Catalog of services

You receive a structured list of problems and growth opportunities, prioritized technical tasks by impact and effort, an explanation of why each item matters, and practical implementation steps. When needed, we also include a short walkthrough call.

The stack depends on your infrastructure. For orchestration and workflows we commonly use tools like n8n, Airflow, Prefect, or Dagster. For integrations we rely on APIs and webhooks, for data we may use SQL, BigQuery, or a warehouse-oriented setup when appropriate, and for alerts we connect Slack or email with systems such as Jira or Linear. The principle is the same: the workflow must be reproducible, auditable, and protected from noisy alerts.

These services are suitable for websites where organic traffic, speed, and stable indexation matter: eCommerce, SaaS, content products, and corporate sites. The approach remains the same: diagnose, prioritize, and execute a clear action plan.

We assess the scope based on architecture size and complexity, templates, parameters, JavaScript dependencies, available data such as logs or field data, the expected deliverable format, and the level of implementation support needed. After a short brief, we provide a clear proposal by stages and outcomes.

We clarify the goals, access, and project context, align expectations, and then move into analysis and preparation of the deliverables. After that, we synchronize on findings and agree on the next implementation steps.

The base format is engineering deliverables: backlog, rules, and QA guidance. From there, implementation support is possible, including task clarification, planning participation, result review, and metric or regression control after releases. We usually work in close collaboration with your development team.

At minimum, read-only access to Google Search Console is recommended. Ideally, we also receive server or edge logs for the last 30–90 days, a list of page types or templates, and the sitemap or robots generation rules. For performance work, local measurements are helpful when available. For SPA or SSR projects, access to staging and a short rendering strategy summary is useful. For SERP analysis, it helps to have your key competitors, priority keywords, and target pages. The more context and data available, the more precise the recommendations.

No, but they significantly improve accuracy for crawl and indexation analysis. Logs are the closest thing to a source of truth for Googlebot behavior. Without logs, we compensate using crawl snapshots and Search Console, but some conclusions will carry lower confidence.

We establish a baseline using Search Console, crawl data, logs, and field performance data, segment results by templates, and define success metrics before implementation starts. Then we use staged rollout where possible, compare before and after states, and monitor regressions. The goal is measurable impact, not a vague sense of improvement.