We read data from invoices, contracts, forms and e-mails with AI, validate it and pass it cleanly into your ERP.
In many companies a team still opens incoming invoices, delivery notes, contracts and application forms and types their contents into the ERP or accounting software one by one. The work is slow and draining, and the small errors that come with fatigue turn into large reconciliation problems at month end. Our document and data processing service hands that repetitive reading and typing to an AI-assisted pipeline.
The system we build captures each document from whichever channel it arrives through, recognises its type, extracts the required fields, checks them against your rules and puts anything it is unsure about in front of a person. Approved data flows straight into your ERP or accounting system. The goal is not to remove people from the process but to keep them only where a decision is needed.
- 01Finance teams keying hundreds of supplier invoices into the accounting system by hand every month.
- 02Logistics and purchasing units that have to match delivery notes, orders and proof-of-delivery documents.
- 03Legal and admin teams struggling to track dates, amounts, renewal and penalty clauses across contracts.
- 04Insurance, finance and service firms processing application, enrolment or claim forms from scans.
- 05Operations teams that want requests in incoming e-mail read and routed to the right system and person.
- 06Service providers such as accounting firms that handle many clients’ documents in different formats.
What is intelligent document processing and how is it different from OCR?
Classic OCR turns the letters in an image into text, but it does not know which number is the total and which is the tax ID. That is why older systems need a separate template for each supplier, with coordinate rules such as “the total is in the bottom right corner”. When a supplier changes its invoice layout, the template breaks and the team goes back to manual entry.
Intelligent document processing reads a document the way a person does. Image and text are assessed together, and the AI model recognises concepts such as “invoice date”, “VAT amount” or “payment due” regardless of where they sit. It can often handle an invoice from a supplier it has never seen, with no template. Extra processing steps cover line items in tables, multi-page documents, handwritten notes and poor scans.
The difference is not only reading quality: the system can also say how confident it is about each field. That confidence score decides where human review is directed.
How does AI invoice reading and data extraction work?
The pipeline has four steps. First the document is captured: an attachment arriving in the accounts inbox, a PDF dropped into a shared folder or an image from a scanner. Next it is classified as an invoice, delivery note, contract or irrelevant attachment. In the third step, fields are extracted according to type: supplier name, tax number, date, line items, subtotals, VAT rates and grand total.
The fourth step matters most: validation. Do the line items add up to the total, is the tax number valid, has this invoice been processed before, is there a matching purchase order and delivery note? We derive these rules from your own finance and purchasing practice. A record that passes moves on automatically; one that fails or has a low confidence score lands in the review queue.
The same structure serves contract analysis: parties, start and end dates, renewal terms, penalty and termination clauses are extracted, and key dates become calendar alerts.
Human review and ERP export: how does data enter your systems safely?
The review queue is the heart of this service. The original document sits on one side of the screen and the extracted fields on the other; the doubtful field is highlighted and the reviewer corrects and approves just that one. There is no need to reread the whole document. Every correction is recorded, and the system makes use of those corrections on later documents from the same supplier.
Approved data goes to systems such as Logo, Mikro, Netsis, Paraşüt or SAP Business One through an API or an import file. Steps such as matching the supplier account, assigning cost centres and account codes are also handled by rules. Documents that arrive as e-Fatura already contain structured data, so we route them through a matching flow instead of a reading one.
Every step a document went through, who approved what and which record was created are traceable. During an audit, any record can be traced back to its source document in one click.
Timelines, accuracy and common mistakes in document automation
A project that starts with one document type, such as supplier invoices, usually reaches parallel running within a few weeks. The main factors are the variety of documents, how quickly samples are collected and what import options the target system offers. Each new document type is then added on top of the existing infrastructure with a shorter piece of work.
The most common mistake is aiming for “100 percent automatic” from day one, which leads either to unchecked errors or to a project that never ends. The second is measuring accuracy per document rather than per field, when a document with the right date but the wrong amount is exactly the dangerous case. The third is skipping validation rules. In a good setup, field-level accuracy is reported regularly, the review queue shrinks over time and every automatic record can be traced back to its source.
