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Capilano AIby Quanteroun Solutions

Document AI

Turn incoming documents into validated data and accountable business actions

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

Best suited for
Teams processing high volumes of forms, reports, invoices, images, or records
Engagement shape
Extraction proof of value through production workflow
Document AI

The service in practice

Start with the work that needs to improve

Document AI is more than OCR. The real operating problem is deciding what arrived, extracting the fields that matter, verifying them against business rules, handling exceptions, and moving trusted information into the next system.

Capilano AI builds intelligent document-processing workflows for invoices, forms, reports, contracts, images, and industry records. We benchmark on representative files and make human review, evidence, monitoring, and recovery part of the architecture.

Why this work matters

Replace operational friction with a service your team can trust

What is getting in the way

Real documents break clean demos

Scans, photographs, tables, handwriting, changed templates, and composite files expose assumptions made on a small sample.

Not every field carries the same risk

A wrong identifier, amount, date, or legal term can matter far more than a cosmetic extraction error.

Manual verification remains invisible

Teams may automate extraction but still spend hours comparing source documents, correcting fields, and rekeying results.

What the engagement should change

A defensible extraction benchmark

Field-level and document-level quality measured on an approved, representative test set.

A controlled exception workflow

Confidence thresholds, deterministic checks, review queues, source evidence, and clear ownership.

Trusted data in the next system

Validated outputs integrated with operational applications, archives, analytics, and audit requirements.

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

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.

Invoice and purchase-order processing

Extract line items, validate vendors and totals, route mismatches, and prepare approved postings.

Forms and application intake

Classify submissions, detect missing information, structure fields, and create the next case or task.

Contracts and case files

Identify document types, obligations, dates, entities, and evidence while preserving review context.

Imaging and technical reports

Combine OCR, layout, visual evidence, extraction, and expert review for complex records.

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 inventory and exception analysis

OCR, layout, classification, and extraction pipeline

Prebuilt versus custom model selection

Confidence thresholds and review queues

Business-system and archive integration

Accuracy evaluation and operational monitoring

Questions to resolve early

Frequently asked questions

Which document AI platform do you recommend?

We decide after reviewing document types, cloud constraints, languages, extraction targets, expected volume, integration needs, regional requirements, and an actual benchmark.

How many sample documents are required?

Enough to represent meaningful layout, quality, source, and exception variation. The right sample is determined by the diversity and risk of the workflow, not an arbitrary page count.

Can people review uncertain fields?

Yes. We design queues that show the source evidence, extracted value, confidence, validation result, and required action so review remains efficient and auditable.

How do you monitor quality after launch?

We track extraction and validation outcomes, review rates, error categories, document drift, processing failures, latency, and cost. Confirmed failures become regression examples.

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

Real estate

Qualify inquiries, organize property and transaction documents, give teams grounded answers, and keep follow-up moving across brokerage, property-management, and development workflows.

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.

Anonymized delivery pattern

A controlled document-intake pattern for imaging operations

A representative administrative workflow for classifying incoming referral and operational documents, extracting routing metadata, and directing uncertain items to a trained team member.

Field notes

Read the implementation detail

All insights

Related services

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.

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.