From PDF to Exact Online with AI: Extract Data
Finance teams often receive invoices, orders and reports as PDF files. Then, someone must read each file, copy values and enter them into Exact Online. This process consumes time and creates avoidable errors.
AI now connects document reading with accounting workflows. It can read a PDF, understand its layout, check important values and prepare a record for review. As a result, companies can reduce repetitive work while keeping financial controls in place.
According to a 2023 finance automation report, businesses can cut manual entry time by 70% to 90% with AI-based document processing. Another study reports that assisted entry can reduce error rates from 1%–4% to below 0.5%.
1. PDF AI: What AI Can Extract from a PDF
A PDF can look simple to a person. However, its internal content may be unstructured or semi-structured. Text can sit inside separate boxes, tables or graphical layers. Therefore, a system must understand position as well as words.
Digital PDFs usually contain selectable text. A user can search, copy or edit that content. In contrast, scanned PDFs contain a picture of a page. The computer sees pixels rather than characters. In this case, optical character recognition, or OCR, helps extract text from scanned documents.
OCR software analyses characters and converts them into machine-readable text. Yet, ordinary OCR does not always understand meaning. It might read a supplier name correctly but confuse an invoice number with a customer reference.
An AI extract service adds context. It can identify and extract the supplier, invoice number, date, VAT amount, total and payment terms. It can also distinguish a billing address from a delivery address. This difference matters when businesses process invoices, purchase orders and financial reports.
For example, an invoice may show “Total,” “Net amount” and “VAT” in different positions. A purchase order may place the supplier code at the top and item quantities in a long table. A report may use several pages and subtotals. AI can learn these patterns without requiring one fixed template.
The result depends on the source. Clear digital files usually produce better results than image-based files. Poor contrast, rotated pages and handwritten notes can reduce accuracy. Still, modern systems can read many layouts and return useful values for review.
Businesses can also use OCR order processing when documents arrive through email. This approach connects reading with the next business action, instead of stopping after text recognition.
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2. Document AI and Data Extraction: Turning Documents into Usable Data
Document AI combines OCR, language models and layout analysis. Together, these technologies help a system understand what each section means. The system can parse headings, tables, line items and notes.
For example, an AI tool can identify the supplier at the top of a page. It can then connect each product description with a quantity, unit price and tax code. This process supports document data extraction across many invoice layouts.
A specialist service can extract data from PDFs and return structured data for another system. Common formats include JSON, XML, CSV and RTF. A developer can also export the result through an API or an SDK.
JSON can contain fields such as supplier name, invoice date, currency and total. Line items can include product codes, descriptions, quantities and prices. As a result, Exact Online receives consistent values rather than a block of copied text.
Validation adds another layer of control. The system can check whether a date follows an accepted format. It can compare line totals with the invoice total. It can verify the VAT calculation and match supplier details against an approved record.
When a value looks uncertain, the system should flag it. For example, it might mark a blurred invoice number for human review. It can also flag a missing VAT value or a total that does not match the line items.
This approach lets teams extract structured data without trusting every result blindly. It also creates a clear boundary between automatic preparation and financial approval. That boundary supports safer document workflows.
virtualworkforce.ai uses AI agents to extract structured data from email attachments and other business documents. The platform can then place values into ERP or operational systems, while employees review exceptions. This suits teams that need document entry and email handling in one process.
3. AI-Powered PDF Processing: Choosing the Right AI PDF Tool
A basic PDF AI tool can search a file, extract text or create a summary. Tools such as Adobe Acrobat can help users find information quickly. Acrobat can also support document review when a person needs to upload a PDF and inspect its contents.
However, a general tool may not understand accounting controls. A specialist invoice service can perform field extraction, table extraction and supplier matching. A broader intelligent document processing platform can classify documents and send them through approval steps.
Key AI features include:
- Field and table extraction from different layouts.
- Document classification for invoices, orders and reports.
- Summaries that explain the main values and exceptions.
- Data validation for totals, VAT and dates.
- Confidence scores for uncertain fields.
- Human approval before posting.
Businesses should compare accuracy, supported languages and integration options. They should also check security controls, price, API access and data retention. Support for multiple languages matters when suppliers operate across countries.
General extraction tools can suit occasional tasks. Specialist platforms work better when a team processes high volumes every week. A company may also need a solution that connects email, ERP records and approval rules.
Do not accept claimed accuracy or cost savings without testing real files. Build a test set with clean digital documents, scanned files, unusual layouts and several suppliers. Then measure field accuracy, review time and rejected records.
One business may process PDFs in seconds but still need people to check every total. Another may achieve a high straight-through rate after supplier matching improves. The correct choice depends on document quality, volume and risk.
For teams that handle purchase orders as well as invoices, extracting purchase order data from PDF to an ERP can extend the same approach beyond accounting.
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4. From PDF to Exact Online: Mapping and Sending Extracted Data
The process starts when an incoming PDF reaches an email inbox or upload folder. The system reads the file, classifies it and extracts the required values. Next, a reviewer checks uncertain fields and approves the proposed record.
PDF AI output must then map to Exact Online fields. Supplier information may connect to an existing account. Invoice headers can include invoice number, date, currency and payment terms. Invoice lines may require a product code, description, quantity, price and VAT code.
Ledger accounts need careful mapping. A supplier record does not always reveal the correct expense account. Therefore, rules can use supplier history, purchase orders, product groups or department codes.
