IntelligenceShow us your workflowDocument processing automation · St. Louis + nationwide
Aule Intelligence builds document workflows that collect the right files, check required information and put exceptions in front of the right person. Stop treating an uploaded file as completed work.
Based in St. Louis · Remote delivery across the United States
On-site sessions and travel are scoped with your team.
Try the review process
A document workflow should hold a questionable record, show the source and explain the required correction. This demonstration deliberately includes missing, conflicting and ambiguous fields. Compare each prepared extraction with its supporting records, resolve the exceptions and see why acceptance must happen before the record moves into another system.
Compare the source with a deliberately imperfect extraction. Resolve each hold, then approve the example. No upload, OCR, AI model, payment or external system is connected.
01 / Inspect the source
Approved PO-204: Sample Operations LLC; equipment maintenance; $1,296 total including tax. No payment instruction changes are included.
02 / Resolve the exceptions
$1,286.00
The prepared extraction differs from the printed total and the line-item sum: $1,200 + $96 = $1,296.
PO-204 · unmatched
A purchase-order number alone does not confirm that the supplier, scope and amount match the approved purchase.
Not captured
The required due date is visible in the source but missing from the prepared extraction.
The record stays on hold until every required check is resolved.
What you receive
Aule scopes the complete path from document arrival to an accepted business record. Deliverables include an intake design, field definitions, validation rules, a reviewer queue and supported connections to your existing tools. The scope also defines who handles exceptions and how corrected documents, rejected submissions and failed transfers are recorded.
Choose the first document
Start with a recurring document type that has a known owner, a clear destination and repeatable acceptance rules. Supplier invoices, purchase orders and client intake packets are useful candidates. Aule evaluates actual samples and exceptions before deciding whether extraction, a better form or simpler process rules would solve the problem.
Bring normal examples and troublesome ones: a revised invoice, a statement from the wrong month, an unreadable scan, an unfamiliar layout and a duplicate submission. Those examples help establish what the process must do when the easy path fails.
For client collection and progress tracking, see our accounting client onboarding workflow. For handoffs across many document types and teams, start with business process automation.
A poor starting point: no agreed acceptance criteria, no reviewer capacity or documents whose correctness requires unavailable evidence. We would resolve those gaps before automating acceptance.
Connect the surrounding work
We identify where documents arrive, where originals belong and which system owns the final record. Supported integration methods, account permissions and destination field requirements determine the design. Aule confirms those details before promising a connection, including what happens when access expires, a record already exists or a destination rejects an update.
For Microsoft environments, Microsoft’s AI Builder documentation describes document processing models used in Power Automate flows and Power Apps. Platform choice depends on the files, licensing, review requirements and existing environment; this page’s demonstration is not an AI Builder deployment.
A typical scope examines a mailbox or portal, a document store and a CRM, accounting or operating application. Our systems integration service covers the record ownership and connection design around that workflow.
From samples to supervised use
We start by defining accepted documents and collecting representative examples, then build the smallest complete workflow. Testing covers each required field, routing decision and transfer failure. A supervised pilot compares reviewer effort, exception rates and completed records with a baseline before the team agrees whether to expand the process.
Before we begin
No. It contains prepared synthetic records and deliberately imperfect extracted values. Every action runs in the browser. It illustrates validation and reviewer decisions, not measured model accuracy or an active integration.
The agreed process determines which rules can permit a handoff and which decisions require a person. We do not assume a confidence score proves correctness or that accepting a document authorizes payment, accounting treatment or professional advice.
A return-and-resubmit process can be included. It should identify the exact missing or incorrect item, preserve the earlier submission and prevent reminders from continuing after the right document has been accepted.
No. Aule is based in the St. Louis area and can deliver this service remotely for U.S. businesses. Discovery confirms access, team availability and whether any onsite work is needed.
Find the right starting point
See client onboarding automation for a firm-wide view of document collection, review and client progress, or home service automation for the documents needed before a completed job can be invoiced.
Explore the Chimney Atlas project for another example of turning information into an operating interface. It demonstrates a different workflow and is not evidence of document extraction accuracy.
A practical first conversation
Bring a few redacted examples, the checklist your reviewer uses and the place accepted information needs to go. We will identify the checks worth automating, the decisions that need a person and the smallest useful workflow to test with your team first.
Map your document workflow