What goes wrong with manual quote processing
An incoming quote request from customer A looks nothing like one from customer B. One sends a PDF with fixed columns, another sends an email with a list in the body text, a third sends an Excel file with a varying column order. Field names differ too: 'item', 'product number', 'SKU', or just a description. Anyone processing this manually rekeys every request into the ERP or quoting system from scratch. The same applies to incoming quote confirmations: you have sent a quote, the customer sends back an approval, and someone has to convert that approval into an order or sales invoice. With three confirmations a day, that is manageable. With thirty, it becomes a bottleneck.
What document-AI concretely solves in quote processing
Document-AI reads the incoming quote or request, understands the context of the document, and extracts the relevant fields: customer details, line items, quantities, prices, delivery agreements, and reference numbers. This happens without having to set up a separate template for each customer or supplier. The result is delivered as structured data to your system, with a human approving it before it goes to the ERP, TMS, or accounting package. That is precisely the difference from ordinary scan-and-recognise: it is not about copying text, but about understanding and matching fields. An item number listed in the quote as 'ref. 4812-B' is recognised as the same as 'item 4812B' in your own system. Discrepancies are flagged, not quietly passed through.
When does automating quote processing make sense?
The rule of thumb is volume and repetition. If you are processing fewer than ten quotes or requests per week, the gain is too small to justify the effort. But once your team is structurally spending multiple hours a week on rekeying, and the documents arrive in recognisable categories — requests, confirmations, amendments — automation pays off. It also pays off when error risk is high: wrong quantities, missed discount agreements, or a reference that does not match the order generate correction work and can cause disruptions further down the chain. Document-AI catches those discrepancies early, before they enter the system.
Where automating quotes does not make sense
High customisation is the main exception. If every quote is fundamentally different, the customer sets different terms each time, and there is little fixed structure, automation has little to grip onto. The person handling it still needs to read and assess the entire document; automation then offers limited help at best, perhaps pre-filling a few standard fields. A second limit is source document quality. Requests that arrive as a photo of a handwritten note, a poorly scanned fax, or an informal email with no structure are difficult to process reliably. That does not mean it is impossible, but you need to be realistic about rejection rates and the time a staff member spends on manual correction. And third: if the decision-making process around a quote is complex — the customer wants to negotiate, prices are not fixed, margins are assessed manually — that is a commercial process, not a document processing problem. Document-AI helps with the reading and keying work, not with the negotiation.
What trade-offs come with automating quote request processing?
The biggest trade-off is accuracy versus throughput speed. Automation is fast, but if you want one hundred percent certainty, the human check is still necessary. That is also the model dottle is built on: the machine does the reading and keying, a staff member approves. That person no longer has to retype anything, but does verify that everything is correct. A second trade-off is flexibility versus standardisation. The more you standardise your incoming quote flow — fixed channels, agreed formats with customers — the better automation works. But that is not always achievable in a market where customers use their own purchase forms. Document-AI is built precisely for that reality: varying formats from different customers, without setting up a separate template per sender. A third point is the connection to your system. Automated quote processing only has value if the structured data actually reaches your ERP or order management system. That requires a working integration, and it is worth setting up that integration and the approval workflow properly before going live.
How do you approach the implementation?
Start with the document flow you want to automate. Are these incoming requests from customers, approvals on quotes you have sent out, or both? Map out the volume, the channels through which they arrive — email, portal, post — and how much the formats vary. Then choose a scope that is small enough to go live quickly but large enough to see a clear effect. In practice, confirmations are often the best starting point: they are more structured than requests and flow directly into an order or invoice. dottle goes live in two weeks, with no months-long implementation project and no setup fee. You pay per document. That makes it easy to start small and scale up once the flow is stable.