In this section I review one AI-powered application and demonstrate how it can be used to create new value. This time: Google's Nano Banana Pro image generation model, which generated over 1 billion images within 53 days of release in late 2025.
If you've tried using AI to generate images for business purposes - infographics, presentations, branded content - you've probably hit the same wall I did: text. AI image generators have been notoriously bad at rendering text. Letters come out garbled, words are misspelled, and anything requiring readable text in an image was essentially unusable for professional purposes.
Nano Banana Pro changes this. Its key capability is reliable text rendering in generated images, and that single improvement opens up entire categories of business use cases that were previously off-limits.
What it can do
Beyond text rendering, Nano Banana Pro offers several capabilities worth noting:
- Up to 4K resolution - high enough quality for print and presentation materials
- Up to 14 reference images - feed it examples of your brand style and it maintains visual consistency
- SynthID watermarking - invisible markers that identify AI-generated content, increasingly important for compliance and trust
You can access it through Google AI Studio for free testing, Vertex AI for production API use, the Gemini app for consumer use, and third-party platforms like Replicate.
What I learned using it in production
I integrated Nano Banana Pro into Castifai, a visual infographic generation platform I built. Moving from "it works in demos" to "it works in production" taught me three lessons that apply to anyone deploying AI image generation.
1. Reliability requires fallbacks
The primary API through Vertex AI is powerful but not perfectly reliable. Requests occasionally fail, timeouts happen, and some prompts trigger content safety filters unexpectedly. In a production application, you can't show users an error message and call it a day.
I built a fallback chain: if the primary API fails, the system automatically tries alternative endpoints and configurations. This kind of redundancy feels like over-engineering until you realize that without it, roughly 5-10% of your users hit a broken experience. For a production tool, that's unacceptable.
2. Prompt consistency demands ongoing maintenance
Different visual styles require different prompts. What works for a clean corporate infographic doesn't work for a bold, colorful social media graphic. I ended up maintaining multiple prompt templates, each tuned for specific visual styles.
But here's the catch: when Google updates the model, those carefully tuned prompts can shift. An update that improves photorealism might degrade flat illustration quality. Each model update means re-testing your entire prompt library and retuning where needed. This is an ongoing operational cost that most teams don't budget for.
3. Unit economics shape everything
At approximately $0.15 per image, the per-use cost significantly impacts product decisions. How many images should a free tier include? Should users be able to regenerate indefinitely? At what point does a power user become unprofitable?
These aren't technical questions - they're business model questions. But they're driven entirely by the unit economics of the AI model. When I was designing Castifai's pricing, the image generation cost was the single largest factor in determining free tier limits, usage caps, and conversion strategy.
When to consider it
If you've previously dismissed AI image generation because of text quality, it's worth revisiting. The text rendering in Nano Banana Pro is genuinely good enough for most business use cases - not perfect, but reliable enough that you're fixing occasional issues rather than rebuilding from scratch.
The combination of text rendering, style consistency through reference images, and 4K resolution makes it viable for infographics, presentation materials, social media content, and branded visuals.
Your action step
Open Google AI Studio and test Nano Banana Pro with a text-heavy image generation prompt - something like an infographic title, a branded quote card, or a presentation slide visual. See if the text quality is good enough for your use case. If text rendering was previously the blocker for AI image generation in your workflow, it's time to reassess.