Build AI agents on Google Cloud.
Kovil AI is a specialist Vertex AI implementation partner. We design, build, and deploy production-grade AI agents on Google Cloud — Gemini integration, Agent Builder, Vertex AI Search RAG, BigQuery ML — in fixed-price sprints with zero delivery risk.
2-week risk-free pilot · Fixed-price sprints · GCP-certified engineers
Kovil AI is a specialist Google Cloud Vertex AI implementation partner serving enterprises across the United States and United Kingdom. Our practice covers Gemini 2.0 integration, Vertex AI Agent Builder, Reasoning Engine orchestration, Vertex AI Search RAG pipelines, BigQuery ML, Document AI, and Vertex AI governance — delivered from our New York and Austin offices.
The GCP AI Stack
The Google Cloud AI Stack
Vertex AI is not a single product — it is a layered platform. Understanding which tier to use for which job is the difference between a prototype and a production system that holds up under enterprise load.
Why Google Cloud AI deployments stall — and what we do differently
Gemini API access without an agent architecture
We design the full Vertex AI stack upfront — model selection, Agent Builder vs Reasoning Engine, grounding strategy, memory management, and tool integrations — before writing a single line of code.
RAG with no enterprise data grounding
We connect Vertex AI Search to your live GCP data sources — GCS, BigQuery, Cloud SQL, APIs — with chunking strategy, hybrid retrieval, semantic re-ranking, and IAM-controlled access throughout.
Dev builds with no GCP security posture
Every build includes VPC Service Controls, IAM least-privilege bindings, CMEK encryption, audit logging, and DLP scanning — configured from the first sprint, never retrofitted at go-live.
Vertex AI Explained
What is Google Cloud Vertex AI?
Google Cloud Vertex AI is Google's unified AI platform for building, deploying, and governing production AI agents and machine learning models. It consolidates the full Google Cloud AI stack — foundation models via the Model Garden, conversational agent development via Agent Builder, multi-step reasoning via the Reasoning Engine, enterprise search and RAG via Vertex AI Search, and structured ML via BigQuery ML — into a single governed environment.
At the core of Vertex AI is Gemini 2.0 — Google's frontier multimodal model family, available as Flash (fast, cost-efficient) and Pro (highest capability). Gemini 2.0 supports a 1-million-token context window, native tool use, and multi-modal inputs including text, images, audio, video, and code. For enterprise deployments, Gemini is accessed through Vertex AI — not the public Gemini API — to get full GCP security and compliance controls.
Vertex AI Agent Builder provides a managed platform for creating production AI agents with Gemini reasoning, data store grounding, Dialogflow CX conversation management, and API tool calling. Reasoning Engine extends this for code-first, complex orchestration using LangChain, LlamaIndex, or custom frameworks — deployed in managed, scalable infrastructure with full observability. Together, they form the agent development layer of the Vertex AI platform.
Enterprise security is handled natively: VPC Service Controls prevent data exfiltration; IAM and CMEK provide access control and encryption management; Cloud Audit Logs record every API call; and Data Loss Prevention scans inputs and outputs. Vertex AI supports HIPAA, SOC 2 Type II, ISO 27001, PCI DSS, and FedRAMP High — making it the right foundation for regulated enterprises.
Services
Our Vertex AI Services
From first agent to full GCP AI rollout — we cover every phase of the build.
Free · No commitment
Not sure which Vertex AI service fits your situation?
Book a free 30-minute discovery call. We'll review your GCP environment, identify your highest-ROI Vertex AI opportunities, and give you an honest architecture recommendation — no sales pitch.
Use Cases
What We Build on Vertex AI
Categories
BigQuery Intelligent Agent
Natural-language analytics over your BigQuery data warehouse — query generation, result interpretation, anomaly surfacing, and scheduled insight delivery to your team in Slack or email.
Document AI Pipeline
Automated extraction and classification of invoices, contracts, and compliance documents using Document AI and Gemini — structured output to your ERP or data lake.
Enterprise Search Agent
Cross-repository enterprise search built on Vertex AI Search — unified search across GCS, BigQuery, databases, and APIs with semantic ranking and access control.
Results
Vertex AI in Production
Real GCP deployments. Real business outcomes.
340-person global retailer · e-commerce platform · 2.4M annual transactions
Problem
Generic product recommendations driving low conversion — no real-time personalisation, no user-level signal capture, 3.1% recommendation click-through rate.
What we built
Personalisation AI agent on Vertex AI using Gemini embeddings and real-time Bigtable signal store — personalised recommendations across web, app, and email, grounded in live inventory.
Investment bank · credit operations · 480 analysts across 6 global offices
Problem
Credit memo review averaging 14 hours per deal — analysts manually extracting financial data from unstructured documents, high error rate on long-form due diligence.
What we built
Document intelligence pipeline using Document AI and Gemini 2.0 — automated extraction of financial metrics, covenant checks, and risk flags from credit memos into structured analyst dashboards.
Global streaming platform · 85M subscribers · 240,000 hours of content
Problem
Manual content tagging taking 6 hours per title — inconsistent taxonomy, poor content discovery, 22% of catalogue undiscoverable via search.
What we built
Content tagging and search pipeline using Vertex AI Search and Gemini Vision — automated multi-label classification, semantic enrichment, and real-time search index updates across the full catalogue.
Playbook
The Vertex AI Playbook
Implementation guides, architecture patterns, and field notes from real Vertex AI deployments — written by the engineers who build them.
Resources
Free Vertex AI Resources
Download and keep. No spam.
The Vertex AI Readiness Guide
Is your organisation ready to build AI agents on Google Cloud? This guide covers the 5 readiness pillars — GCP environment, data estate, use case prioritisation, security posture, and team capability — with a self-assessment scorecard and Vertex AI stack selection framework.
GCP AI Agent Architecture Whitepaper
A technical deep dive into the Vertex AI agent stack: Model Garden, Agent Builder vs Reasoning Engine, Vertex AI Search RAG pipelines, BigQuery ML integration patterns, VPC Service Controls configuration, and phased production rollout framework.
Why Us
Why Kovil AI for Vertex AI?
Multi-cloud AI engineering, GCP-native expertise
We hold GCP certifications and have shipped production Vertex AI agents across Gemini, BigQuery ML, and Document AI. We understand VPC Service Controls, IAM, CMEK, and how Vertex AI integrates with the rest of your GCP data estate — not just the LLM layer.
Fixed-price sprints. No GCP surprises.
Every engagement is scoped, priced, and milestone-gated before we write a single line of code. You know exactly what you're getting, when you'll get it, and what it costs — including a detailed GCP infrastructure cost model for the first 12 months.
2-week risk-free pilot
We scope a single high-impact Vertex AI agent, build it to production standards, and deploy it in 2 weeks. If it doesn't hit your agreed success metrics, you pay nothing. Over 90% of our Vertex AI pilots convert to full engagements.
GCP security from day one
Every build includes VPC Service Controls, IAM least-privilege bindings, CMEK encryption, audit logging, and Data Loss Prevention — configured from the first sprint. Security is not an afterthought or a post-launch retrofit.
Built for enterprise. Trusted by regulated industries.
GCP certified engineers
Hands-on Vertex AI expertise
BigQuery + Vertex AI specialists
Production MLOps
Gemini 2.0 integrations
Enterprise RAG pipelines
30-day post-launch support
Included in every build
FAQ