10× more quote requests, driven by an AI technical advisor.

The advisor lays out the equipment that fits the plant an engineer describes. The buyer describes their facility in a chat interface and the system lists suitable products for the whole site in a single form. Previously that selection could not be made without expert support, and every question turned into a phone call. In mechanical seals the right choice depends on variables such as operating pressure and the fluid handled.

The rise came from engineers now being able to build their own list. The discovery flow was tied straight to the quote step, so a buyer adds several products to a list and sends one form. Before that the process ran on phone and email, and every request needed human follow-up. Once the friction went, request volume rose, because asking had become cheap.

The drop came from the request-to-response step moving into CRM automation. An incoming request lands in the system automatically, with data reaching the brand and a reply reaching the customer at the same moment. Previously the step was handled by hand and both timing and follow-up depended on individuals. Automation added consistency as well as speed: every request is now recorded the same way.

Discovery runs through command-palette search and a catalogue built to e-commerce depth. Every product is live with its technical description and imagery, and search reaches a result in seconds. Before, products could neither be found nor compared online. The catalogue architecture was deliberately built to an e-commerce standard, because industrial buyers now expect the speed of consumer interfaces.

The files are published so an engineer can verify their choice. A STEP or DWG file is downloadable for every product, letting a buyer test the part in their own design before moving to the quote step. In industrial purchasing, verification comes before the buying decision, and when that step cannot be done on the site the process can end in a change of supplier.

The languages were chosen against the target and adjacent markets: Turkish, English, Arabic and Russian. The SEO and GEO architecture was built from scratch in all four, positioning keywords inside a single structure. The four-language build also covered adjacent markets, and by the end of the engagement the Türkiye interface had moved ahead of the brand's global sites.

Local technical visibility lagged behind the global position. Meccanotecnica Umbra is the Türkiye arm of a world-leading manufacturer, yet in the local market products could neither be found nor compared online and the quote process ran on the phone. Rather than waiting for global sites to adapt to local search language and buyer habits, the structure was built on the questions of the Türkiye market.

Measurement ran on monthly organic impressions and ranking. Impressions reached 15,000 and keep climbing, and the brand ranks in the top 5 for every target keyword, mechanical seals included. Impressions are the indicator that arrives before clicks, and on an architecture built from scratch they give the first signal, because visibility can be measured before ranking settles.

The project ran 22 weeks, roughly five months. That span covered the catalogue architecture, the AI technical advisor, the quote portal, the CRM connection and the four-language search structure together. The pieces were not delivered separately: since the quote flow only means something once catalogue and advisor work together, all three went live in the same release.

The setup works where there is a technical catalogue and a selection problem. The lever is not the number of products but the buyer's inability to choose the right one alone. Where selection is simple, an AI advisor becomes overhead and the real gain sits in the quote portal and CRM automation. The precondition is structured product data — a scattered catalogue gets organised first.

The work falls under custom software and mobile apps, AI advisory and business automation. The three converged in a single application, with the four-language search architecture built as part of the same structure. The case sits in the Transform discipline, because the real gain was not a new product but an existing sales process measurably speeding up.