The integration can use an API or middleware. After approval, it can create a purchase invoice in Exact Online and attach the original PDF. OAuth 2.0 can protect access while limiting the actions available to the integration.
Error handling matters at every stage. The service should record failed requests, retry temporary errors and show clear messages. It should also respect API limits. A queue can prevent a large batch from overwhelming the accounting system.
Matching improves control. The system can compare a supplier with existing records. It can match an invoice to a purchase order and check whether a similar invoice already exists. Duplicate detection can compare supplier, invoice number, date and amount.
Approval should happen before automatic posting. A person may need to confirm a new supplier, unusual amount or unexpected VAT code. This control prevents a fast process from creating financial errors.
Companies already using Exact Online may also benefit from Exact Online order entry automation. That workflow can connect customer orders with ERP records and reduce repeated entry across departments.
5. AI Assistant Workflows: Summaries, Checks and Exception Handling
An AI assistant can summarise an invoice and explain its extraction result. For example, it can say that the supplier matched an existing record, while the VAT value needs review. This gives an employee useful context before approval.
A practical workflow begins with an email inbox. The system receives an attachment, identifies its document type and reads the relevant fields. It then checks totals, supplier details, purchase orders and duplicate records.
Approved items move to Exact Online. The original document stays attached to the accounting record. Uncertain items go to a person with the source file, extracted values and reason for escalation.
A small business might use this sequence:
- Receive supplier invoices in a shared mailbox.
- Read the attachment and classify it.
- Check the supplier, VAT and total.
- Send exceptions to the bookkeeper.
- Create approved invoices in Exact Online.
- Store the source document and audit result.
The system can identify duplicate invoices and unusual amounts. It can also detect missing payment terms, changed bank details or a supplier that does not match its history. These signals do not prove fraud. Instead, they help a person focus attention where risk appears higher.
Every correction should create feedback. If a reviewer changes a ledger account, the system can record that decision for future suggestions. Yet, changes should follow governance rules. A correction must not silently alter approved accounting policies.
Audit trails show who reviewed a record, what changed and when posting occurred. They also preserve the original source. This traceability supports audits and helps teams investigate disputes.
virtualworkforce.ai can connect email, documents and ERP actions through human approval. Its agents can route exceptions with relevant context, rather than sending employees back to search several systems.
6. Safe Implementation: Accuracy, Security and the Future of AI PDF Workflows
AI does not remove the need for good source files. Poor scans, unusual layouts and handwritten content can reduce extraction quality. Therefore, test documents from different suppliers before setting production rules.
Use a representative sample. Include clear files, scanned documents, credit notes, multi-page invoices and foreign currencies. Measure processing time, review rates, field accuracy and posting errors. These figures provide a better business case than a vendor’s headline claim.
Invoices can contain names, addresses, bank details and payment information. Under GDPR, a company must know why it processes this information and how long it keeps it. It should also document supplier agreements and processing locations.
Encryption should protect files during transfer and storage. Access controls should limit documents to authorised users. Retention rules should remove temporary files when the business no longer needs them. Teams should also review subcontractors and model providers.
Automation should reduce manual work without removing financial controls. Keep approval rules for new suppliers, high-value invoices and unusual transactions. Also, provide a clear way to correct results and stop automatic posting.
Future systems will support better multilingual extraction, real-time validation and anomaly detection. E-invoicing will reduce the need to read some files, while AI will still help with exceptions and mixed formats. Generative AI may explain records in plain language, but rules and approvals should govern financial actions.
Businesses can also connect order documents with purchase order processing automation. The strongest design combines speed with review, security and clear accountability.
FAQ
Can AI read every PDF?
No. Clear digital files usually produce better results than poor scans or handwritten pages. A review step should handle unclear, incomplete or unusual documents.
What fields can AI capture from an invoice?
Common fields include supplier, invoice number, date, VAT, total, currency and payment terms. Systems can also capture line descriptions, quantities, prices and account suggestions.
Does Exact Online support automated invoice entry?
An integration can prepare and create purchase invoice records through supported connections. The design should respect authentication, permissions, validation rules and API limits.
Should every invoice receive human approval?
Approval depends on risk and business policy. New suppliers, unusual amounts and uncertain values should normally receive human review before posting.
Can the original PDF stay with the Exact Online record?
Yes, an integration can attach the source file to the relevant record when the connection supports attachments. This helps employees and auditors trace the posted values back to the source.
How does AI detect duplicate invoices?
It can compare supplier details, invoice number, date, currency and amount with existing records. It can also identify similar files when a supplier changes formatting.
Is OCR the same as document understanding?
No. OCR reads characters from a page, while document understanding connects text with meaning and position. Therefore, OCR may read a value without knowing which accounting field should receive it.
How can a company test an extraction service?
Use real files from several suppliers and document types. Measure field accuracy, review time, failed matches and posting errors before choosing a production solution.
Is cloud processing safe for financial documents?
It can be safe when the provider offers encryption, access controls, retention policies and suitable GDPR agreements. The buyer should also confirm processing locations and subcontractor responsibilities.
Can the same technology process purchase orders?
Yes. The same approach can read order headers, product lines, delivery details and customer references, then send approved values to an ERP system.
Drowning in emails?
Here’s your way out
Save hours every day as AI Agents label and draft emails directly in Outlook or Gmail, giving your team more time to focus on high-value work.