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How to automate business email handling with AI
3 min read
The shared mailbox (info@, orders@, quotes@) is where almost all the work arrives and where it piles up. Every message has to be read, understood, passed to someone or turned into an action. It is the ideal candidate for automation, provided it is done the right way.
Why the shared mailbox is the bottleneck
Requests arrive in free text, with attachments of every kind, often with incomplete information. Nobody has sorted them: the same quote request can arrive as three lines of text, as a hand-filled PDF or as an Excel table.
The result is a manual workflow that is slow, repetitive and prone to transcription errors. And it is invisible in reports, because no system measures it.
What AI can do with an email
Four things, in sequence. Classify: understand whether it is a quote request, an order, a complaint or spam. Extract: pull out customer, quantity, product, deadline and every other useful field, each with a confidence score. Route: forward it to the right department or person. Act: send an automatic reply, generate a quote, open a ticket, update the management software.
Language models suit the job precisely because the text is messy: they understand intent and data even when the form changes from one sender to the next.
Attachments: PDFs, Excel, CSV
Most of the value is in the attachments. A native PDF is read directly; a scan first goes through OCR and then through the model, which interprets the text; spreadsheets and CSVs are read as structured data and then validated against catalogues and business rules.
The flow is the same for all of them: every extracted field has a confidence score, and the next step depends on it.
What stays with people
The decisions. A high-confidence email can generate the reply and the order line by itself. An email with a doubtful deadline or an unknown customer stops in a dashboard, where someone approves, corrects or rejects in one click, and the decision stays on record.
The goal is not to take the team out of the mailbox, but to take the mechanical work out of the mailbox and leave the team only what requires judgement.
A concrete example: the quote request
An email arrives from a customer saying "we need 240 M8 flanges by mid July". The system classifies it as a quote request, extracts customer, quantity and product with high confidence and the deadline with lower confidence, because "mid July" is ambiguous. The deadline is flagged for a check; the rest proceeds: automatic acknowledgement, forward to sales, quote generated, ERP sync.
This is the flow we are building in Sabkha, our solution currently in testing. If it looks like yours, let's talk.