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 date	2023
Author	Google
Documentation	Link
Type	Generative 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.

Generative AI Studio Links

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

Generative AI Studio Official Documentation Learn how to use Generative AI Studio with the official documentation.
Google's Foundation Models Browse the wide array of foundation models available in Generative AI Studio, such as PaLM, Imagen, Codey, and Chirp.
Generative AI Studio API Quickstart Get started with the Generative AI Studio API with this helpful quickstart guide.

clawback

The Problem Shippers pay carrier invoices with thousands of charges, and nobody has time to check each one against the negotiated contract. Audit vendors report that freight audits recover about 3 to 7 percent of freight spend, and carriers often allow only a short dispute window, commonly around 15 days for parcel. Outsourced audit firms commonly take 20 to 50 percent of what they recover. These figures come from vendor sources and are indicative only. Why This is a Commerce Track Project Most commerce tools chase top-line revenue growth. Clawback protects margin from the cost side. In commercial logistics and supply chain operations, freight spend represents 8 to 15 percent of total cost of goods sold (COGS). A dollar recovered from an overcharge drops directly to pre-tax net margin. How AMD is Used Model & Host: The model that reads contracts and normalizes messy invoice charge descriptions is google/gemma-2-9b-it, hosted on AMD Developer Cloud / AMD Token Factory infrastructure (notebooks.amd.com). Replay Mode: When offline or if API quotas are exhausted, a deterministic replay mode serves recorded outputs from real AMD calls, visibly labeled with a persistent REPLAY badge. Workload Concurrency: Utilizes k=3 parallel self-consistency consensus extraction and batch charge-description normalization, fully utilizing AMD ROCm GPU concurrency. Measured Results (Synthetic Benchmark) Source: benchmark/results.json (Mode: replay / baseline; Run Date: October 16, 2026). Accuracy: Audit Recall: 100.00% (284 of 284 planted errors detected) Audit Precision: 100.00% (0 false positives) True Positives: 284 | False Positives: 0 | False Negatives: 0 Planted Ground Truth: $9,130.51 USD Recovered Overcharges: $9,130.51 USD Dollar Variance: $0.00 (0.00%) Note: These results prove the pipeline and deterministic arithmetic engine work as designed on synthetic benchmark data. They are not evidence of real-world carrier recovery rates.

NEXUS ONE — Multisectoral AI Platform

NEXUS ONE is a multisectoral AI platform designed to help organizations analyze documents, access reliable knowledge, and automate complex tasks through specialized AI agents. It combines Retrieval-Augmented Generation (RAG), document analysis, modular AI workflows, security, and data governance to provide context-grounded answers while supporting responsible data management. INDUXIA is the industrial intelligence module of NEXUS ONE. It focuses on equipment monitoring, anomaly detection, predictive maintenance, and industrial resilience. By combining technical documentation, maintenance manuals, operational data, and AI-assisted analysis, INDUXIA aims to help industrial operators identify potential problems earlier, improve maintenance decisions, and reduce operational disruptions. The platform is designed with a modular architecture that can evolve to support different sectors, including industry, healthcare, education, agriculture, finance, telecommunications, and public services. Each module can address specific needs while sharing a common foundation for AI orchestration, document retrieval, access control, and governance. Our long-term vision is to build a secure, adaptable, and scalable AI ecosystem developed with African needs in mind and designed for use in Africa and internationally. NEXUS ONE provides the multisectoral foundation, while INDUXIA demonstrates its practical value through industrial predictive intelligence. The project aims to make advanced AI more accessible, useful, and responsible for organizations of different sizes.

