We build a layer that reads incoming invoices, delivery notes, specifications and customs documents field by field and posts them into your ERP. It validates every field it reads against your own records, routes anything it is unsure of for approval, and keeps a record of which value came from which part of which document.
The invisible part of data entry in a factory piles up on the purchasing and accounting desks. Supplier invoices arrive by e-mail as PDFs, delivery notes come through the door on paper, product specifications arrive in each customer's own format, and customs and letter-of-credit paperwork sits in a separate folder. Fields such as dates, amounts, VAT, quantities, units, batch numbers and tariff codes inside these documents are read by a person and typed into the system by hand. Most days the work keeps up; in busy weeks and holiday periods entries fall behind, and when they do, stock and cost figures drift away from reality.
Automating this is not, as often assumed, simply a matter of running documents through a scanner and converting them to text. Conversion to text is only the first step; the real challenge is picking out which number in that text belongs to which field. Every supplier lays out its invoice differently, the same supplier changes its template over time, line items spill onto the next page, and discounts and freight appear sometimes within a line and sometimes as separate lines. A setup that relies on fixed coordinates starts silently producing wrong data at the first template change — and that is exactly what makes it dangerous, because the error is invisible.
That is why the layer we build does three things together: it parses the document along with its layout, validates the extracted fields against your own records, and routes any field it is not confident about to a person for approval. Validation is where the safety of the whole process lives: the machine checks that line totals match the grand total, that the VAT matches the rate, that the supplier and product match your master records, and that quantities agree with the order and the delivery note. Records that pass these checks are posted to the ERP; records that do not land in an approval queue, with the reason each item was flagged shown on screen.
The work described on this page does not replace accounting judgement. The software carries the data from the document accurately and keeps proof of where it came from; which entry goes to which account, whether an expense is accepted and what goes into a filing are decisions for your accountant and finance lead. Likewise, extraction accuracy is never flawless out of the box in any deployment; our commitment is not zero errors, but errors that are visible and catchable. That is why every field below the confidence threshold goes to a human, and every record remains traceable back to the page of the document it came from.
Factories where the monthly volume of supplier documents has grown to the point of consuming someone's entire day. Because delays in document entry directly distort stock and cost reports, the problem stops being a data-entry issue and becomes a question of whether management can trust the picture it sees.
Businesses working with foreign supplier invoices, packing lists, quality certificates and customs paperwork. These documents fall outside the e-document framework; they arrive in different languages, in different layouts and often as scans. Manual entry is at its heaviest and most error-prone here.
Textile, carpet, packaging and machinery manufacturers who work from the customer's own technical specification form on every order. Manually transferring dimensions, colours, quantities and tolerances from the specification to the production order is a well-known source of errors that become expensive later.
Businesses whose contracts, quotations and paperwork from years past sit in folders. The documents are stored, but the data inside them is not searchable; finding a clause depends on someone remembering.
Invoices, delivery notes, order forms, specifications, packing lists and customs documents are each defined as a separate type; for every type, which fields to extract and what format each field must take is defined up front. Extraction weighs the document's layout and field labels together rather than relying on fixed coordinates, so the setup does not collapse outright when a supplier changes its template.
Every extracted field is tested against your real data: supplier master, product master, open orders, units and currency, payment terms and VAT rate. If line totals do not match the grand total, if a quantity exceeds the order or if the product does not match, the record is not posted automatically. Validation rules are written to fit the way you work and can be changed over time.
Every field comes with a confidence score. Fields above the threshold are processed directly; those below it land in an approval queue. The approval screen shows the document image and the extracted field side by side; the approver compares at a glance, corrects and passes it through. Thresholds can be tuned per field according to how critical each one is.
A document that passes validation becomes a record in the system you use: a purchase invoice, a goods receipt, an order match or the relevant record type. We use the interfaces or data transfer methods supported by systems such as Logo, Mikro, Netsis, Canias and SAP; the system's internals are left untouched — it operates as a layer writing alongside it.
For every record, the system stores which document it came from, which page of that document and which field. When a figure is questioned, its source opens in one click. Who approved it, when, and what was corrected are also on record — producing, as a matter of course, the trail needed for both internal control and audit.
Processed documents do more than turn into records: together with their content they build a searchable archive. All of a supplier's delivery notes for a given period, a product's historical prices or a specific clause in a contract can be found without opening a folder.
