AI & SEO automation
Custom SEO tools
I design the architecture of an SEO tool: the choice of form (application, web or report), integration with the data and the workflow, and decisions on data storage, logs and access control. I plan the data pipeline and the mapping of URLs and canonicals. I then test the metrics and validate the AI output against SEO rules and acceptance criteria.
About the service
SEO tool architecture design and validation of AI output in the day-to-day development process
About the custom SEO tools service
A few service details- Choice of deployment form
- Plan for integration with the workflow
- Design of data, logs and access
- Data pipeline and URL mapping
- Metric and data consistency tests
- AI validation and acceptance thresholds
Work process
The process of designing and validating SEO tools in practice
I start by establishing how the tool is meant to work and how it will fit into the day-to-day workflow. I then design the data integration, the architecture and the backlog priorities for the MVP. Finally, I run quality tests and validate the results so that a decision on implementation can be made.
- 01/ 03
Deciding how it runs
I define whether the tool should work as an application, a web solution or a report, and what the input and output flows will look like.
- 02/ 03
Data integration design
I plan data storage, event logging, access control and the data pipeline, with validation, cleaning, normalisation and the mapping of URLs and canonicals.
- 03/ 03
Testing and moving on
I test the metrics and the data, check the repeatability of the AI output and validate it with SEO rules against the acceptance criteria before moving to the production version.
Kick-off and needs mapping for SEO tools
Kick-off and needs mapping means establishing which SEO problems are to be automated and how the target tool should work. At this stage I clarify whether the priority is auditing, monitoring, work on content or another repeatable process, and who will receive the results (e.g. the person responsible for SEO, content or implementation). We also agree on the way of working together and on the acceptance criteria, which will later be the basis for quality testing and for the decision to move to the production version. As a result, it is clear from the start which inputs, processing and outputs are needed and where manual approval will be necessary.
During the kick-off I gather information about the current way of working and the places where repeatable activities and quality control points occur. This prepares the ground for the later analysis of the SEO process to be automated and for choosing a minimal, useful MVP scope.
The outcome is a shared understanding of what the tool is meant to deliver, in what form, and which decisions or actions it should lead to.
- Establishing the problems to automate (e.g. auditing, monitoring, content).
- Defining who the tool is for and how they will work with the results.
- Defining the acceptance criteria that will later be the basis for validation.
Data and access inventory as the foundation for automation
The data and access inventory means gathering a complete set of information about data sources, formats, update frequency and the authentication method. On that basis I identify the gaps and limitations that may affect what can be automated and how reliable the results will be. This stage is direct preparation for mapping the SEO process and building the data pipeline in which data will be fetched, validated, cleaned and normalised. The more precisely the input data is defined, the easier it is to plan later integrations and quality control.
This step is also the moment to confirm whether access to the site and its environments allows data to be read and tests to be run. A lack of the required access limits automation and the reliability of the conclusions, so the limitations are recorded and taken into account in further planning of the MVP scope. In practice, this ends with a list of data sources along with a description of the limitations and decisions on what can be processed and what requires simplification or further agreement. It is also the starting point for establishing which data the tool will rely on, which affects accuracy and refresh frequency.
- A list of data sources, formats and update frequency.
- Defining the authentication method and the access required for reading and testing.
- Identifying the gaps and limitations that affect the further scope of work.
Analysis of the SEO process to be automated with AI
Analysing the SEO process to be automated with AI means mapping out the current way of working step by step and indicating which elements are manual and repeatable. On that basis I define where quality control points are needed and which input data must be available for the results to be useful. During the analysis I identify the places where AI can support decisions, e.g. through classification, grouping or preparing proposals for review. The outcome is a description of the workflow that can be translated into functional requirements and, later, a backlog.
In practice, this stage sets out which activities should end with a result in the form of a report, a task list or an export for further implementation in the process. At the same time I specify which quality checks are needed to limit errors and keep the results repeatable. As a result, the later choice of MVP is not a collection of loose features but a coherent “input → processing → output”. This analysis also makes it easier to establish where manual approvals will be required before recommendations are passed on.
