Trinki

What AI says

AI engines have very limited information about Trinki, a Food Distribution supplier in fr. Across recent queries the company scored 8 out of 100 (Dark Signal), with the strongest dimension being Digital Footprint at 50 out of 100 and the weakest AI Recommendation Rate at 0 out of 100. None of the 2 tested AI engines (GPT-4o mini, Claude Haiku) returned substantive information about the company, even when queried by name, category, or domain. No specific products were surfaced by AI engines for this supplier, which contributes directly to the Product Clarity dimension at 0 out of 100. No certifications or third-party validations surfaced in AI responses, which limits the Buyer Trust dimension at 0 out of 100 and the AI Recommendation Rate at 0 out of 100. The biggest opportunity for improvement is the AI Recommendation Rate dimension; the Improvement Roadmap below lists concrete supplier-executable actions ranked by expected score uplift.

AI engine breakdown

One row per AI engine. Aggregates identity, sourcing, and trust queries.

Improvement Roadmap

Concrete actions to lift this supplier's Signal Score, ranked by impact. Generated from the latest audit.

1. 1

   Register Trinki on Google Business Profile and major B2B directories

   +18 pts

   Create or claim your Google Business Profile with accurate company name, address, and phone number. List Trinki on at least 3 B2B food distribution directories (e.g., Kompass France, TradeKey, or local French food supplier registries). This ensures AI engines can discover and verify your company exists, directly lifting findability from 0/100.

   Identity & Presence · Low effort

2. 2

   Publish structured product list on website with schema markup

   +16 pts

   Add a dedicated products or services page listing the specific food categories and product types Trinki distributes (e.g., frozen foods, fresh produce, specialty ingredients). Use Schema.org Product or LocalBusiness schema markup so AI crawlers can parse offerings. Currently AI found zero products; explicit markup ensures LLMs cite your actual range.

   Product Clarity · Medium effort

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