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Google's Generative AI Studio

Experience the power of Google's Vertex AI through Generative AI Studio, a managed environment that streamlines the interaction, customization, and deployment of foundation models for production applications.

General
Release date2023
AuthorGoogle
DocumentationLink
TypeGenerative AI Model Management

Start building with Generative AI Studio

Explore the best Generative AI Studio resources and libraries to help you get started with building projects using Google's Vertex AI today.

A curated list of libraries and resources to help you build outstanding projects with Generative AI Studio.

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Google Generative AI Studio AI technology Hackathon projects

Discover innovative solutions crafted with Google Generative AI Studio AI technology, developed by our community members during our engaging hackathons.

ShieldRadius AI

ShieldRadius AI

ShieldRadius AI bridges the gap between high-stakes threat detection and rapid revenue enablement. Built for the modern enterprise, it uses an event-driven, serverless state machine deployed on Vercel to turn open-web security indicators into immediate business action without hitting function timeout limits. How It Solves the Tracks: Track 3 (Security & Compliance): Features an automated, cron-triggered Threat Intelligence Agent that uncovers vendor-specific vulnerabilities, credential leaks, and compliance updates. A Risk Assessment Agent converts this into a structured, actionable threat footprint. Track 1 (GTM Intelligence): The moment a vulnerability is confirmed, a GTM Enrichment Agent tracks down accounts heavily reliant on that compromised tool. An Autonomous Outreach Agent then synthesizes this live security context into hyper-personalized executive briefs and switch-vendor outreach campaigns. Bright Data Integration: SERP API: Broad monitoring of news feeds and security advisories. Web Unlocker & Scraping Browser: Bypassing rigid enterprise bot-protection to safely map out status logs. Web Scraper API: Harvesters run asynchronously with webhook callbacks to pull enriched target account profiles and firmographics seamlessly under Vercel's serverless architecture.

OmniClaims Adjuster

OmniClaims Adjuster

OmniClaims Adjuster: El Futuro de la Liquidación de Siniestros En la actualidad, el procesamiento de reclamos de seguros es un proceso manual, lento y propenso a errores. OmniClaims Adjuster revoluciona el sector Insurtech mediante una arquitectura multi-agente totalmente autónoma construida sobre la familia de modelos Gemini 3.1 de Google. Diseñado para la AI Agent Olympics Hackathon, este sistema actúa como un ajustador de seguros experto. En lugar de depender de un solo modelo monolítico, el flujo de trabajo orquesta múltiples agentes especializados trabajando en paralelo y en tiempo real: 1. Agente de Extracción: Transforma las narrativas no estructuradas del cliente en datos estructurados estandarizados bajo esquemas estrictos de Pydantic. 2. Agente de Pólizas: Analiza los términos contractuales (PDFs) verificando límites, deducibles, exclusiones y coberturas con precisión milimétrica. 3. Agente de Visión (Daños): Aprovecha la multimodalidad nativa de Gemini 3.1 Pro para examinar fotografías de evidencias, evaluando la severidad y la congruencia del daño reportado. 4. Agente Antifraude: Detecta anomalías cruzando variables (ej. inconsistencias entre la historia del cliente y la evidencia visual) para emitir una puntuación de riesgo. 5. Agente Orquestador: Consolida todos los análisis en una decisión final holística (Aprobado, Rechazado o Revisión Manual). A nivel técnico, la plataforma cuenta con un backend en FastAPI y una interfaz Gradio con diseño premium glassmorphism. Priorizando la explicabilidad (AI Transparency), el sistema expone en la UI todo el Chain of Thought (Razonamiento) de los agentes. OmniClaims Adjuster no reemplaza al ajustador humano; lo empodera resolviendo automáticamente el 80% de los casos claros y entregando un dossier procesado de alta inteligencia para los reclamos complejos.

RELAY — Meeting to Document Intelligence

RELAY — Meeting to Document Intelligence

Enterprise teams don't struggle to write proposals. They struggle to coordinate them. Nine people contribute to the average enterprise RFP. It takes 9.3 calendar days to complete. 68% of that time is coordination — chasing colleagues, resolving version conflicts, and rebuilding context for every reviewer who joins late. $725,000 in proposal revenue is abandoned annually by the average organisation — not because the content was bad, but because the process collapsed. (Source: Loopio 2025 RFP Trends Report) RELAY is the coordination layer that existing tools don't provide. Upload any meeting recording — MP4, WAV, M4A, any format. RELAY's backend preprocesses it through FFmpeg (16kHz mono MP3) before sending to Speechmatics Enhanced ASR, which returns a fully diarized transcript with automatic speaker separation. The host then labels each speaker with their name, title, and team — with a "hear 5 seconds" audio preview per speaker so identity is confirmed, not assumed. Once speakers are labelled, the host selects a document type: Client Proposal, Statement of Work, Meeting Minutes, Requirements Document, or Strategy Brief. Total time from audio upload to collaborative document: under 90 seconds RELAY hits four of the five hackathon tracks: Agentic Workflows (autonomous audio-to-document pipeline), Enterprise Utility (documented $570K/year coordination problem for VP Sales Operations), Multimodal Intelligence (audio → structured text → AI generation), and Collaborative Systems (Speechmatics handles audio intelligence, Gemini handles generation, humans handle final judgment).

ActionPilot - Meeting Execution Agent

ActionPilot - Meeting Execution Agent

ActionPilot is an AI-powered meeting execution agent designed to solve one of the most common productivity challenges in modern organizations: turning meeting discussions into real, trackable execution. In many workplaces, meetings generate lengthy audio recordings, unstructured conversations, and scattered notes, making it difficult for teams to capture important decisions, assign responsibilities, and follow up effectively. Employees often spend hours manually creating summaries, writing follow-up emails, and organizing action items, while managers struggle to maintain accountability and ensure tasks are completed on time. ActionPilot addresses this problem by automating the entire post-meeting workflow using advanced AI technologies.

RackVision: Multimodal Spatial Agent

RackVision: Multimodal Spatial Agent

The Enterprise Friction Point: Global data centers operate with a costly "Visibility Gap." Network management systems track digital traffic and software logs but remain completely blind to physical environment data. When an on-site engineer over-bends a critical fiber patch or miswires a high-density GPU rack, the logical network drops packets silently, forcing engineering teams to waste hours guessing the physical root cause. The Solution: Beyond Copilots to Autonomous Action RackVision AI is a production-grade enterprise agent that unifies physical environment visual telemetry with logical infrastructure workflows, moving beyond simple chat copilots into real decision-making systems. Partner Technology Integration * Google Gemini Flash (via Gemini API): Handles our low-latency multimodal pipeline. It acts as an autonomous observer, analyzing live facility camera feeds to evaluate device port alignment, link-flapping anomalies, and cable strain before network failure occurs. * Google Gemini Pro: Drives our interactive "Sketch-to-Topology" simulation engine, instantly translating geometric hand-drawn whiteboard layout diagrams from onsite teams into dynamic logical twin configurations. Real-World Enterprise Utility When a physical or structural anomaly is caught by the multimodal pipeline, RackVision AI plans its own diagnostic steps without human intervention. It queries logical databases, maps the physical fault to the network topology, and triggers automated tool-calls to redirect mission-critical workloads minimizing downtime risks and optimizing Mean Time to Triage straight down to 48 seconds.

Generative AI Studio