AI for CMS
Get your CMS ready for AI
A modern CMS is not just a platform for publishing content. It is a source of knowledge for customers, AI search engines and intelligent agents.
We help organisations prepare their CMS platforms for the use of artificial intelligence – from readiness audits, through support for editors, to the implementation of AI agents and semantic search. We integrate AI into existing processes, rather than building yet another standalone tool.
Why is a traditional CMS no longer enough?
For years, CMS systems were designed primarily with the publication of content on websites in mind. Today, content is used in a much wider range of applications: by AI search engines, virtual assistants, chatbots, mobile apps and organisations’ internal systems.
This means that the CMS on its own is not enough. You need properly prepared data, a consistent content structure and processes that enable the use of AI in day-to-day work.
- Users search differently now. Google, AI Search and tools such as ChatGPT and Perplexity are increasingly providing answers rather than links. If your content isn’t optimised for semantic search and AI Search, it simply doesn’t exist in this ecosystem.
- Content teams have too much work on their hands. Editors spend hours on research, briefs are drawn up manually, translations are outsourced to external providers, and the quality and consistency of the content still depend on the experience of a single person.
- Data from the CMS is unstructured. Years’ worth of articles, pages, documents and descriptions form a dataset that nobody uses, because there is no tool capable of ‘reading’ it and drawing conclusions from it.
- AI models require high-quality input. Implementing a chatbot, assistant or AI agent without properly prepared content, taxonomy and data structure will result in a tool that generates errors and produces inaccurate results. AI is only as good as the data it is fed.
AI does not replace a CMS. It expands its capabilities.
Therefore, rather than implementing yet another standalone tool, it is worth harnessing the potential of a system that already serves as a central hub for content management. Companies that organise their content, data models and information architecture will be better prepared for the development of AI Search, AI agents and new ways of discovering information.
How is AI changing the way content teams work?
Artificial intelligence can support the entire content lifecycle.
From preparing materials, through editing and translation, to researching information and providing user support.
Well-designed AI solutions enable, amongst other things:
- speed up the creation and updating of content,
- to support editors in their day-to-day work,
- to make use of the knowledge stored in the CMS,
- provide users with answers rather than lists of search results,
- prepare the organisation for the development of AI Search and AI agents.
We don’t sell ready-made chatbots. We build AI that works with your CMS
Our approach to AI in content environments is based on many years’ experience with CMS platforms: Magnolia, Contentful and Sulu. We know what the data looks like in these systems, what their limitations are, and how to prepare it properly so that AI can use it effectively.
Working with us is an iterative process. We start with an audit and assessment, then implement specific solutions — one, two or several at a time, depending on your priorities and resources. There is no one-size-fits-all approach. Instead, we have a tried-and-tested process that we tailor to your platform, team and business objectives.
The three principles of our work:
1. First, the foundations. Before we implement AI, we make sure that your content, metadata, taxonomy and data structure are ready for it. This isn’t a waste of time. It’s essential for the AI to work properly.
2. AI as an extension (rather than a replacement) of the CMS. We do not build separate AI systems alongside your existing infrastructure. We integrate AI directly with what you already have: your CMS, content repositories, search systems and editorial workflow.
3. A solution that the team will actually adopt. Even the best technology is worthless if editors don’t use it. We design solutions with adoption in mind: they are easy to use and integrated into existing processes, rather than simply tacked on as an afterthought.
Are you looking for a team that will advise you, rather than just write the code?
We’re not just here to tackle tasks from the backlog. We help make decisions that make sense – both for the product and for the business.
Let’s discuss how we can help: from strategy to roll-out.
Our services
Choose one area or take a comprehensive approach – we’ll start where it makes the most sense.
Semantic Search & GEO
Search based on meaning, not just keywords.
More and more users expect answers rather than a list of results.
We design solutions that use semantic search, enabling users to find the right information even when they do not know the exact search terms.
At the same time, we help prepare content for the evolving ecosystem of AI Search and Generative Engine Optimisation (GEO), ensuring that content is utilised more effectively by modern search engines and AI agents.
Semantic Search: search within your platform
We are replacing or supplementing the traditional full-text search in the CMS with a semantic search based on embeddings. The result: a user searches for ‘how to configure payment integration’ and gets the correct result, even if the document does not contain those exact words.
The implementation includes:
- the development or configuration of a vector database for content from the CMS,
- content indexing with embedding generation,
- integration with the platform’s search interface,
- testing and calibration of the accuracy of the results.
Thanks to semantic search, users can find the right information more quickly, even if they do not know the exact keywords. The organisation makes better use of the knowledge stored in the CMS, and the search engine becomes a real source of support for customers and staff.
GEO Readiness: visibility in AI Search
GEO (Generative Engine Optimisation) is a set of practices that increase the likelihood of your content being cited or used by AI search systems when responding to user queries.
We do:
- content audit focusing on structure, authority and relevance for AI Search,
- optimisation of metadata, headers and semantic structure,
- implementation of structured data (schema.org) and extensions for LLMs,
- recommendations regarding the format and length of content in the context of AI Search,
- the configuration of
llms.txtand other mechanisms that allow AI models to access your content.
Who is it for: companies whose content should appear in search results and AI tools. In particular, e-commerce businesses, publishers, SaaS companies and websites with extensive documentation.
What does AI implementation in a CMS look like?
Every organisation has a different content architecture, different editorial processes and different business objectives. That is why we start by understanding your CMS environment, and only then do we design solutions that meet your specific needs.
