Marketing Personalization
Meitu needed one cloud platform that could supply advanced image-generation models, developer productivity tooling, performance telemetry, and large-scale analytics so it could improve AI imaging quality for a global audience without scattering workloads across multiple systems.
Reported impact
Meitu improved image-generation quality and cultural adaptability, boosted developer productivity, supported real-time monitoring for global apps, and strengthened retention across a base of 276 million monthly active users.
Meitu consolidated its AI imaging stack on Google Cloud so user-facing content generation, engineering productivity, and product analytics could all run on a common platform. On the creative side, Meitu uses Nano Banana and related Gemini-powered models to interpret user intent, reconstruct images, and generate culturally adaptive visuals for ecommerce sellers in more than 20 languages. Gemini Code Assist helps the development team raise coding and testing productivity, while Firebase gives the company real-time cross-platform performance monitoring. Usage data collected through Firebase is sent into BigQuery for multidimensional analysis, allowing teams to detect issues quickly and understand how product growth, quality, and user behavior evolve. Meitu is also building step-specific AI agents for intent understanding, content creation, detail modification, and multi-channel distribution, making Google Cloud the backbone for human-machine co-creation at very large scale.
User requests enter Meitu's imaging apps, are routed to the most suitable Nano Banana model for image generation or editing, monitored through Firebase across device and network conditions, and then aggregated into BigQuery so product teams can analyze usage patterns and improve future AI features.
Designkit automatically interprets each seller's image generation request and routes it to whichever model Nano Banana Pro or Nano Banana 2 best fits the task
Enables business-ready, culturally adaptive image generation with Nano Banana
Firebase automatically generates records that are easy to locate and track
The usage data collected through Firebase is then sent to BigQuery for multidimensional cross-analysis
Through Nano Banana and Gemini Code Assist, we’re able to employ the cutting-edge generative AI models to optimize our imaging features and enhance our development efficiency.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Google Cloud Sample Modified · Oct 9, 2026 HOLYWATER TECH HOLYWATER TECH wanted to produce long-form fantasy video content faster and more consistently, but traditional filming and fragmented AI tools made it hard to keep characters, settings, and sound synchronized across… 10x ARR growth for My Muse since implementing BigQuery · Gemini · Gemini platform Google Cloud Sample Archived · Sep 25, 2026 TapNation However, the company quickly realized that the true revolution lay elsewhere. "For us, AI isn't just another tool stacked on top of existing ones. It is the backbone of a new operating model, capable of supporting all… 60% reduction in time spent drafting technical specifications Gemini · Gemini Enterprise · Gemini Enterprise Agent Platform Google Cloud Sample Archived · Sep 13, 2026 Machina Sports Machina Sports needed to deliver low-latency live-sports experiences to large concurrent fan audiences while staying flexible enough to swap models, ground answers in fresh information, and expand multimodal features… Expanded market presence to 1.6M sports fans in Brazil Cloud Storage · Gemini Enterprise Agent Platform · Gemini Flash Lite 