Akçansa Case Study

Akçansa worked with Webtures on a programme that treated organic search and generative AI visibility as a single problem. In cement and ready-mix concrete, buying decisions are made from standards, strength classes and declaration documents, not from product copy. The work therefore centred on one question: is the technical truth readable from the company's own source, in a form a machine can parse.

Why Akçansa needed SEO and GEO support

Five structural challenges shaped the brief.

  • Entity sprawl. Akçansa is read alongside Betonsa for ready-mix, Agregasa for aggregates and AkçansaPort for ports, plus three integrated plants, 26 ready-mix facilities, four terminals and two ports. "Whose brand is Betonsa" had to be answerable from the company's own content.
  • Technical value locked in PDFs. Data sheets, declarations of performance and analysis certificates live largely inside PDFs. Information with no HTML counterpart cannot be quoted in a generative answer.
  • No authority layer on standards queries. When people search around TS EN 197-1, TS 13515 or EN 206, a weak explanatory layer on the producer's side hands the answer to standards resellers and forums.
  • A new question set created by regulation. Full application of the Carbon Border Adjustment Mechanism began on 1 January 2026 with cement in scope, and Türkiye's ETS pilot phase runs through 2026 and 2027 under Climate Law No. 7552. Exporters and buyers now ask questions that did not exist before.
  • Corporate freshness. Ownership changed: Heidelberg Materials holds 79.43 per cent, 20.57 per cent publicly traded. Copy describing an older arrangement risks teaching generative engines an outdated relationship.

Goals set for the Akçansa project

The programme aimed to bring the company's own sources forward on technical and standards queries, make the multi-brand, multi-site operation machine-readable, open the document layer to crawling, and strengthen presence in AI-assisted search. Each goal carried its regulatory boundary: the CE declaration regime sets the limits of technical language, and Turkish advertising regulation requires carbon claims to rest on evidence.

How Webtures approached SEO and GEO for Akçansa

Webtures combined technical SEO, content architecture and generative visibility. The premise is straightforward: there is no separate mechanism for appearing in generative answers. Google states that AI Overviews and AI Mode need no special markup; the precondition is being indexable and snippet-eligible.

Technical SEO and AI accessibility

Crawlability, indexation and parsability for AI systems were reviewed. In B2B building materials the decisive item is the document layer: when data sheets and declarations of performance are published only as PDFs, the value inside them never becomes a citable passage. HTML summary layers for those documents and a consistent structured-data description of the multi-site operation were handled here.

Content and semantic authority

Content was built around the questions people actually put to search engines and AI assistants. The questions arrive in the language of classes rather than product names: what CEM I 42.5 R denotes, which jobs call for C30/37, where low-carbon CEM II differs from CEM I. Answering these in passages that hold up out of context matters as much to an engineer on site as to a model choosing what to cite. Export topics such as CBAM and carbon accounting sat in the same layer, every statement tied to evidence.

GEO and AI visibility

Work targeted presence across ChatGPT, Gemini, Perplexity and Google AI Overviews. A category-specific question set was defined, with standards and product questions tracked separately from brand architecture questions. The second group was isolated deliberately: a wrong answer to "who makes Betonsa" points at entity confusion, not at a ranking problem. Uncited questions were treated as content gaps. Measurement relied on Search Console's Generative AI performance report; third-party tools claiming access to Google's internal metrics were not accepted as evidence.

Experience and conversion

Page structures and journeys were arranged so visitors could reach what they needed and act on it. Conversion here usually means a quote request, a dealer lookup or a document download, and those high-intent surfaces were treated as priorities.

What was carried out in the Akçansa project

Main workstreams across technical SEO, content architecture, structured data and GEO:

  • Crawl, indexation and rendering audit
  • HTML summary layers for data sheets and declarations of performance
  • Question-led content architecture for standards and class queries
  • Structured data clarifying brands, plants, terminals and ports
  • Freshness review of ownership and corporate information
  • Question-set monitoring across generative surfaces
  • Page experience and Core Web Vitals improvements

What the SEO and GEO work delivered

The programme was tracked across three reinforcing layers: organic performance on technical queries, entity clarity on brand architecture questions, and citation in generative answers. Search Console, analytics and regular review of the question set made up the measurement frame.

Organic traffic and search visibility

Coverage was widened across standards and class queries, and the document layer was audited for HTML counterparts. Search visibility was reported by query intent rather than by volume alone.

Visibility across AI platforms

Generative answers to the question set were monitored, tracking separately where the brand was cited and where it was absent. Accuracy on brand architecture and ownership questions was its own check, because an error there damages trust before it costs traffic.

The outcome of the Akçansa and Webtures partnership

In a category where technical claims rest on documents and documents rest on regulation, durable visibility is not held by a one-off optimisation. It is held by the discipline of making those documents readable and by a measurement loop that keeps checking.

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