Gemini

Most mid-market teams already use Gemini without realising it. Smart Compose drafts an email. A doc gets summarised. Someone tries Deep Research for a competitor scan. That is the surface. The interesting question is what happens when an operator treats Gemini as infrastructure, not a chat box.

What Gemini Does

Gemini is Google's family of frontier AI models plus the platform that ships them. The same model line powers the consumer Gemini app, the AI features inside Google Workspace, the developer API, the Vertex AI enterprise platform, and Gemini Code Assist for engineering teams. One model lineage, many surfaces.

• Gemini 3 Pro and Flash. Frontier reasoning on Pro for complex analysis, Flash for high-volume tasks where latency and cost matter. Both are natively multimodal across text, image, audio, video, and code.
• Long context. Pro models handle very large inputs in a single call, so you stop chunking documents and stitching outputs together.
• Gemini in Workspace. The model lives inside Gmail, Docs, Sheets, Slides, Meet, and Drive on Business Standard, Business Plus, and Enterprise plans.
• Vertex AI. Google Cloud's enterprise surface for deploying Gemini with grounding on your data, tuning, context caching, batch pricing, and the agent runtime.
• Gemini Code Assist. An IDE assistant for VS Code, JetBrains, Android Studio, and the Gemini CLI, with Standard and Enterprise tiers and code customisation on private repos.
• Deep Research, Canvas, and Gems. Multi-step research reports, collaborative drafting, and reusable agent personas inside the Gemini app.
• Specialised models. Veo for video, Imagen and Gemini Flash Image for image generation, native TTS, and embeddings, all on the same billing surface.

Gemini's AI Platform

The shape that matters for operators is the platform underneath. Vertex AI gives you grounding on your own data, grounding with Google Search and Maps, context caching that cuts repeated-prompt costs by roughly 90 percent, batch pricing at half rate for non-urgent jobs, and an agent runtime that supports tool use, function calling, and computer use. That is the difference between bolting AI onto a workflow and running it as production infrastructure.

Automations We Build with Gemini

Gemini is at its best when it stops being a chat window and starts being a service inside your stack. The plays we run for mid-market clients almost all share that pattern: Gemini sits behind a workflow, called by code or an n8n node, returning structured output that another system can act on. Here is what that looks like in practice.

• Inbox triage and response drafting. A workflow reads new Gmail threads, classifies intent with Gemini Flash, routes to the right rep or queue, and pre-drafts a reply that lands in the assignee's inbox as a draft.
• Document extraction at scale. Contracts, invoices, RFPs, and lender packets get parsed by Gemini Pro with long context. Structured JSON lands in HubSpot, Salesforce, or your data warehouse with no humans copying fields.
• Internal knowledge agents. A Slack or Teams bot grounded on Drive, Notion, and your CRM that answers ops questions, drafts policies, and points new hires to the right document. Built on Vertex AI with retrieval grounded on your data.
• Meeting-to-action pipelines. Gemini reads Meet transcripts (or Fireflies, Otter, Fathom), extracts decisions and owners, creates ClickUp or Asana tasks, and posts a recap in the right Slack channel.
• Voice and multimodal support. Gemini Live API powers a real-time voice tier for tier-one support calls. Multimodal chat handles support tickets with screenshots, PDFs, and product photos attached.
• Code review and engineering velocity. Code Assist Enterprise tuned on your private repos. PR summaries, refactor suggestions, and on-call runbook generation that respects your conventions, not a generic style guide.
• Analyst-style reporting. Sheets gets a Gemini-powered tab that turns raw exports into a written summary, flags anomalies, and posts to Slack on a schedule. The CFO reads two paragraphs instead of opening a workbook.

Why Teams Choose Gemini

• Native Workspace integration. If your company runs on Google, Gemini is already authenticated against your data with the right permissions model. No bolt-on connectors, no shadow IT.
• Vertex AI for enterprise deployment. Data residency, VPC controls, audit logs, and an SLA that procurement actually accepts. Same model, enterprise wrapper.
• Cost shape. Flash and Flash-Lite are aggressive on price for high-volume jobs. Context caching and batch pricing make long-running pipelines cheaper than the equivalent on competing platforms.
• Multimodal as a first-class citizen. Image, audio, video, and PDF in the same call as text, not a stitched workflow. This matters more than people expect once you start automating real document and call workflows.
• One billing surface. Gemini app, Workspace add-on, Vertex AI usage, and Code Assist seats all reconcile through Google. Finance teams stop chasing six AI vendors.

Gemini integrates with the Google stack natively (Gmail, Docs, Sheets, Drive, Meet, Calendar, BigQuery, Cloud Run, Apigee) and with the rest of your tools through Vertex AI's agent runtime, the developer API, and standard webhooks via n8n or Zapier. Pricing covers a free tier in AI Studio, Workspace plans starting around $14 per user per month with Gemini included on Business Standard and up, Vertex AI usage-based pricing for Gemini 3 Pro at roughly $2 per million input tokens and $12 per million output tokens (subject to change), and Code Assist Standard at roughly $19 per seat per month. That is the build we do. Workspace bundle plus a Vertex AI deployment plus the automations that make the model actually work for your operators.