AI-OCR loyalty platform built on Google Vertex AI
- ~99%
- AI validation accuracy, up from ~80%
Google Gemini Integration Services
Google Gemini integration services that run reliably in production.
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
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
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?
Short answer
RaftLabs integrates Google Gemini into web apps and data pipelines via the Google AI API and Vertex AI, handling multimodal inputs, 1M-token context, and Google Cloud ecosystem. 20+ AI products shipped in 24 months for clients across the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia.
Key takeaways
- RaftLabs has shipped 20+ AI products in 24 months for clients in the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia
- Gemini integrations are delivered via the Google AI API and Vertex AI, with native Google Cloud ecosystem connectivity
- Gemini 1.5 Pro's 1M token context window enables full-document processing of 300-page agreements without chunking
- Integration development costs $20,000-$70,000 depending on complexity, with inference costs modeled at your usage volume before build
Trusted by
The 300-page contract that used to be a chunking problem.
A review team used to break every 300-page agreement into fragments, feed the pieces to a model one chunk at a time, and stitch the answers back together. Every chunk boundary was a place for context to fall through: a definition on page 4 that governs a clause on page 280, lost because the two never sat in the same window.
Gemini 1.5 Pro holds the whole agreement in one context. The model reasons across the entire document at once, so the clause on page 280 is read against the definition on page 4, not against a fragment.
The chunking pipeline was never the point. Reading the whole document was.
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.
According to McKinsey's 2025 State of AI report, 78% of organizations reported using AI in at least one business function in 2024, yet most deployments remain in pilot mode rather than full production. For teams already on Google Cloud, Gemini integration removes the biggest friction point between pilot and production: data residency, IAM alignment, and infrastructure fit.
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.
RaftLabs has shipped 20+ AI products in 24 months for clients across the US, UK, Europe, Canada, the GCC, South Africa, and Southeast Asia, backed by 100+ products shipped since 2015 for companies including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. One team scopes the integration and ships it: the people you meet in week 1 are the people who deploy in week 12. GDPR, HIPAA, and SOC 2 requirements are scoped in week 1, not retrofitted before launch, and Vertex AI's VPC Service Controls and data residency options are factored into the architecture from day one.
Gemini pays off when the use case plays to what only Gemini does.
Everything on the left should already be true for your use case. Even one thing on the right, and GPT-4o or Claude is the smarter first call.
A fit
01
Documents or multimodal inputs that exceed other models' limits: 300-page agreements, hours of video, or image-rich files you need read in full.
02
Already on Google Cloud, and you want AI that inherits your IAM, VPC Service Controls, and data residency instead of fighting them.
03
A production integration you can measure, with inference cost modeled at your real usage volume before you commit.
Not a fit
- A general-purpose language task where GPT-4o or Claude is the stronger, simpler fit.
- No long-context, multimodal, or Google Cloud advantage in play, so Gemini adds nothing you can't get elsewhere.
- You want the model that's easiest to sell, not the one that fits the use case.
What we build
What we build with Gemini
- 01Long document processingApplications that analyse very long documents in full, without the chunking and retrieval complexity RAG requires for other models. Built on Gemini 1.5 Pro, the roughly 750,000-word context window fits entire contracts, research corpora, and full transcripts in a single API call, so the model reasons across the whole document instead of retrieved fragments. We model cost at your expected query volume before choosing full context over a RAG approach.
- 02Multimodal AI applicationsApplications that process text, images, audio, and video in a single Gemini model call, replacing the multi-model pipeline of OCR, vision, ASR, and orchestration code that multimodal work used to require. Example: insurance claim processing, where the claim form and damage photos go in one API call and Gemini assesses both together. One call is typically cheaper than a stitched pipeline at moderate volume, and simpler to maintain.