- 01Document capture from e-mail, folders and scanners
- 02Document classification and field extraction model
- 03Validation layer built on your business rules
- 04Human review queue for uncertain records
- 05Automatic export to ERP and accounting systems
- 06Accuracy and throughput monitoring dashboard
Review queue designed in from the start
We build human review as a core part of the system, not an exception added later. The reviewer’s screen, correction speed and how corrections feed back are designed with as much care as the reading model.
We speak finance and ERP
We run Rocketly CRM ourselves, connected to systems such as Paraşüt, Nebim V3, QuickBooks and e-Fatura. Account matching, VAT breakdowns and duplicate records are part of our daily operations, not theory.
Field-level measurement
We measure accuracy per field rather than as one overall percentage. That way you decide from data, not guesswork, which fields can safely pass automatically and which need review.
Data privacy and retention rules
Where documents are processed, how long they are kept and how personal data is protected are defined at the outset alongside KVKK and GDPR requirements. If needed, processing runs on your own infrastructure and documents never leave the company.
Document inventory and samples
We map which document types arrive, in what volume and through which channel. We collect real samples of each and agree on which fields go to which system.
Extraction and validation rules
We build the extraction pipeline and add your finance and operations rules alongside it: total consistency, tax number checks, purchase order matching and duplicate detection.
Measure on a sample set
We measure field-level accuracy on documents whose correct values are already known, and use the results to decide which fields pass automatically and which go to review.
Run in parallel, then switch over
We run the system alongside your current manual entry for a while and compare results. Once they earn trust, we switch on the export and tune review thresholds over time.
The main factors behind the quote are the number and variety of document types, monthly volume, the share of scanned or handwritten documents, how deep the extracted fields go, the complexity of validation rules and how many systems the data must reach. Beyond the build, there is a variable running cost that scales with document volume, which we model transparently up front from realistic volume estimates. We usually recommend a pilot with a single document type.
Let’s talk about this
Accuracy varies by document quality, type and field, so rather than quoting one number we measure it field by field on your own documents. It is high on clean PDF invoices and lower on poor scans or handwriting. Validation rules and the human review queue stop a wrong field from slipping into your system unnoticed.
OCR turns the writing in an image into text but does not understand it. Intelligent document processing knows which part of the text is a date, an amount, a tax number or a contract clause, turns fields into structured data and validates them with rules. No per-supplier template is needed, and new layouts are usually handled without trouble.
Yes, scanned documents, phone photos and much handwriting can be read. As image quality drops, however, confidence drops too and those records go to review more often. At the start we measure on your sample documents what results each quality level gives, and suggest scanning settings where useful.
Yes. We can pass approved data to systems such as Logo, Mikro, Netsis, Paraşüt or SAP Business One through an API or an import file. Supplier account matching and assignment of account codes and cost centres can also be rule-based. We choose the safest export method together based on what the target system supports.
e-Fatura carries structured data, so it needs no reading; we route it straight into a matching flow. But foreign invoices, delivery notes, expense receipts, contracts, quotes and e-mail attachments still arrive as PDFs or paper. Document processing handles that remainder and brings both flows together in one review screen.
Fields such as parties, dates, amounts, renewal, termination and penalty clauses are extracted and compared with your standard terms. Clauses that deviate are flagged and key dates become calendar alerts. The final legal judgement always stays with your legal team; the system lightens their reading and tracking load.
We agree on the processing location and retention period at the start. With cloud models, enterprise terms that exclude training and a suitable region are chosen. For stricter needs, processing can run on your own infrastructure. Personal data can be masked, documents deleted automatically after a set period and all access is logged.
Yes, but in a controlled way. Corrections made in the review queue are recorded, and supplier-specific hints, matching tables and rules are updated from them. Each change is first tested against a sample set with known correct values and does not go live if quality drops. Over time the review queue shrinks.
A pilot with one document type usually starts running alongside the current process within a few weeks, provided samples are gathered quickly. Results are compared during the parallel period, then the export is switched on. Document variety and the target system’s integration options affect the timeline most. New document types are added to the same infrastructure faster.
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