Need to process very long documents or multi-modal inputs that exceed other models' limits?
Already on Google Cloud and want AI that integrates natively with your existing infrastructure?
Google Gemini Integration Services
Gemini 1.5 Pro and Gemini 2.0 bring capabilities that other frontier models don't match: a 1 million token context window, native multimodal understanding across text, images, audio, and video, and deep integration with the Google Cloud ecosystem.
We integrate Gemini into your applications via the Gemini API and Google AI Studio -- using the right model for your use case, grounded in your data, and running reliably in production.
Gemini 2.0 Flash, Gemini 1.5 Pro, and Gemini 1.5 Flash via Google AI API and Vertex AI
Native multimodal: text, image, audio, video, and document understanding in one model
1M token context window for very long document and conversation processing
Google Cloud ecosystem integration -- BigQuery, Cloud Storage, Workspace
RaftLabs integrates Google Gemini (Gemini 2.0 Flash, Gemini 1.5 Pro, Gemini 1.5 Flash) into web applications, mobile apps, and data pipelines via the Google AI API and Vertex AI. We handle prompt engineering, multimodal input handling (text, images, audio, video), RAG pipeline development, Google Cloud ecosystem integration, and production deployment with monitoring. Gemini is particularly well-suited for teams already on Google Cloud, use cases requiring very long context windows, and applications processing multimodal inputs.
The right model for the right job
Gemini is not the right choice for every AI integration. We recommend Gemini when it provides a genuine advantage for your specific use case: very long context, multimodal input, or Google Cloud ecosystem integration.
We recommend GPT-4o or Claude when they are better fits. Our goal is a production AI integration that works -- not the integration that requires the most convincing to sell.
What we build with Gemini
Long document processing
Applications that analyse very long documents in full: entire contracts, full research papers, complete code repositories, lengthy transcripts, and multi-document corpora. Gemini 1.5 Pro's 1M token context eliminates chunking for most real-world documents. Use cases: full-contract legal review, codebase analysis and explanation, research synthesis across multiple long papers, historical document analysis.
Multimodal AI applications
Applications that process text, images, audio, and video together. Insurance claim processing that reads both the claim form and damage photos. Product catalogue enrichment from supplier images. Meeting analysis from video recordings. Technical specification processing combining drawings and text descriptions. Gemini handles all modalities in a single API call -- no separate model pipeline required.
Google Cloud integration
Gemini integrated with your Google Cloud data pipeline: querying BigQuery data with natural language, processing files from Cloud Storage, connecting to Cloud SQL databases, and using Pub/Sub for event-driven AI processing. IAM-controlled access, VPC Service Controls for data isolation, and Cloud Logging for audit trails. The right choice when your data lives in Google Cloud and you want AI that stays within your existing security perimeter.
Google Workspace AI
AI applications built on Google Workspace data: email intelligence from Gmail, document summarisation and Q&A from Google Drive, data analysis and generation from Google Sheets, and meeting intelligence from Google Meet transcripts. Built with appropriate OAuth scopes and user-level data access -- the AI sees what the user's Google account can see. Suitable for enterprise productivity tools and knowledge management applications.
Video and audio intelligence
Applications that extract insights from video and audio content. Meeting recording summarisation with action item extraction. Product demo analysis for sales intelligence. Training video indexing for learner support. Surveillance and monitoring applications (with appropriate compliance review). Audio content analysis without a separate transcription step. Gemini's native video and audio understanding simplifies pipelines that previously required multiple model calls.
Code intelligence
Code review, explanation, and generation applications using Gemini's large context window to process full codebases rather than individual files. Legacy code documentation generation. Codebase-aware AI assistants that understand your full project structure. Automated code review that considers full context. Migration assistance from one technology stack to another with full codebase awareness.
Using Google Cloud or processing long documents?
Tell us the use case. If Gemini is the right model, we will integrate it. If another model fits better, we will tell you that too.
Related services
ChatGPT Integration -- OpenAI GPT-4o API integration
Claude Integration -- Anthropic Claude API integration
Generative AI Integration -- model-agnostic generative AI integration
RAG Pipeline Development -- knowledge grounding across any model
Generative AI Consulting -- model selection and AI architecture strategy
Frequently asked questions
Choose Gemini when: you need to process very long documents (Gemini 1.5 Pro's 1M token context window handles entire books, codebases, or hours of video); you need native multimodal understanding across text, images, audio, and video in one model call; you are already on Google Cloud and want native Vertex AI integration with IAM, VPC, and Google-managed infrastructure; you need tight integration with Google Workspace (Docs, Sheets, Gmail) data. For general-purpose language tasks, GPT-4o and Claude are strong alternatives -- model selection depends on your specific use case, not brand preference.
Google AI API (ai.google.dev): Direct API access to Gemini models, simpler setup, usage-based pricing, suitable for prototyping and lower-scale production. Vertex AI: Google Cloud's enterprise ML platform, includes Gemini API access with additional enterprise features -- VPC Service Controls for data isolation, IAM-based access control, no data training opt-out by default, regional data residency, and integration with other Google Cloud services. Vertex AI is the right choice for enterprise deployments and Google Cloud environments. Google AI API is right for quick integration and lower-volume use cases.
Gemini processes text, images, audio, and video natively -- you can send a PDF with embedded charts and images and ask Gemini to analyse both the text and the visual content in a single API call. Practical use cases: document analysis that includes charts and diagrams (financial reports, technical specifications), video content understanding (summarising meeting recordings, extracting key moments from product demos), audio transcription and analysis in one call, and image-rich document processing (insurance claim photos + text, architectural drawings + specifications).
Gemini 1.5 Pro's 1M context window (approximately 750,000 words) allows you to include entire large documents, full codebases, or hours of transcript in a single context. This changes the RAG trade-off: for documents that fit in the context window, you can include them in full rather than chunking and retrieving. The cost trade-off matters -- 1M token inputs are expensive. We design the right context strategy for your use case: full context for tasks requiring complete document understanding, RAG retrieval for high-volume applications where cost is a constraint.
Yes. Via the Google Workspace APIs and Gemini's native Google integration, we build applications that access Gmail, Google Docs, Google Sheets, and Google Drive data with the user's permission. Common patterns: AI assistant that answers questions based on your company's Google Drive documents, automated processing of data in Google Sheets, email classification and routing based on Gmail content. Data stays within your Google account -- Gemini processes it on request, does not store or train on it by default.
Integration development costs $20,000--$70,000 depending on complexity. Gemini API costs: Gemini 1.5 Flash at $0.075/1M input tokens (very cost-efficient for high-volume applications), Gemini 1.5 Pro at $1.25/1M input tokens for standard context, Gemini 2.0 Flash competitive with Flash pricing. We model the expected monthly inference cost at your estimated usage volume before build.