- 03Google Cloud integrationGemini integrated with your Google Cloud data infrastructure so AI processing stays within your existing security perimeter, inheriting IAM access controls, VPC Service Controls for data isolation, and audit logging on every API call. BigQuery ML lets Gemini run directly from SQL to enrich large datasets without extracting the data, Pub/Sub and Cloud Run give you event-driven processing that scales to zero when idle, and grounding with Google Search adds cited, current answers where factual accuracy matters.
- 04Google Workspace AIAI applications built on Google Workspace data that operate within the user's existing Google permissions, no data migration required. The AI sees exactly what the authenticated user can see and OAuth scopes stay as narrow as the use case allows, across builds like Gmail classification and response drafting, Google Drive Q&A with document citations, Sheets automation, and Meet transcript summaries linked to the original calendar event.
- 05Video and audio intelligenceApplications that extract insights from video and audio in a single Gemini 1.5 Pro model call, without the separate transcription, speaker diarisation, and summarisation pipeline this work used to require. The model processes up to 1 hour of video or roughly 8.4 hours of audio in one context, for builds like meeting summaries with decisions and action items as structured JSON, timestamp-anchored references, and sales-call analysis that extracts objections and buying signals for CRM enrichment.
- 06Code intelligenceCode review, explanation, documentation, and migration applications using Gemini's large context window to process full codebases rather than individual files. A repository of 50,000-200,000 lines fits in a single context, so Gemini reasons about structure, dependencies, and patterns across the whole codebase at once, for builds like legacy code documentation, a codebase onboarding assistant, cross-file pull request review, and stack migration where file-by-file tooling loses the thread.
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.
How it works
From scope to shipped
Every Gemini integration follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 101
Discover and scope
We map your data sources, input types, and usage volume. We compare Gemini against GPT-4o and Claude for your specific case and recommend the model that fits. You leave week 1 with a written scope and a fixed-price quote.
- Weeks 2-302
Prototype and validate
A working prototype against your real data before the full build starts. We test context window strategies, multimodal input handling, and grounding approaches. We validate cost-per-query at your expected volume before committing to architecture.
- Weeks 4-1203
Build, integrate, and QA
Production integration with your Google Cloud infrastructure, Vertex AI or Google AI API, and your application stack. QA runs in parallel with every sprint. Bi-weekly demos. Working software at a staging URL by the end of sprint one.
- Weeks 12+04
Deploy and monitor
Production deployment with monitoring on launch day. Token usage, latency, and error tracking active from day one. 8 weeks of post-launch support included in every project.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Amer Abu Khajil
CanadaFounder, Peak Studios & Perceptional
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
01 / 02
What Gemini integration costs
We price integration by project, not by the hour, and we model your inference cost at your estimated usage volume before the build starts. Where you land depends on scope and model choice:
- Integration development, $20,000-$70,000
- Depending on complexity. Scope is mapped and the price is fixed before development starts.
- Gemini 1.5 Flash, $0.075 per 1M input tokens
- Very cost-efficient inference for high-volume applications.
- Gemini 1.5 Pro, $1.25 per 1M input tokens
- Standard-context pricing for full-document reasoning and 1M-token context.
- Gemini 2.0 Flash
- Competitive with Flash pricing. We model the expected monthly inference cost at your estimated usage volume before build.
What it costs
Gemini integration, starting at $20,000.
A working prototype against your real data first, then a production integration with the model, context strategy, and grounding your use case needs to run reliably.
Starts at $20,000
Integrations start at $20,000, with inference cost modeled at your estimated usage volume before build. Start with one workflow, then expand once it's proven.
If Gemini is the right model, we integrate it and start with the highest-value workflow first. If GPT-4o or Claude fits better, we tell you before you commit to either.
No hourly billing
Once we scope the integration, that price is locked in writing. No hourly billing, no surprise invoices as usage grows.
The right model, not the sellable one
We compare Gemini against GPT-4o and Claude for your specific case and recommend the model that fits, then model the inference cost at your real usage volume before build.
Stay on topic
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Read moreFrequently 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.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Google Gemini Integration Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.