Move document data into a review queue before it reaches your systems.
Build AI document processing workflows for field extraction, validation, human review and approved exports from invoices, forms and business documents.
Our approach to AI Document Processing & Workflow Automation
Document processing work starts with the fields your team needs and the checks that make them usable. We build a scoped intake, extraction and review workflow for one document family, such as supplier invoices or structured business forms.
Extracted text and model output are treated as untrusted input. The workflow validates required fields, highlights exceptions and asks an operator to approve the result before an export or downstream write. This is a custom development service; existing finance records in KasaFlow do not establish an implemented OCR or invoice-extraction product.
When to choose this service
- Supplier invoice data prepared for bookkeeper review
- Business-form extraction with missing-field checks
- Document classification and approved record exports
Project success criteria
We agree measurable goals and acceptance checks during discovery. Outcomes depend on the scope, starting point and how the solution is used.
Quality
Representative acceptance tests
How we review this
Control
Human review and recovery
How we review this
Ownership
Documented handover
How we review this
What You Get
- One document family and agreed output field schemaAcceptance checkReview “One document family and agreed output field schema” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Validated intake and selected OCR or model extractionAcceptance checkReview “Validated intake and selected OCR or model extraction” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Required-field, format and duplicate-review checksAcceptance checkReview “Required-field, format and duplicate-review checks” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Human review, correction and exception queueAcceptance checkReview “Human review, correction and exception queue” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Approved export or one separately verified integrationAcceptance checkReview “Approved export or one separately verified integration” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
- Representative-document evaluation and privacy/retention handoverAcceptance checkReview “Representative-document evaluation and privacy/retention handover” in the agreed user journey with representative data. Confirm who owns it, which exceptions must be handled and what evidence is needed for acceptance.
How We Execute
- 1Review authorized samples and define fields and exceptionsReview this stepFor “Review authorized samples and define fields and exceptions”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 2Evaluate extraction against an agreed sample setReview this stepFor “Evaluate extraction against an agreed sample set”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 3Build validation, correction and approval before exportReview this stepFor “Build validation, correction and approval before export”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
- 4Test failure cases and document the operating responsibilitiesReview this stepFor “Test failure cases and document the operating responsibilities”, agree the input, responsible person, expected output and the decision required before moving forward. Keep exceptions and remaining work visible in the project review.
Next Step
Discuss your document workflow
Share your goals and current setup. We will give you a clear scope, timeline, and success criteria before build starts.
AI Document Processing & Workflow Automation: common questions
Is invoice extraction already included in KasaFlow?
KasaFlow demonstrates finance records, allocations and reconciliation. OCR and AI invoice extraction are a separate custom implementation; they are not attributed to the existing foundation.
Can extracted data be posted automatically?
We start with review and approved exports. Any downstream write requires agreed validation, access permissions, idempotency and recovery behavior. Financial or other consequential decisions remain with the responsible operator.
Which document formats and languages are supported?
The scope defines supported formats, languages and layouts using representative samples. Scanned pages, handwriting, tables and poor image quality can change accuracy and delivery effort; they are evaluated before a quote is finalized.
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Your next step
Ready to plan your project with BrainGlow?
Get a concrete execution plan and timeline matched to your growth goals.
Choose a workflow with a clear owner and a recoverable failure path
Bring a process example, the tools involved, and the point where work stalls. Start with one trigger and one expected result. Identify which decisions can follow fixed rules, which need interpretation, and which require a person to approve the next action.
- Document access requirements and the system that owns each field.
- Test duplicate inputs, invalid data, and unavailable integrations.
- Agree on a manual fallback, monitoring owner, and a baseline before expanding.
Review related delivered projects to assess fit. Timeline and outcomes depend on scope, data quality, and third-party access; define acceptance criteria during discovery.