Google Gemini application development
Google Gemini Application Development
Build multimodal and long-document AI features on the Gemini API and Vertex AI.
Gemini is Google's family of multimodal AI models. Developers use it through the Gemini API and, for enterprise workloads, through Vertex AI on Google Cloud. We build applications that use Gemini for documents, images, audio and video, with function calling, grounding and production controls.
What we cover
- Long-document analysis
- Images, audio and video
- Function calling and structured output
- Grounding and retrieval
- Vertex AI deployment
- Context caching and cost control
Requirement-led advice · Documented handover
- Advice based on your requirements
- Works with your existing stack
- Documented, maintainable delivery
What Is Google Gemini?
Gemini is a family of AI models from Google DeepMind. The models accept text, images, audio, video and PDF documents as input, and some support very long context windows, so a single request can include large documents or long recordings. Faster, lower-cost models suit high-volume tasks, while larger models suit complex reasoning.
Developers can call Gemini through the Gemini API, prototyped in Google AI Studio, or through Vertex AI, which adds Google Cloud identity, networking, data residency options and enterprise controls. Features such as function calling, structured JSON output, grounding with Google Search and context caching help turn a model into a dependable application component.
When Gemini is a good fit
- Your inputs are long documents, scanned files, images, audio or video.
- You run on Google Cloud, or want Vertex AI controls for AI workloads.
- You need to process large volumes at low cost with smaller models.
- You want to compare Gemini with other models for the same task.
Where Teams Use Google Gemini
Applications that play to Gemini's multimodal and long-context strengths.
Document understanding
Extract data from long contracts, reports and scanned forms.
Image and video analysis
Describe, classify or inspect images and video footage.
Meeting and call analysis
Summarise recordings and pull out actions.
Research assistants
Answer questions across large document sets with sources.
Agents with tools
Call your APIs through function calling with structured output.
Content workflows
Draft and adapt content with human review.
Product features
Add multimodal AI features to web and mobile apps.
Data extraction at scale
Batch-process documents into structured data.
Google Gemini Services We Deliver
Each piece of work has a clear output, so you know what you will receive.
Use-case and model selection
Match tasks to Gemini models and compare against alternatives.
Deliverables: model comparison, cost estimate
Prototype
A working prototype in AI Studio or Vertex AI on your data.
Deliverables: prototype, quality results
Application integration
Backend services that call Gemini with function calling and structured output.
Deliverables: API service, schemas, tests
Grounding and retrieval
Ground answers in your documents or Google Search where appropriate.
Deliverables: retrieval design, citation handling
Vertex AI deployment
Identity, networking, quotas and monitoring on Google Cloud.
Deliverables: deployment, monitoring, runbook
Evaluation
Test sets and checks to track quality as models change.
Deliverables: evaluation suite, release checklist
Gemini Building Blocks
Parts of the Gemini platform we commonly use.
Access
- Gemini API
- Google AI Studio
- Vertex AI
- Google Gen AI SDKs
Capabilities
- Multimodal input
- Long context
- Function calling
- Structured output
Grounding and data
- Grounding with Google Search
- Retrieval over your documents
- Cloud Storage and BigQuery
Operations
- Context caching
- Batch processing
- Quotas and monitoring
- Google Cloud IAM
Listing a feature here does not mean every project uses it. We recommend what fits your requirements, skills, licensing and running costs.
How Gemini Fits Your Stack
Gemini applications can run on Google Cloud or call the Gemini API from services hosted on Microsoft Azure or AWS, subject to your data policies. We build the services in Python or .NET and often orchestrate them with LangChain and LangGraph.
Many organisations use more than one model provider. We can design applications so that Gemini and models in Microsoft Foundry sit behind the same interface, making it easier to switch or compare.
Implementation Considerations
- Check model availability and data-residency options for your region before committing.
- Long context is powerful but costly; use caching and retrieval to control spend.
- Structured output and validation make results safe to pass to other systems.
- Evaluate on your own data, because benchmark results do not always transfer.
- Review Google's data-use terms for the API tier and Vertex AI you plan to use.
- Keep humans in the loop for decisions with legal or financial impact.
How We Work With Google Gemini
The same four steps we use across our technology work, adapted to your scope.
Discover
Goals, users, constraints and the systems Google Gemini must work with.
Select
Confirm Google Gemini is the right fit and agree the architecture and scope.
Build and integrate
Deliver in short iterations with testing, reviews and documentation.
Operate and improve
Monitor, support and improve the solution as your needs change.
Google Gemini Questions, Answered
What is Google Gemini?
A family of multimodal AI models from Google that work with text, images, audio, video and documents.
What is the difference between the Gemini API and Vertex AI?
The Gemini API is the quickest way for developers to start. Vertex AI adds Google Cloud enterprise controls such as IAM, networking and regional options.
Can Gemini read long PDFs and recordings?
Yes. Gemini models accept long documents, audio and video. We test accuracy and cost on your own files.
Can Gemini call our systems?
Yes, through function calling with structured output. Your application decides which calls are allowed and checks the results.
Is our data used for training?
It depends on the service tier. Paid and Vertex AI tiers have different terms from free tiers, and we review them with you.
Can we combine Gemini with other models?
Yes. We often design applications that can use several providers behind one interface.
Planning a Gemini Project?
Tell us about the content you want to process and the systems involved, and we will suggest a first step.
Product names and logos belong to their respective owners. Mentioning a technology does not imply a partnership, certification or endorsement.