Gemini API integration services
We integrate Google's Gemini models into applications for product teams working with images, documents, audio and video. We test real files and choose the deployment and operating controls for your quality, data and cost requirements.
01Capabilities
Gemini integration for multimodal and document workflows
We connect Google's Gemini API to an existing product through AI integration work, then validate the input and deployment requirements for the workflow.
- Pictures
Interpret image and video inputs
We integrate condition and damage photographs for logistics, and listing photos and walkthrough video for real estate. We test the required outputs on representative files, including difficult inputs.
- Estate
Deployment controls for a Google Cloud project
We integrate with the project and access rules chosen for your Google Cloud deployment. We verify endpoint, region and audit settings against the workflow's data requirements.
- Everything
Whole-file analysis or retrieval
We test sending a document set as whole files and building a retrieval layer. The decision follows answer quality, latency and cost on your corpus, including the needs of one-off analysis.
- Bulk
Model tiers matched to the task
For sorting, tagging and summarising at scale, we test appropriate Gemini model tiers against accepted outputs. We compare total cost at expected volume before choosing the model.
02Fit
Choosing Gemini API access and Google Cloud deployment
We select the Gemini access path around the application and its data requirements. Existing infrastructure, endpoint controls and the task test decide which deployment and model to use.
| Your situation | What we recommend |
|---|---|
| Photographs, scans, audio or video are the main input | Test Gemini on the workflowEvaluate Gemini on actual files and required outputs. Streaming work also needs a separate assessment of Google's Live API, model support, credentials and data controls. |
| Your application runs on Google Cloud with project and region requirements | Review managed cloudChoose the service and endpoint that support those requirements. Verify regional or global behaviour, caching and enabled features before approving the data path. |
| An existing product runs outside Google Cloud | Compare direct APIsGemini's Developer API can integrate with an existing application without moving the wider system to Google Cloud. Compare it with the OpenAI API against task quality and operating requirements. |
| A Developer API prototype is moving into customer use | Review service termsConfirm paid or unpaid service status, billing, jurisdiction and enabled features. Those conditions affect data use and need review before customer data enters the workflow. |
| No third-party inference is permitted | Evaluate local weightsWe evaluate local deployments using Llama or Qwen and verify the data path against that requirement. |
| One narrow judgement, repeated on clean text at high volume | Compare the task optionsCompare an existing product, Gemini tiers, DeepSeek or a task-specific model built with PyTorch against the task. Choose by accepted outputs and total operating cost. |
Scope
We own the API access and data terms recommendation, tests on representative inputs, whole-file and retrieval cost comparison, supported version pins and retirement planning. We validate the required capabilities of a non-Google fallback behind the same application interface. unicrew has built software since 2012 with 100+ senior in-house engineers across six countries and an ISO 27001:2022 certified information-security management system.
03Delivery
Deciding the deployment before writing any code
Deployment and test decisions establish what the Gemini feature may process and how it will be accepted.
- Choose the API path and record its termsWe review the exact service, paid or unpaid status, billing setup and jurisdiction. The decision records what content can be sent, which regional controls apply and which endpoint or feature conditions need approval.
- Test the outputs on difficult real inputsSkewed phone photos, dark video, faxed scans and forms filled in by hand belong in the test set. We score the fields or answers your workflow needs against accepted outputs.
- Compare whole-file input with retrievalWe test both designs on the same document set and expected volume. Answer quality, latency and operating cost determine how much material to send and what to retrieve.
- Pin supported versions and test the fallbackWe plan for model retirement and monitor latency and errors. A second provider stays behind the application interface with the required capabilities and passing tests. Our QA and test automation practice covers the feature and provider changes.
04Stack
Google Cloud and model choices around the integration
The Google Cloud platform and alternative models each carry a separate decision alongside the Gemini feature.
05Questions
Asked by a security reviewer looking at Google
Your reviewers assess the data terms and controls of the exact Gemini service and endpoint in scope.
Yes. We provide engineers to integrate Gemini with your application and test its multimodal workflows.
The deployment recommendation covers data handling and the chosen API or cloud service. An architect outside the delivery team reviews the design, and our AI QA and evaluation practice measures required outputs on representative input files.
Retention depends on the exact Gemini service, endpoint and enabled features.
Content submitted to paid services is not used to improve models under the stated terms; safety, legal and feature-specific processing can remain. Unpaid terms depend on billing and jurisdiction, including EEA, Switzerland and UK exceptions.
On Google Cloud, we verify region, endpoint, caching and enabled features before relying on a residency or retention control. We remove identifiers the task does not need.
Yes, when whole-file analysis meets the task's quality, latency and cost requirements.
We compare a whole document set with retrieval on your files. A one-off analysis and a repeatedly used feature can have different operating requirements; the measured results determine the design.
We compare Gemini and Claude on the documents, input formats and rules your workflow uses.
We test both on the same corpus, including scans, layout-dependent files and long instructions. The comparison with Claude covers output quality, latency, cost and deployment requirements.
Bring representative input files and the outputs your workflow needs to a scoping call with an engineer.
We assess feasibility, deployment, data handling and cost at expected volume using those examples. You receive the assessment in writing. Most engagements start within two to four weeks.
We quote Gemini integration engineering by scope. Google API or cloud usage is a separate cost.
Gemini API has paid and unpaid access paths; the selected service and terms determine which is suitable.
- Time and materials: billed hourly, quoted per project.
- Fixed price: outcome based, quoted per project.
- Team extension: billed monthly per team member.
Working with photographs, scans or video?
Send representative files, including the difficult examples, and the outputs you need. We will return a written view on feasibility, data handling, deployment and cost at your expected volume.