Choose the document family before the extraction tool
Within our AI services for businesses, document processing is most useful where repeated data entry creates an operational bottleneck. Supplier invoices, expense receipts, order forms and delivery notes require different fields and approval rules. An initial pilot therefore focuses on one document family, with its volume, source channel and review effort made explicit.
For example, a purchasing team receiving PDF invoices by email may need an order reference and currency as much as the total. A system that extracts a plausible amount but attaches it to the wrong supplier has not solved the problem. Where a source already provides structured data, direct integration is evaluated first. Reading an image should not introduce uncertainty into data that is already available in a usable form.
Invoices and receipts
Define dates, references, line items and amounts. Tax treatment and payment decisions remain governed by the controls your authorised team sets.
Operational forms
Extract specific fields from service, intake or delivery forms. Missing information and difficult handwriting need a deliberate exception path.
Contracts and attachments
Collect bounded facts such as dates and identifiers. Data extraction does not constitute legal interpretation or approval of contractual terms.
Define the fields and acceptance rules first
The data connected to an automation workflow needs a clear schema from the outset. Each field receives a type, required status, source reference and failure behaviour. Leaving an uncertain invoice number empty and requesting review can be more useful than letting a model complete it with a plausible value.
The evaluation set should include more than clean samples. Different supplier layouts, multiple pages, weak scans and repeat submissions expose problems that a polished demo hides. Extracted values are compared with an approved reference. Rather than relying on one document-level accuracy percentage, your team can see which critical fields require correction and which are reliable enough for the proposed workflow.
| Field | Illustrative check | Exception handling |
|---|---|---|
| Invoice identifier | Compare supplier and invoice reference together | Hold suspected duplicates before creating a record |
| Amount and currency | Check line totals and currency interpretation | Route discrepancies to authorised review |
| Purchase order | Look for the corresponding order record | Keep unmatched documents in a visible queue |
FROM READING TO A NEXT STEP
Map one document workflow
Tell us which files arrive, where the data should go and which fields demand the most checking. We will define a practical pilot and approval boundary.
Make human review a usable part of the operation
Helping teams use AI includes showing reviewers what they are correcting and what their approval triggers. The source document and extracted values should be visible together. A reviewer needs the specific uncertain field and the reason for the check, rather than a generic warning covering the whole file.
Assign responsibilities before rollout: who checks the document, who releases it to accounting and who requests a clearer copy from a supplier? Without an owner, an automated review queue can become another growing inbox. Corrections should be recorded so repeated error patterns inform improvements. High-risk financial fields can have stricter review rules than descriptive text. A model's confidence signal is one input to this decision; your business rules and reference checks still matter.
Test accounting delivery independently from extraction
Workflow integration must carry the approved information into the correct record. We inspect the destination system's API or import capability, permissions and validation rules. Successful extraction does not prove successful posting: the target record identifier and delivery outcome must also be observable.
Permission to enter data is separated from permission to approve or initiate payments. Producing a draft record may be the right first release. Acceptance tests cover interrupted connections, repeat processing and a partially completed transfer. The goal is to resume safely without silently losing a document or generating a duplicate. Connectivity to a named accounting package is confirmed after technical review, rather than assumed from a product logo.
Receive and identify
Record the source and document identity, then select the appropriate field schema.
Extract and validate
Read the fields, apply agreed checks and mark information that requires review.
Approve and deliver
Create the destination record after the appropriate decision and retain its reference.
Handle delivery failures
Notify an assigned owner, define bounded retries and document the manual fallback.
Map access, copies and retention before live documents
A business AI solution should have an understandable data path. We discuss which services receive a file, who can access it, which copies are stored and when they are removed. Sample documents should omit unnecessary personal or commercially sensitive information wherever practical.
Cloud and company infrastructure are assessed through more than installation cost. Provider terms, processing volume, access policies and maintenance capacity influence the choice. A locally hosted workflow may still call an external model, so the actual service connections need inspection. Moving historical archives, cleaning old records and defining retention policies are separate deliverables when required. These boundaries prevent an extraction pilot from becoming an undefined migration of every document the business owns.
Measure the total work per document
Involving the operations team helps a pilot reflect daily work rather than a laboratory demonstration. Establish the baseline for opening, entering, checking and correcting each document. Compare the human effort after implementation as well as the machine processing time.
A release decision considers critical-field quality, review queue size, failed transfers and reviewer usability. Costs include document pages, model usage, infrastructure, integration work and maintenance. A new supplier format or document family may require fresh testing. For discovery, tell us the document type, approximate volume, target system and required fields. We can then agree a secure sample-sharing method and a bounded pilot with clear acceptance criteria. Expanding the scope should follow evidence from that pilot rather than an assumption that every document will behave alike.
BEFORE YOU DECIDE
Frequently asked questions
Is OCR the same as AI document processing?
OCR converts visible text into machine-readable text. This service also considers field extraction, business checks, approval and delivery into another system. The appropriate combination depends on the documents.
Can it connect to any accounting package?
API, import and permission constraints need review before we commit. A controlled file export may be the best starting point, while direct integration is scoped separately.
What happens with handwriting or poor scans?
They are included in evaluation samples where relevant. Readability affects the result, and uncertain fields should go to review rather than silently entering the accounting system.
Does this include issuing official invoices?
Extracting data and issuing an official invoice are different workflows. Requirements for your existing system and invoicing provider are assessed separately; payment authority is not implied.
How do you assess accuracy?
We compare critical fields with approved reference values and record corrections and delivery outcomes. A single overall percentage is insufficient for deciding whether a financial workflow is ready.
What should we prepare for discovery?
Prepare document types, approximate monthly volume, your destination system and required fields. Representative samples are reviewed after agreeing the appropriate sharing method and access boundaries.
LET’S DEFINE THE SCOPE
Map one document workflow
Tell us which files arrive, where the data should go and which fields demand the most checking. We will define a practical pilot and approval boundary.
Request document workflow discovery