Documents vary more than the sample set
Scans, photographs, handwriting, multi-document packages, changed layouts, and missing fields expose brittle extraction rules.
Document AI on Microsoft Azure
Build measured document-processing workflows with Azure Document Intelligence and Microsoft Foundry
Build production document workflows on Azure with Document Intelligence, Microsoft Foundry agents, AI Search, Fabric, APIs, confidence controls, and human review.
- Best suited for
- Microsoft teams processing invoices, contracts, forms, reports, images, or industry records
- Engagement shape
- Document assessment, extraction benchmark, workflow pilot, and production rollout
Cloud-specific delivery
Architecture, implementation, evaluation, and operations designed for the platform you already run.
Azure Document IntelligenceMicrosoft Foundry agentsAzure AI Search
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. Azure Document Intelligence provides the extraction foundation; the complete solution also needs classification, validation, exception handling, integration, security, and measurable accuracy.
Capilano AI designs the full Azure 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
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 Azure Document Intelligence 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
Microsoft Foundry, Azure Functions, Logic Apps, Fabric, and APIs 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.
01Azure Document Intelligence
Read layout, tables, key-value pairs, invoices, receipts, contracts, IDs, and custom fields.
02Microsoft Foundry agents
Validate, enrich, reason over, and route extracted information with controlled tools and approvals.
03Azure AI Search
Index documents and extracted metadata for hybrid retrieval, citations, and downstream knowledge workflows.
04Fabric, Functions, and Logic Apps
Land structured results, orchestrate exceptions, and integrate ERP, CRM, case, or archive systems.
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 intake
Extract line items, validate totals and vendors, route mismatches, and post approved records.
Contract and case-file processing
Classify files, extract obligations and dates, preserve evidence, and create review tasks.
Imaging and technical reports
Combine layout, OCR, visual evidence, and agent review for complex operational 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- 01
Define the operating outcome
Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.
- 02
Prove the difficult assumptions
Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.
- 03
Build the complete service
Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.
- 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, composed, or custom model benchmark
Extraction schema and confidence thresholds
Foundry agent 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
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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.