What problem does Dottle solve?
Many operational teams spend a significant part of their day retyping data. A purchase order arrives as a PDF. Someone opens it, reads the article number, the quantity, the address, types everything into the ERP or TMS, and moves on to the next document. The work is not difficult, but it is error-prone and does not scale well. When volume increases, headcount grows with it, or the backlog builds up. Dottle addresses this pattern at the root: it reads the document, recognises the fields, and places the data where it needs to go. The employee only sees the exceptions.
Which documents does Dottle process?
Dottle is built for the document types that arrive most frequently in operations: purchase orders, packing lists, consignment notes, invoices, and delivery confirmations. What they have in common is that they are structured or semi-structured documents where the layout varies by sender but the content is predictable. A packing list from supplier A looks different from one from supplier B, but both contain an item description, a quantity, and a location. Dottle learns to recognise that variation per sender. Documents that contain genuinely free text, such as complaint forms or contracts with negotiable clauses, are less suitable. That is worth stating plainly.
How does it work in practice?
The document arrives via email, an upload portal, or a shared folder. Dottle processes it, extracts the relevant fields, and matches them against existing records in the system, for example an open order or a debtor number. If the confidence level is high enough, it posts through without any intervention. If there is uncertainty, an unknown supplier or a deviating quantity, it goes to a review queue. The employee sees exactly why Dottle is uncertain, confirms or corrects, and the system learns from that correction for next time. That is the human-in-the-loop principle: not the AI deciding in ambiguous cases, but the person having the final say.
Where do teams run into issues during implementation?
The most common obstacle is not the technology but the state of master data on the receiving end. Dottle can recognise an article number, but if the ERP holds a thousand active items with inconsistent naming, matching becomes harder. The same applies to addresses, supplier codes, and units of measure. Before Dottle works well, the system it connects to needs to be in order. That sometimes takes more time than the integration itself. A second pitfall is the expectation: some teams assume the system will process everything without errors from day one. In the first weeks, the share of documents that pass through without intervention is lower than at the end of the run-in period. That is normal, but it needs to be discussed upfront.
When is Dottle not the right fit?
If you receive only ten documents a week and the variation is limited, the time savings are small. The implementation effort and ongoing maintenance will not justify the gain. Dottle delivers the most value at volumes of several dozen documents per day or more, especially when the layout variation is high or the documents come from many different sources. If the real problem is not data entry but the decision behind it, such as purchasing recommendations or planning choices, a different tool is better suited. Dottle is an execution system, not a decision-support system. That distinction matters.
What does it deliver?
The direct result is less manual work on documents that are always the same. Employees can direct their attention to the exceptions that genuinely matter, rather than routine processing that fills the day. Indirectly, faster processing means faster throughput: an order in the system within a minute rather than at the end of the afternoon. That has an impact on inventory management, planning, and customer satisfaction. Not because Dottle is intelligent in a self-learning sense, but because it reliably and quickly does what people always did by hand.