- Mapping out the current process: manual, repeatable and data-dependent activities.
- Indicating the quality control points and the places where AI should support decisions.
- A description of the target workflow in the format: inputs, processing, output and next action.
Choosing the MVP scope for custom SEO tools
Choosing the MVP scope for custom SEO tools means establishing the minimal set of features that delivers a useful result in a specific process. I define the scope so that it can be described unambiguously: what the inputs are, what the processing looks like, what output is produced and what the expected action or export is. This stage translates the findings from the process analysis into a specific part of the tool that can be implemented. The MVP scope is also the starting point for further detail in the functional specification.
The MVP can include selected modules, depending on which process is to be improved and which data is available. The scope may cover, for example, a technical audit and indexing, change monitoring and alerts, keyword and intent analysis, content support (briefs and corrections), internal linking, and reporting and prioritisation. In parallel I establish the level of automation: whether the tool should only report or also prepare ready-made tasks/changes, and where manual approval points are required. The final scope is also shaped by the choice of data sources and their quality, because this determines accuracy, refresh frequency and how detectable problems are.
- Establishing the minimal flow: inputs → processing → output → action/export.
- Selecting MVP modules in line with the process to be automated.
- A decision on the level of automation and the points of manual approval of the results.
Designing the architecture and integration of SEO tools
Designing the architecture and integration of SEO tools means choosing how the solution will run and planning its integration with the data and the workflow. At this stage I define whether the tool should work as an application, a web solution or a report, and what the input and output flows will look like in day-to-day use. The design also covers decisions on data storage, event logging and access control. These decisions are matched to the backlog priorities and to the features that are to go into the MVP.
Within the architecture I take into account security and compliance requirements, including the rules for data processing, anonymisation, retention and permissions, as well as restrictions on sending content outside the environment. In parallel, the data pipeline is planned: fetching, validation, cleaning, normalisation and versioning, with the mapping of URLs, canonicals, statuses and parameters. Decisions on data sources and their quality affect how often the data can be refreshed and how reliably problems can be detected. The final form of the results also depends on the level of automation: whether the tool should only report or prepare elements ready to be passed on in the process.
- Choosing the form in which the tool runs (application/web/report) and how it integrates with the workflow.
- Designing data storage, event logging and access control.
- Taking into account security and compliance rules and restrictions on content processing.
- A data pipeline plan with the mapping of URLs, canonicals, statuses and parameters.
Quality testing and validation of AI output in the SEO process
Quality testing and validation of AI output in the SEO process means checking whether the tool’s metrics, logic and results are correct and repeatable. The verification covers unit tests and data tests, to confirm that processing is correct and that metric definitions are consistent. In addition, the repeatability of the AI output is checked, and hallucinations and other errors that could reduce the usefulness of the recommendations are detected. The tests are measured against the previously agreed acceptance criteria, which determine readiness for the next stage.
Validation also uses a layer of SEO rules as quality control for the AI-generated output. The rules and tests can cover, among other things, detecting duplicate titles, missing headings and indexing errors, which helps to distinguish problems in the data from problems in the reasoning. Once verification is complete, a decision is made on moving to the production version, provided the quality thresholds for the results are met. Only then can the solution be launched with access control, logs, error monitoring and a data update process.
- Unit tests and data tests, and verification that the metrics are correct.
- Checking the repeatability of the AI output, detecting hallucinations and errors.
- Validating the AI output using SEO rules as a quality control layer.
- Assessing whether the acceptance criteria are met as a condition for moving to the production version.
Testimonials
What clients and the industry say
Feedback from clients and industry people I have worked with on SEO projects.
I had the chance to work with Kuba at Kulturalnie o SEO, an event I organise. Kuba did a great job as a speaker and received high marks from the audience. He showed professionalism and broad knowledge. In other projects at Vestigio, Kuba shows enormous commitment, a willingness to explore and implement new ideas, and excellent organisation of his work.
01 / 08
You talk to me, not a salesperson
Tell me what you want to achieve. I’ll reply personally
I work as a freelancer: the same person reads your message, prepares the quote and then runs the project. No sales team in between.