Culltron CivicOps

The problem. In a South African storm, municipal call centres flood. Reports arrive by phone, WhatsApp, email and walk-ins, and get re-typed, lost or duplicated. Residents never learn what happened, and control rooms cannot show who decided what. What CivicOps does. One front door: residents report by voice, chat, SMS, WhatsApp, web form or Open311 and get a reference number. On the live demo the resident agent is an ElevenLabs conversational agent using short-lived signed sessions. Bounded agents (Gemini 3.5 Flash-Lite, with a deterministic fallback) extract facts, triage, flag duplicates, recommend an eligible crew and draft resident updates, recording provider, model, confidence and reasoning. Agents cannot approve, assign or send anything: a dispatcher approves and assigns, and a supervisor names the incident commander and decides mutual aid. A live map shows aggregated operations only, with no person tracking. Multiple agencies share one incident and timeline. SOS needs a deliberate 3-second hold and never replaces 10111, 10177 or 112. Every action goes into an append-only SHA-256 hash-chained audit trail verifiable in the browser. Hard spend caps switch the demo to a labelled simulation. Honest scope. The demo uses synthetic data (EXPLICIT DEMO); email goes to an outbox only and field responses are simulated; Afrikaans and isiZulu translations await native review. An AMD Instinct/vLLM open-model path is implemented but was not run on AMD hardware for this event.

TrustForge — Evidence-Gated AI Software Factory

TrustForge is an autonomous software factory designed around one principle: code that works is not necessarily code that can be trusted. Instead of relying on a single AI coding agent to build software and declare it finished, TrustForge creates a complete engineering loop: Architect → Operator → Adversary → Verifier → Repairer → Verifier → Evidence Each seat has a bounded responsibility. The factory transforms requirements into explicit properties, generates adversarial scenarios, implements the required behavior, independently verifies the running system, repairs concrete failures and re-verifies the repaired revision before producing an evidence package. For the Pocketful track, TrustForge produced a self-contained Stage 1 wallet and payments service covering authentication, payments, requests, bill splitting, idempotent writes, atomic multi-item operations, exact integer money arithmetic, activity visibility and state export/import. The verification philosophy is simple: Requirement → Property → Scenario → Implementation → Execution → Observation → Verdict → Repair → Re-verification TrustForge does not treat “tests passed” as proof of universal correctness. Its evidence model explicitly distinguishes VERIFIED, FAILED, INCONCLUSIVE and BLOCKED states and preserves limitations and failure history. The recovered project records 147/147 shipped checks passing in the official isolated Pocketful Stage 1 harness, alongside independent HTTP verification and reproducible container instructions. The factory itself is designed to be reusable beyond Pocketful. Its mandates are domain-agnostic and can be applied to software, APIs, business automations, AI agents and AI-generated applications. TrustForge's goal is not simply to make AI build faster. It is to make AI-built software harder to trust blindly.

InterviewAI

InterviewAI is an AI-powered voice interview coach designed to help users prepare for upcoming interviews through realistic mock interview sessions. The user interacts with an AI interviewer through voice, allowing them to practice answering questions in a natural conversational environment. During the session, the AI interviewer asks relevant interview questions and responds based on the conversation. After the interview is completed, InterviewAI analyzes the candidate's responses and generates a structured session report. The report provides an overall performance score along with individual evaluations for communication, relevance, clarity, confidence, and technical/content quality. The report also highlights the candidate's strengths and identifies specific areas that need improvement, helping users understand how they performed and what they should work on before a real interview. InterviewAI therefore combines realistic voice-based practice with actionable AI-generated feedback in a single interview preparation experience.

Adaptive Compute: Inference Optimization

Adaptive Compute is an intelligent AI inference engine that dynamically chooses how an AI workload should be executed based on its requirements and the current system state. Instead of relying on a fixed model configuration, the system analyzes factors such as workload size, latency requirements, memory availability, compute utilization, batch size, model variant, and numerical precision. It then selects an execution strategy designed to balance inference speed, resource consumption, and output quality. The system continuously monitors inference performance and feeds the results back into its decision layer. Over time, this feedback can help the engine make better choices for recurring workloads and changing system conditions. For example, a latency-sensitive request may use a lightweight model and smaller batch, while a large batch workload can use a more throughput-oriented configuration. The goal is to demonstrate that AI inference does not need to use a one-size-fits-all configuration. By making compute decisions dynamically and measuring the resulting performance, Adaptive Compute aims to improve the practical efficiency of AI applications while maintaining acceptable inference quality. The project will focus on measurable benchmarks across latency, throughput, memory usage, utilization, and model accuracy.