Native multimodal processing
Text, images, scanned documents, audio and video can be analyzed by the same model. This is useful for classifying attachments, reading forms, or transcribing and summarizing conversations.
AI integration · Gemini
Gemini API Development: Multimodal AI and Agents in Production
Etixio builds AI solutions with the Gemini API: document processing, multimodal interfaces, agents and automation. We evaluate Google’s models in your context before integrating them into your product, then deliver a production-ready service with controls, evaluations and cost tracking.
Understanding the technology
A model integrated into your application.
Google’s Gemini models can be used in applications that process text or, depending on the model, other types of content. The choice of interface and service determines the capabilities and operating conditions available. We integrate these components within a defined software scope.
The Gemini API is available directly or through Vertex AI on Google Cloud. In both cases, the model is just one building block: around it, we build the service that prepares the data, checks the results and integrates with your product as AI features or with an AI agent connected to your tools.
The strengths for your product
Why choose the Gemini API?
Gemini stands out mainly for its multimodal processing and its integration with the Google ecosystem. We choose it when these strengths match your use case, after comparing it on your own examples.
Long documents
Depending on the model, the context window makes it possible to process large case files in a single request, which simplifies some document pipelines.
The Google Cloud ecosystem
Vertex AI for deployment, monitoring and governance; BigQuery to connect AI to company data; Google Workspace for office use cases.
A lineup suited to volume
Flash and Flash-Lite models make it possible to process large volumes at a controlled cost; Pro models are reserved for steps that require in-depth analysis.
Reasoning and planning
Recent models chain several steps, call functions and produce structured outputs, making it possible to build agents controlled by the application.
Our scope of work
Choosing a model family for the work at hand.
We compare Gemini families on the same set of representative requests, using your real formats: scanned PDFs, images, audio recordings. The cost of multimodal processing depends on file size and duration; we measure it from the first trials. With Vertex AI, the processing region is chosen on Google Cloud. When a European provider or hosting on your own servers is required, we also compare with Mistral or a self-hosted open-source model.
Gemini Flash
A family for general processing and agentic workflows.
Gemini Flash-Lite
A family to evaluate for high-volume processing.
Gemini Pro
A family to evaluate for tasks that require in-depth analysis and complex reasoning.
Our Gemini API expertise
The Gemini ecosystem we work with.
Model access
Gemini API and Google AI Studio for prototyping and comparison, Vertex AI for enterprise deployment, official SDKs (Python, Node.js, Java, Go).
Data and documents
Search across your documents (RAG), extraction from PDFs and images, data analysis with BigQuery, classification and summarization.
Multimodal
Analysis of images and scanned documents, audio transcription and understanding, video analysis depending on the use case.
Agents and integration
Function calling, structured outputs, connection to your APIs and tools, with logging, monitoring and usage caps.
In the field
An example processing pipeline.
For a document classification process, we prepare the content, categories and examples needed for evaluation. The service classifies documents or extracts information, then the application checks the format and applies business rules. Errors and uncertain cases are kept to improve the pipeline and decide which human validations are needed.
From prototype to production
Moving from an AI Studio trial to a production-ready service.
Many Gemini projects start with a successful trial in AI Studio or a notebook. We take over that POC and turn it into a service: maintainable code, authentication and access rights, error and timeout handling, an evaluation set, monitoring and cost tracking, deployment on your infrastructure or on Google Cloud.
The service is then maintained and re-evaluated with every model change. See our AI POC to production service, our AI solutions for businesses and our precautions for controlled AI use.
The choices that matter
Controlling the documents you send.
The documents and media sent to the model are often the most sensitive: ID documents, contracts, recordings. We define what can be sent, mask what is not needed and enforce access rights in the application before any call.
With Vertex AI, Google Cloud identities, the processing region and audit logs fit into your existing governance. Each model change is replayed against the evaluation set, file formats included, before it is deployed.
From work to deliverables
The software delivered around Gemini.
- Preparation pipeline for documents, images and audio files.
- Connections to data (BigQuery, storage, business APIs) within the agreed scope.
- Evaluation set covering each format, error and usage tracking.
- Code, Google Cloud configuration and documentation to run the service.
The service is deployed in your Google Cloud project or your own infrastructure, with your accounts and access. The delivery framework specifies environments and maintenance.
Frequently asked questions
Gemini API: your questions.
What is the Gemini API?
The Gemini API provides access to Google’s multimodal language models, developed by Google DeepMind. An application sends them text, documents, images or audio and receives a response, which can be structured or include function calls. Access is direct or through Vertex AI on Google Cloud.
Gemini, OpenAI or Claude — what are the differences?
Gemini is often chosen for multimodal use, long documents and Google Cloud integration; OpenAI for its versatility; Claude for technical tasks and agents. We decide on a set of representative examples, comparing quality, latency and cost. If hosting in Europe or on your own servers is required, Mistral or a self-hosted open-source model join the comparison.
Which use cases are a good fit for Gemini?
Document classification and extraction, analysis of images or scanned files, transcription and summarization of conversations, data analysis support, agents connected to Google services. Gemini is relevant when your content is not just text or when your system already runs on Google Cloud.
Does Gemini handle multimodal content well?
Yes, it is one of its strengths. The same model can analyze text, images, audio and video. As with text, results are checked by the application and evaluated on real examples before go-live.
How can Gemini be used for complex analysis?
By breaking the problem down into steps, enforcing an output format and adding checks between steps. Pro models are reserved for steps that require in-depth reasoning; simple steps use faster, less expensive models.
How do you deploy Gemini in production?
Through Vertex AI when you are on Google Cloud, or through the direct API integrated into your application. In both cases, we add authentication, error handling, logs, monitoring, evaluations and usage caps.
What are Gemini’s limitations?
Like any language model, Gemini can produce inaccurate answers. Pro models cost more, and some features depend on the Google Cloud ecosystem. An evaluation set and application-level checks remain necessary.
Is Gemini suitable for enterprises?
Yes, especially for organizations already on Google Cloud or Google Workspace. Vertex AI provides the expected governance and monitoring features. Data processing terms should be checked for the plan you choose.
How long does it take to integrate Gemini?
It depends on the scope: a few weeks for a targeted feature in an existing application, several months for a complete solution with data, agents and monitoring. A scoping phase sets a realistic initial scope.
What engagement models do you offer?
A fixed-price project for a solution with a defined scope, a dedicated team to evolve an AI-powered product over time, or a targeted engagement (POC takeover, migration to Gemini).
Let’s build an AI use case integrated into your business.
Tell us what you want to build, the users involved and your technical environment. Together we will define the initial scope to explore.