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Agents tracked: 271 Downloads (7d): 219M up 6.2% GitHub stars: 5.5M VS Code installs: 151M Releases (7d): 307 Agent status: 1 with issues Updated Oct 8, 2026
Case study BeGlobal

How BeGlobal automated commercial offer creation with n8n and PromptGorillas

BeGlobal, a Dutch branded corporate gifts company, worked with the agency PromptGorillas to automate its proposal process. An n8n-orchestrated pipeline now turns a chat intake into a finished Google Slides offer, which removed the main bottleneck on how many offers the team could send.

The problem

BeGlobal could only create and send about 50 offers a year because each proposal was built by hand. Sales reps searched a catalog of roughly 100,000 products, then assembled a presentation with images and descriptions matched to the occasion. Past offers and CRM data for existing clients were not reused.

How they did it

  1. A custom frontend (built with Cursor) hosts a chat agent that collects product type, budget per unit, occasion, client name and domain, and whether the client is new or existing, then confirms the inputs with the user.
  2. An n8n workflow checks whether the client already exists and, if so, pulls CRM data and historical offers.
  3. An AI agent (GPT) node and code nodes build precise queries against Supabase, which stores the roughly 100,000 products. Results go back to the frontend via a webhook.
  4. n8n sends the selected product images through Nano Banana to generate themed, occasion-specific visuals.
  5. n8n duplicates slides from a Google Slides template in Google Drive, one per selected product, and fills in details, prices and the generated images.

Results

  • As reported by n8n, offers sent per year rose roughly tenfold, from ~50 to almost 500.
  • As reported by n8n, creating one offer went from a few hours manually to <1 min of workflow execution.
  • As reported by n8n, the end-to-end workflow takes around 52 seconds to produce a full presentation.
  • Long-term revenue metrics are still being collected, but the team can serve a larger pipeline with the same or less effort.

As reported by the source (n8n customer case study); AgentGid did not measure these figures.

Takeaway. A chat-based intake combined with database querying and template-driven slide generation can remove a manual proposal bottleneck, and a modular n8n setup lets you swap in new AI tools mid-project.
AgentGid's take

This suits a team with a structured product catalog, someone comfortable with n8n, Supabase and API wiring, and a reusable slide template; the advanced difficulty rating is fair. The roughly tenfold and 52-second figures are reported by n8n, not independently measured, and the case gives no revenue results yet. If you want a free open-source orchestrator, Dify (Free) is the closest alternative.

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