Together we map out which document types arrive, their monthly volume, which channel they come through and who currently spends how long on them. Real samples are collected for every type — including the worst ones, not just the cleanest. Scope is set from this inventory; trying to automate everything at once is a common mistake.
We start with the highest-volume, most standard type — usually the supplier invoice. Fields, validation rules and thresholds are defined for that type and tried on real documents. Throughout the pilot the system does not post records, it only suggests; its output is compared against the manual entry.
What passes automatically, what goes to approval and what is rejected outright is set down in explicit rules. The rules are drawn from your existing control habits; the checks a person does by eye today are put in writing. This step is the part of the project that deserves the most discussion.
Record posting is connected, the approval screen is opened to the relevant people and permissions are defined. The undo path is built from the start: how a wrongly posted record is cancelled and corrected is known in advance. We move to this stage only when the pilot output reaches a level you are willing to accept.
Other document types are added one by one. For each type we track the share passing automatically versus going to approval, the frequency of corrections and which fields cause persistent trouble. This measurement drives the tuning of thresholds and rules over time; the work does not end when the installation does — the tuning continues.
Usually yes, but quality directly affects accuracy. Extraction gets harder on skewed scans, faded pages, stamped documents and handwriting. That is why we ask for your worst samples at the inventory stage; we set the scope by looking at your real documents. In some cases the most honest recommendation is to fix how the document arrives before installing any software; asking the supplier to send the same document as a PDF is often the cheapest improvement available.
Records are created in your system and with your approval. The system is built to stop errors slipping through silently: fields below the threshold are routed for approval, and documents that trip a validation rule are never processed automatically. We decide together which fields require human approval; for critical fields, never enabling automatic pass-through is a perfectly valid choice. Responsibility for the accuracy of the books and for filings remains with you and your accountant in every case.
Documents within the e-invoice framework already arrive structured and need no reading. Document intelligence is really for everything outside that framework: foreign supplier invoices, packing lists, quality certificates, customer specifications, customs and transport paperwork, paper delivery notes and contracts. As electronic documents spread, the number of manually entered documents falls but their variety grows; most of what remains is non-standard.
That decision is yours, and the architecture is built around it. Documents can be processed by models running on your own server without ever touching a cloud service; with that option there is a trade-off between accuracy and hardware cost, and we discuss it openly. If an external service is to be used, what data goes out, how long it is retained and what the contract says are put in writing from the start.
We use the interface the system exposes or the data transfer method it supports. Logo, Mikro, Netsis, Canias and SAP environments can all be worked with; the method depends on your version and licence, so the integration route your vendor permits is clarified at the first discovery. We never make unauthorised changes inside the ERP; the layer runs alongside it.
A working flow that collects, reads, validates and posts documents; an approval queue and approval screen; an audit trail showing which record came from which document; a document archive searchable by content; and handover documentation explaining how to tune the flow. Everything produced, source code included, belongs to you and is delivered at handover.
In a 30-minute discovery call we listen to what you need and tell you honestly whether custom development or an off-the-shelf product is the better answer.
We develop solutions built on Turkish speech recognition and synthesis: call recordings transcribed and made searchable, topic and sentiment analysis, a voice assistant on the order line, and voice-driven data entry for field staff whose hands are full.
Details →For organisations that will not let their data leave the building, we build model infrastructure that runs on your own servers or in your own cloud tenancy: hardware planning, model selection, internal document search, routing that decides which job goes to which model, and cost measurement.
Details →We install workflow engines and internal tool builders that run on your own servers, connect them to your systems and — most importantly — set up their governance: who can build flows, where secrets live, who owns each flow and who fixes it when it breaks.
Details →We build a system that gathers occupational health and safety (OHS) data in one place — from risk assessments and periodic equipment inspections to per-employee training and medical examination validity and near-miss records. When an audit arrives, the requested file is not compiled; it is already there.
Details →We build a system that runs your personal data inventory, retention and disposal schedule, data subject requests and breach response records. The software manages the process and the evidence; legal interpretation and the content of your notices belong to your lawyer.
Details →We build a system that traces waste from the moment it is generated to the moment it leaves the site, feeds your declarations and tracks permit and licence deadlines. The figures are not compiled at declaration time; they fall into place the moment they are recorded.
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