Build AI products with Google’s Gemini models
Gemini provides powerful multimodal and reasoning capabilities alongside close integration with Google’s cloud ecosystem.
- The stack serves the product, not the other way around
- AI assistants
- Multimodal applications
- Document understanding
System view
How it sits in a real application
We choose technology based on the product, team and business problem — then wire it into a maintainable system, not a logo collage.
What we use it for
Where this shows up in products
AI assistants
Multimodal applications
Document understanding
Content processing
Agent workflows
Search and retrieval
Google Cloud applications
Why we use it
Why it earns a place in the stack
- Strong multimodal capabilities
- Natural fit for Google Cloud and Vertex AI estates
- Competitive reasoning and retrieval-oriented workloads
- Works in multi-model architectures beside OpenAI and Claude
When we would not
When we recommend something else
- When the product is not on GCP and another provider is clearly stronger for the task
- When procurement already standardizes on a different model family
- When a non-generative approach is simpler
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Related technologies & services
FAQ
Questions teams ask
Straight answers on stack choices, fit, and how we engage.
Gemini is the model family; Vertex AI is often how enterprises deploy and govern AI on Google Cloud. We use both as the architecture requires.
Yes — document, image and mixed-input workflows are a common reason we evaluate Gemini.
Yes. We frequently design routing so different jobs use Gemini, Claude or OpenAI based on measured fit.
Next step
Build with Gemini
Especially strong when AI and Google Cloud already meet in your organization.
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