Content & Publishing Platform

Consistent content production across websites

Running a consistent publishing operation across many websites involves far more than generating text. Topic planning, research, source evaluation, drafting, SEO, internal linking, image creation, quality assurance and publishing all need to work as connected steps. As the number of websites grows, managing this process manually becomes operationally difficult. Asking a single AI model to handle research, writing, quality control, SEO context and existing content relationships in one step also compresses too many responsibilities into a single output.

The platform was developed to turn content operations into a centralized production and publishing workflow where specialized responsibilities work together as part of one coordinated system.

Using specialized responsibilities instead of one model call

The platform runs content production through a pipeline involving 8 specialist agents. A central orchestrator tracks the current stage of each content job and routes the next task to the appropriate agent. The process begins by creating the core content structure and production plan. A research agent then searches the web, reads real articles and sources, and gathers information that can be used in the content. The research output is passed into the drafting stage. Once the first draft is created, a separate editorial task strengthens the text and turns it into a more professional, publication-ready version.

This approach treats research, writing and editorial improvement as separate responsibilities instead of trying to solve them all inside a single prompt.

Making SEO part of the production pipeline

SEO is integrated directly into the content workflow rather than applied only as a final checklist. Agents generate titles, descriptions and other SEO metadata while also searching the existing content repository stored in Qdrant. When relevant content exists within the same tenant, the system can identify internal-linking opportunities. If allowed by the tenant, related links to other projects owned by the same organization can also be incorporated. This allows each new article to be evaluated not only as a standalone page but also in the context of the wider publishing network.

Bringing E-E-A-T considerations into the content structure

The platform uses an editorial approach designed to support experience, expertise, authority and trust signals where relevant. Author profiles, source references and related publishing information can be included according to tenant configuration. This is not presented as a search-ranking guarantee; it is treated as an editorial framework that supports content quality and publishing transparency.

Blocking automatic publication when quality checks fail

Generated content is not sent directly to publication. During the QA stage, content is evaluated against defined quality checks. If it fails, the system performs up to two automated retries. If the required quality level is still not reached after those retries, the content is routed to human review instead of being published automatically. This creates a controlled boundary between automation and human oversight.

Generating visuals specifically for the content

Once the article is ready, a dedicated task prepares an image prompt based on the subject and context of the content. The prompt is then sent to Stability AI Ultra for image generation. Tenant administrators can adjust the visual style used for generated assets from their own management panel. This allows the same production platform to support different visual languages across different publishing brands.

Completing production and publishing in the same system

After content and visuals are ready, publishing is managed by the platform's own scheduling algorithm. Content is delivered to the relevant websites through a Custom API and published according to the configured release plan. OpenAI Batch is also used for high-volume AI workloads so that production tasks can be handled through batch processing when appropriate. This brings research, writing, optimization, image generation and publishing together inside one centralized content operation.

Agent and workflow structure

Area	Responsibility
Pipeline planning	Creating the core content structure and production schema
Research	Searching the web and reviewing real articles and sources
Draft generation	Creating the first content draft from the research output
Editorial refinement	Strengthening the text and turning it into publication-ready copy
SEO and linking	Finding related content through Qdrant and identifying internal/external linking opportunities
SEO metadata	Preparing titles, descriptions and related metadata
QA	Quality control, two automated retries and human-review fallback
Image generation	Preparing the image prompt and triggering the AI image-generation service
Orchestration	Managing all agent tasks and pipeline state centrally
Publishing	Automated publishing through the Custom API and platform scheduler

Real-world operating scale

The platform is actively used in production. More than 300 active blog websites run their content production through the same centralized platform. To date, the system has generated and published more than 58,000 pieces of content. The platform supports Turkish and English content generation and allows tenants to manage their own publishing, linking and visual-style rules.

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An orchestrated production chain instead of a single AI call

The platform's core architectural principle is to avoid leaving complex content production to one model request. Research, drafting, editorial refinement, SEO, quality assurance and image preparation are separated into distinct responsibilities. The orchestrator manages pipeline state and turns the output of each stage into the input for the next task. This allows quality issues and process failures to be handled at the relevant stage rather than discovered only in the final output.

Using existing content memory in new production

The Qdrant-based retrieval layer connects previously created content with new production tasks. When agents work on a new topic, they can identify related or complementary content within the same tenant and use it to support internal-linking decisions. Depending on tenant preferences, other websites belonging to the same organization can also be included in the linking strategy.

Human review at the boundary of automation

The system automates as much of the production flow as possible, but it does not rely on unlimited retries when quality checks fail. After two automated retry attempts, content that still does not meet the required criteria is routed to human review. This preserves production speed while preventing failed outputs from moving automatically into publication.