Gemini

Gemini gives workflows another model option where Google's context window, pricing, or data residency makes it the better fit. n8n treats it the same as any other AI step, so switching or combining models is a configuration choice, not a rebuild.

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Use cases built on Gemini

Real deployments that lean on this integration.

Industrial Distribution & Wholesale

2,250 h saved/year

AI Quote & Inventory Matching Assistant

A B2B distributor carrying thousands of SKUs was drowning in inbound quote requests. Customers emailed in technical, jargon-heavy part descriptions (thread types, materials, dimensions) that took skilled staff real time to decode, cross-reference against inventory, and respond to. With only a handful of customer service reps handling the volume, routine requests ate hours that should have gone to relationship-building and complex orders, and the occasional large multi-line request could tie up the whole team for days.

Logistics & Freight / Transportation

$510k / year

AI Shipment Intake & Human-Verified Data Entry

A freight logistics operation was processing roughly 1,000 shipment requests a month, arriving from vendors in every format imaginable: emails, inconsistent PDFs, even handwritten forms. Getting that information into the Transportation Management System meant staff manually reading each document, decoding the details, and typing them in by hand. A team of six spent roughly half their day on this alone, and multi-stop loads made it worse: pairing related shipments relied on staff memory and manual cheat sheets, often holding up carrier booking for a full day while someone tracked down the matching load.

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