We carry out our projects in an iterative manner: from the initial assessment and first implementations through to the development of further scenarios for the use of AI.
1. We’re getting to know your content ecosystem
Before we propose any solution, we want to understand how the organisation operates. We analyse the CMS platform used, the content architecture, editorial processes and the flow of information between systems.
We talk to business stakeholders and the teams responsible for content to identify the biggest challenges and the areas where AI can deliver real benefits.
2. We are assessing the CMS’s readiness to utilise AI
As part of the CMS AI Readiness Scan, we assess whether the current environment is ready to work with AI solutions. We analyse the content structure, data models, metadata, the quality of information, and the potential for integration with language models and semantic search.
We also assess editorial processes and content management practices, as the effective use of AI depends not only on the technology, but also on the quality of the data and the organisation of work.
3. We are designing the first solution
Based on the results of the analysis, we work together to select the scenario that will deliver the greatest business value. For some organisations, this will be an AI Content Assistant to support editors; for others, it will be an AI Knowledge Chat or the implementation of semantic search.
We design the solution architecture, define how it will integrate with the CMS, and specify which data sources will be used by the AI models.
We are keen to ensure that the initial roll-out delivers tangible results quickly and forms the basis for further development.
4. We are implementing AI within our existing CMS environment
We integrate the solution with the CMS platform and the organisation’s other systems. We configure AI models, develop semantic search mechanisms, integrations and the necessary security processes.
At the same time, we ensure that new features become a natural part of users’ day-to-day work — without the need to use additional, separate tools.
Following implementation, we support the team during testing and the first few weeks of using the solution.
5. We are developing the platform in collaboration with the organisation
The initial implementation is usually the starting point for further development. In subsequent stages, we help to extend the use of AI to new scenarios, automate content processes and prepare the platform for the development of AI Search and future generations of AI agents.
We monitor how the solution is being used, gather feedback from users and work together to plan the next stages of the platform’s development.
Thanks to an iterative approach, an organisation can gradually build up its capabilities and develop AI in areas where it delivers the greatest value.
FAQ – or questions worth asking before you start
Where should we start if we’ve never implemented AI in a CMS before
Start with the CMS AI Readiness Scan. It’s a short, structured audit that will tell you the current state of your platform and content, and what you should do first. Many of the companies we speak to discover at this stage that they first need to get their data and metadata in order, and only then does it make sense to integrate AI.
Will AI replace our editorial team
No. And that’s not the point. AI takes over repetitive, mechanical tasks: generating drafts, tagging, preliminary translations and research. Editors gain time for work that requires judgement, expert knowledge and an understanding of context. In practice, companies that have implemented AI in their editorial processes do not reduce their teams – they increase content output using the same resources
Will our content end up in external AI models? What about data security
It depends on the architecture we design together. We can work with cloud-based models (OpenAI, Anthropic, Google), where data is processed on external servers in accordance with the provider’s privacy policy. Alternatively, we can implement open-source models hosted on your infrastructure – in which case no data leaves your environment. For most clients, a hybrid solution is the best option. We always discuss these issues during the consultation phase.
Will AI use only CMS content
Not always. We can also integrate solutions with documentation, knowledge bases, product systems, the intranet or other data sources. A CMS is often the central component of a content ecosystem, but it does not have to be the sole source of knowledge for AI agents.
Which CMS platforms do you work with
We specialise in Magnolia CMS, Contentful, Sulu and WordPress. We also work with other headless and traditional platforms: Storyblok, Sanity and Strapi. If you use a different system, please get in touch – many AI integrations are possible via standard APIs, regardless of the platform.
How much does it cost to implement AI in a CMS?
We always determine the scope and budget on a case-by-case basis following the preliminary audit. The CMS AI Readiness Scan is a service with a fixed, transparent price — it’s a good starting point for assessing the scope and cost of the next steps. The implementation of specific solutions (AI Content Assistant, AI Knowledge Chat) involves projects lasting several weeks, priced according to the complexity of the integration and the scope of functionality.
How long does a typical implementation take?
CMS AI Readiness Scan: 2–4 weeks. First operational module (e.g. AI Content Assistant or AI Knowledge Chat): 6–12 weeks in a typical scenario. The timeframe depends on the complexity of the CMS, the condition of the input data and the scope of integration. We work iteratively, so you can have your first working tool sooner than after full implementation.
Which organisations is the AI for CMS service intended for
This service has been designed for companies that use a CMS as their central content management hub and wish to prepare their platforms for AI development. We support organisations developing complex websites, product platforms, knowledge portals and multilingual solutions. We most frequently work with marketing, digital, e-commerce, communications and IT teams responsible for the development of digital platforms.
What is GEO and why is it important
GEO stands for Generative Engine Optimisation – practices that increase the likelihood of your content being cited or used by AI search systems (Google AI Overview, ChatGPT Search, Perplexity). It is analogous to SEO, but for a new generation of search engines. If you’re involved in content marketing or keen to drive organic traffic, GEO will become an increasingly important part of your strategy over the next 12–24 months.
Get your CMS ready for the next generation of search and content management.
Book a consultation with SYZYGY’s experts and find out where to start
AI does not require building everything from scratch. In many organisations, the greatest potential already lies in existing content and CMS platforms. We will help you assess the readiness of your environment, identify the best scenarios for using AI, and implement solutions that will deliver real business value.
