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Capilano AIby Quanteroun Solutions

Document AI on Google Cloud

Create reliable document-to-data workflows with Google Cloud Document AI and Gemini

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

Best suited for
Google Cloud teams processing forms, invoices, contracts, IDs, reports, composite files, or scanned records
Engagement shape
Document assessment, processor benchmark, workflow pilot, and production rollout
Google Cloud platform

Cloud-specific delivery

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

Google Cloud Document AIVertex AI and GeminiCloud Storage and BigQuery

The service in practice

Start with the work that needs to improve

Document processing creates value only when reliable data reaches the next business step. Google Cloud Document AI provides the extraction foundation; the complete solution also needs classification, validation, exception handling, integration, security, and measurable accuracy.

Capilano AI designs the full Google Cloud document workflow around your actual files and downstream decisions. The result is an operable intake system, not an OCR demonstration.

Why this work matters

Replace operational friction with a service your team can trust

What is getting in the way

Documents vary more than the sample set

Scans, photographs, handwriting, multi-document packages, changed layouts, and missing fields expose brittle extraction rules.

Accuracy is discussed but not defined

A single aggregate score hides which fields, document types, or exceptions create financial and operational risk.

Extracted data stops at a spreadsheet

Without workflow integration, validation, review queues, and ownership, automation simply moves manual work to a different screen.

What the engagement should change

A measured extraction baseline

Representative documents, field-level metrics, confidence thresholds, and an evidence-backed choice of Google Cloud Document AI processors or models.

Exceptions routed to the right person

Low-confidence or policy-sensitive records enter a structured review flow with the source evidence preserved.

Validated data reaches the system of record

Gemini, Vertex AI, Cloud Storage, BigQuery, Workflows, and Cloud Run connect approved outputs to the ERP, CRM, case platform, archive, warehouse, or operational API.

Document AI sample

See extraction, confidence, and review together

This interactive demonstration uses invented sample data. It does not upload a file, call a production model, or represent a client result.

Vendor

North Shore Fixtures

Invoice

NSF-1048

Total

$4,286.00

Due date

2026-09-15

Human review requested

Line 3 tax code · 72% confidence

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.

01Google Cloud Document AI

OCR, split, classify, parse layout, and extract entities with prebuilt or custom processors.

02Vertex AI and Gemini

Validate, normalize, summarize, enrich, and reason over multimodal document content.

03Cloud Storage and BigQuery

Store source files, extracted metadata, review outcomes, and analytics-ready records.

04Workflows, Cloud Run, and Cloud Logging

Orchestrate processing, exceptions, integrations, traces, alerts, and operational ownership.

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.

Composite document classification

Split multi-document files, classify each record, and route it to the appropriate processor.

Custom field extraction

Use custom extractors for industry-specific forms, reports, and records with measured accuracy.

Document-to-data workflows

Validate outputs with Gemini, write trusted records to BigQuery, and surface exceptions for review.

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.

Document taxonomy and exception analysis

Prebuilt versus custom processor benchmark

Extraction schema and confidence thresholds

Gemini validation and enrichment workflow

Human review and system-of-record integration

Accuracy, latency, cost, and drift monitoring

Questions to resolve early

Frequently asked questions

How do you estimate document-processing accuracy?

We create a representative, approved test set and score the fields and document classes that matter to the downstream decision. We report errors by type instead of relying on one blended number.

Do you use prebuilt or custom extraction models?

We benchmark the simplest viable option first. Custom models are justified when document variation, field requirements, language, or accuracy targets cannot be met reliably with prebuilt processors and deterministic validation.

What happens when confidence is low?

The workflow can request missing information, apply deterministic checks, compare trusted records, or route the item to a human review queue. The threshold depends on the consequence of an incorrect field.

Can this connect to our existing business systems?

Yes. Integration is part of the design. We map identifiers, validation rules, API constraints, retries, audit evidence, and ownership before posting data to a system of record.

Apply it in context

Connect the service to an operating job

Use case

Intelligent document processing

Classify files, extract structured fields, validate business rules, and route exceptions across Azure, AWS, or Google Cloud.

Industry

Document-heavy operations

Turn invoices, forms, reports, images, and case files into validated data and controlled business workflows across Azure, AWS, or Google Cloud.

Field notes

Read the implementation detail

All insights

Related services

Document AI

Classify documents and extract validated, structured information from invoices, forms, reports, images, and industry-specific records.

Document AI on Microsoft Azure

Build production document workflows on Azure with Document Intelligence, Microsoft Foundry agents, AI Search, Fabric, APIs, confidence controls, and human review.

Document AI on AWS

Build production document workflows on AWS with Amazon Textract, Bedrock, S3, Lambda, Step Functions, validation rules, human review, and monitoring.

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.