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Capilano AIby Quanteroun Solutions

AI Modernization on Google Cloud

Build governed Gemini agents and data-grounded workflows on Google Cloud

Modernize AI and automation on Google Cloud with Vertex AI, Agent Development Kit, Agent Engine, Gemini, Document AI, BigQuery, and production-ready serverless delivery.

Best suited for
Google Cloud and Workspace estates building governed Gemini agents, document workflows, and data-grounded applications
Engagement shape
Use-case assessment, target architecture, pilot, migration, and production rollout
Google Cloud platform

Cloud-specific delivery

Architecture, implementation, evaluation, and operations designed for the platform you already run.

Vertex AI and GeminiADK and Vertex AI Agent EngineVertex AI RAG and Search

The service in practice

Start with the work that needs to improve

Google Cloud AI modernization is not a model-shopping exercise. It is the work of turning a useful experiment into a secure service with dependable data, controlled access, measurable quality, and an operating owner.

Capilano AI helps teams use Vertex AI, Gemini, Agent Development Kit, Agent Engine, Document AI, BigQuery, and Cloud Run to modernize a bounded workflow first, then establish the architecture and delivery practices needed to extend it responsibly across the business.

Why this work matters

Replace operational friction with a service your team can trust

What is getting in the way

Promising pilots with no production path

Prototypes may demonstrate model capability without resolving identity, integration, evaluation, deployment, cost, or support.

A fragmented Google Cloud, Workspace, BigQuery, and serverless estate

Data, documents, automations, and business logic sit in separate services with inconsistent ownership and controls.

Platform choices made before the workflow

Teams accumulate overlapping services because the operating outcome, evidence standard, and human role were never made explicit.

What the engagement should change

A defensible target architecture

A practical design for Gemini and Vertex AI agents, Workspace grounding, document workflows, BigQuery data, and Cloud Run operations, grounded in your existing cloud, identity, data, and integration boundaries.

One use case moved into production

A focused workflow delivered with evaluation, approval, telemetry, release controls, and a clear handoff to operations.

A modernization sequence leaders can fund

Dependencies, risks, platform decisions, and follow-on opportunities organized into a phased roadmap rather than an unbounded transformation program.

Platform capabilities

Use the cloud as a delivery system, not a model catalogue

We select managed services around the workflow, identity model, data boundaries, quality target, and operating responsibility.

01Vertex AI and Gemini

Select models, build multimodal applications, evaluate quality, and govern production endpoints.

02ADK and Vertex AI Agent Engine

Develop, deploy, scale, and observe agents with tools, sessions, memory, and managed runtime services.

03Vertex AI RAG and Search

Ground answers in enterprise content with managed ingestion, retrieval, citations, and quality testing.

04Google Cloud Document AI

OCR, split, classify, parse, and extract structured data with prebuilt or custom processors.

05BigQuery and Cloud Storage

Connect governed analytical, operational, and unstructured data to AI applications.

06Cloud Run, Workflows, and Cloud Logging

Operate serverless integrations with identity, orchestration, tracing, alerting, and audit.

Where to apply it

Start with a bounded operating outcome

Each use case is scoped with data access, integration, evaluation, human approval, monitoring, and ownership from the beginning.

Build and deploy a custom agent

Use ADK and Agent Engine for a tool-enabled workflow with managed runtime, evaluation, and observability.

Automate document-heavy operations

Combine Document AI processors, Gemini review, Workflows, and human exception queues.

Ground an agent in Workspace and BigQuery

Create permission-aware retrieval across company documents and governed business data.

Modernize a prototype into Cloud Run

Move ad hoc agent code into versioned serverless services with CI/CD, IAM, logging, and runbooks.

How we deliver

Evidence before scale. Ownership before launch.

The method is intentionally practical: reduce uncertainty early, build the full operating path, and leave the service with people who can run it.

See our delivery approach
  1. 01

    Define the operating outcome

    Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.

  2. 02

    Prove the difficult assumptions

    Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.

  3. 03

    Build the complete service

    Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.

  4. 04

    Release with an owner

    Roll out in controlled stages, train the operating team, review production evidence, and convert confirmed failures into improvements.

Typical engagement

What your team receives

The exact scope follows the operating outcome and current environment. These are the core work products typically required to make the result useful and supportable.

AI use-case, workload, and data-readiness assessment

Vertex AI, Gemini, ADK, and Agent Engine architecture

RAG, search, BigQuery, and Workspace grounding design

Cloud Run, Workflows, Pub/Sub, and API integration

Evaluation, trace, safety, latency, and cost baselines

Infrastructure as code, rollout plan, monitoring, and runbooks

Questions to resolve early

Frequently asked questions

Do we need to move everything to Google Cloud?

No. We start with the systems and constraints you already have. The target design can keep selected data or applications outside Google Cloud when that is the safer or more economical choice.

Can you modernize an existing pilot or low-code automation?

Yes. We review its workflow, prompts, tools, data access, failure modes, deployment method, and ownership, then decide what can be retained and what should be redesigned.

How do you choose the first use case?

We look for repeatable work with a measurable baseline, accessible data, a willing process owner, and a safe way for people to review or take over when needed.

What makes the result production-ready?

Production readiness includes identity, data protection, tested integrations, representative evaluations, telemetry, cost controls, release gates, incident handling, documentation, and an accountable owner.

Apply it in context

Connect the service to an operating job

Industry

Film & media

Organize production knowledge, extract metadata from operational documents, accelerate asset discovery, and coordinate repeatable production-office workflows with rights and approvals preserved.

Field notes

Read the implementation detail

All insights

Related services

AI Modernization on Microsoft Azure

Modernize AI, automation, and knowledge workflows on Microsoft Azure with Foundry agents, Azure AI Search, Document Intelligence, Fabric, Copilot Studio, and production controls.

AI Modernization on AWS

Modernize AI and automation on AWS with Amazon Bedrock agents and knowledge bases, Textract, serverless workflows, secure data foundations, and production observability.

Document AI on Google Cloud

Build production document workflows on Google Cloud with Document AI processors, Gemini, Vertex AI, Cloud Storage, BigQuery, Workflows, validation, and human review.

Bring us the workflow—not a finished AI specification

We will help clarify the operating outcome, difficult assumptions, delivery path, and evidence required for a responsible investment decision.