, and platform-specific adaptation.

Each article is verified using an AI detection tool to ensure it meets originality standards, and we perform additional checks to guarantee SEO compliance. After optimization, the content is automatically published to our website and LinkedIn, with proper formatting, visuals, and keyword-rich hashtags.

This end-to-end automation significantly reduced manual effort and ensured consistent, high-quality publishing across platforms.

Challenges

Content Source Variability: Websites used as content sources often had inconsistent structures and no APIs, requiring a flexible scraping approach that could handle dynamic layouts and changing HTML.
Maintaining Content Quality: Raw scraped text lacked structure, clarity, and SEO relevance. We needed to ensure that GPT-based rewriting maintained the original meaning while enhancing readability and keyword density.
Context-Aware Rewriting: Standard rewriting wasn’t enough — content had to be adapted for both a blog format and LinkedIn posts, requiring different tones, lengths.
Automation Stability: Ensuring the system could operate independently without interruption required implementing robust error handling, status monitoring, and recovery logic to handle potential failures gracefully.
Content Uniqueness: To maintain SEO integrity and user interest, we designed mechanisms to detect and eliminate repetitive or overly similar content before publication, ensuring each post delivered distinct value.

AWS Lambda

Allowed us to run the scraping in a serverless environment, automating processes without the need for server management.

Puppeteer

A Node.js library that enables automated browser interaction for data collection and validation through Google Search.

Make

Automated the entire workflow, integrating different services and processes, allowing seamless coordination between scraping, rewriting, and publishing.

GPT Assistants

Rewrote the scraped content, optimizing it for SEO and tailoring it to specific platform needs, ensuring high-quality, ready-to-publish articles.

Solutions & Technologies

We leveraged a combination of cutting-edge technologies to build a highly efficient solution for content scraping, rewriting, and publishing. Node.js provided a scalable and flexible environment for handling asynchronous tasks and managing multiple data pipelines. AWS Lambda enabled serverless execution, ensuring cost-effective and scalable processing of scraping tasks. GPT-based assistants were integrated to rewrite the scraped content, optimizing it for SEO and tailoring it for different platforms. The Make platform was used to automate workflows, seamlessly connecting each part of the system for a smooth and hands-off operation.

Make: The Automation Backbone

Make plays a central role in orchestrating the entire pipeline — from scraping to publishing. It acts as the glue connecting all services and processes into a seamless, automated workflow.
Workflow Automation: Manages the full content lifecycle — triggering scrapers, sending raw content to GPT agents for rewriting, optimizing the result, and publishing it across selected platforms. All steps are fully automated, reducing the need for manual oversight.
API Integration: Easily connects services like AWS Lambda, OpenAI GPT, CMS platforms, and the LinkedIn API using built-in modules or custom HTTP requests. Make supports both REST and SOAP APIs, enabling integration with virtually any web service.
Flexible Logic: Allows creation of advanced workflows with conditional branching, iterators, routers, and error handling blocks. These let you build logic that adapts to different inputs, handles edge cases, and makes decisions in real time — all without writing code.
Data Transformation: Offers built-in tools for parsing JSON, modifying text, formatting dates, mapping arrays, and more. This makes it easy to prepare and manipulate data between steps without external scripts or additional services.
Scalability & Monitoring: Supports parallel executions and scheduled triggers, and includes execution logs, error tracking, and automatic retries. This ensures stability and transparency of all automated processes, even under increased load.
Built-in Apps & Templates: Provides hundreds of ready-to-use integrations with popular services like Google Sheets, Notion, Dropbox, Airtable, Slack, Webhooks, and more — helping to speed up development and expand the system with minimal effort.
We chose Make for its flexibility, low setup time, and rich integration ecosystem — making it easy to iterate, scale, and maintain a complex automation system with a low technical barrier.

Visuals: Branded, Consistent, and Automated

To ensure every piece of content looks polished and on-brand, we integrated an automated image assembly step into the workflow. Our design team provided a set of flexible templates — including logos, backgrounds, and layout options — tailored for both blog and LinkedIn formats.

The system combines these visual elements with the article headline to generate a final image, ready for publishing. This ensures consistency in style and branding across platforms, without needing manual work from designers for each post.

While the setup is optimized for specific content types and formats, it delivers stable, high-quality visuals that enhance post engagement and make our content instantly recognizable.

Experimenting with LLMs: DeepSeek vs GPT

During our experiments with replacing GPT-based rewrite agents with DeepSeek, we were able to significantly reduce content generation costs by more than 10x. This allowed us to save budget without compromising on core outcomes.

While DeepSeek handles basic structure and SEO well, it’s worth noting that the texts might lack the same level of nuance, tone, and polish that GPT provides. Headlines may be less compelling, and transitions can feel more mechanical. However, for large volumes of content where quantity matters more than high-end quality, DeepSeek proves to be a great option.

We decided to keep GPT as the primary tool for high-quality content, while using DeepSeek for internal drafts or test cases where speed and cost-efficiency are more important. It was a valuable experiment that helped us better understand the balance between cost and content quality.

Results

Automating the entire content process changed the way we work with articles. What used to take hours of manual effort — from gathering information and rewriting to formatting and publishing — now runs smoothly in the background, without constant supervision.
Time Savings: We cut content production time by over 90%, allowing the team to shift focus to strategy and creative tasks.
Consistency: New SEO-optimized content is published several times a week without delays or bottlenecks.
Engagement Boost: Posts tailored for LinkedIn and blog audiences led to a noticeable uptick in engagement and impressions within the first month.
Scalability: The system easily scaled from a single source to multiple inputs without changing the core logic — just plug and play.
Reliability: Even if something breaks (like a changed site layout), built-in monitoring helps the system self-recover or notify us instantly.
SEO Impact: With consistent publishing, keyword-rich content, and technical optimization baked into the process, we observed a steady improvement in organic visibility and search engine rankings.
With the heavy lifting now automated, our team can shift focus from routine tasks to strategy and growth. Content is published consistently, performs well in search, and our online presence continues to strengthen