Technical SEO Consulting

What a technical SEO consultant actually fixes

Technical SEO is the part of search work that deals with access and understanding. Before any page can rank, a search engine has to discover the URL, crawl it, render it, decide whether it is the canonical version, and store it in the index. Problems at any of those steps quietly limit everything else you do in SEO.

The issues I find most often are not exotic. They include:

• Faceted navigation or internal search creating large numbers of near-duplicate URLs
• Canonical tags pointing to the wrong page, or conflicting with redirects and sitemaps
• Important content or links injected by JavaScript that crawlers do not reliably see
• Redirect chains and soft errors left behind by past redesigns or migrations
• Templates that strip titles, headings or structured data when a CMS setting changes
• Slow pages caused by heavy scripts, unoptimized images and third-party tags

Each of these has a root cause in how the site was built or how it is maintained. My job as a technical SEO consultant is to find that root cause rather than patch symptoms page by page, and to explain it in a way that both developers and managers can act on.

How I run a technical SEO audit

An audit starts with context, not a crawler. I want to know which page types matter commercially, how the site is built, who deploys changes and what has changed recently. A site with a few hundred pages on a standard CMS needs a different audit from a large JavaScript application with millions of parameterized URLs.

From there, I combine several data sources so that no single tool drives the conclusions:

• Crawl data to map URLs, status codes, internal links, canonicals and directives
• Google Search Console to see what Google reports about indexing, enhancements and performance
• Rendered HTML comparisons to check what changes after JavaScript runs
• Server logs, where available, to see real bot behavior rather than assumptions
• Field performance data for Core Web Vitals across real users

I then group findings by template and root cause. Instead of a list of thousands of affected URLs, you get a short set of underlying problems, the page types they affect, and the business reason each one matters. My technical SEO checklist gives a sense of the foundations every site should have in place before deeper work starts.

Deliverables: from findings to developer tickets

A technical audit is only useful if it turns into shipped changes. I write deliverables with your development process in mind, whether that is a sprint backlog, a ticketing system or an agency change request.

A typical engagement produces:

• An executive summary explaining the main risks and opportunities in plain language
• A prioritized issue register with root cause, affected templates, impact and effort
• Developer-ready tickets with the current behavior, the expected behavior and acceptance criteria
• Example URLs and test steps so fixes can be checked on staging
• Recommendations for robots rules, sitemaps, canonicals and redirect maps where relevant
• A structured data plan by page type, aligned with what the page visibly shows
• A release QA checklist your team can reuse after the engagement ends

I stay available while fixes are built, because questions always come up during implementation. If a recommendation conflicts with a product requirement or platform limitation, we work out an alternative together rather than leaving the ticket blocked. This is the same practical approach I bring to system integration work: the design has to survive contact with the real system.

JavaScript SEO, rendering and site architecture

Modern sites built on JavaScript frameworks can perform well in search, but they introduce risks that traditional sites do not have. Content may only exist after client-side rendering, internal links may be generated by click handlers rather than real anchor tags, and routing may produce URLs that return the same status regardless of whether the page exists.

I check how your setup behaves in practice:

• Whether primary content and links are present in the server response or only after rendering
• Whether each route returns correct status codes, titles, canonicals and meta robots
• Whether lazy loading, infinite scroll or tabs hide content that should be indexable
• Whether hydration errors or blocked resources change what crawlers see

Architecture sits alongside this. A clear hierarchy, sensible URL patterns and deliberate internal linking help search engines understand which pages matter most. On large catalogs or listing sites, I pay particular attention to filters, sorting parameters and pagination, because these decide whether crawl activity goes to valuable pages or to endless variations. Where a site generates pages from data, the same principles apply, and I cover that in more depth under programmatic SEO.

Measuring technical SEO and connecting it to revenue

Technical improvements should be measured, but honestly. I do not promise rankings or traffic, because those depend on competition and many factors outside the website. What we can track is whether the site is healthier and whether the pages that matter are being crawled, indexed and found.

The indicators I use include:

• Indexed versus submitted pages for each important template
• Crawl activity on priority sections compared with low-value URLs, from logs where available
• Core Web Vitals status from field data for key page groups
• Valid structured data items and any enhancement errors in Search Console
• Impressions, clicks and landing page performance for the affected templates
• Organic conversions and inquiries in GA4 for those same pages

Where you have a CRM, I go one step further and connect organic landing pages to leads, opportunities and closed deals. That connection is the core of how I work across CRM consulting and search: it shows whether technical fixes on commercial pages lead to real pipeline, not just better reports. For teams also investing in AI search, clean technical foundations support generative engine optimization as well, since AI systems still rely on content they can access and parse.