On discovery, back offices, and where AI actually helps.
We don’t publish often — only when there’s something concrete to say. What’s here tends to cluster around a few threads: finding out what to build before anyone writes code, putting AI inside real workflows (inboxes, order desks, quotes), connecting the systems you already pay for, and the build-or-buy calls operators face every month. Written for people who run the work, not for developers chasing trends.
Showing 1–4 of 4 in ai
AI in customer services: a draft on every reply
How AI changes the shape of a customer-services inbox — reading inbound messages, looking up the customer and order across your systems, and putting a draft reply in front of a person who still presses send.
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AI for document understanding: POs in, structured data out
Purchase orders, supplier price lists, delivery notes — the documents your back office still re-types by hand. How AI plus a few well-chosen MCP lookups turns the order desk from a typing job into a judgement job, with humans on the exceptions.
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AI for the back office: drafting quotes from your own data
The outbound mirror to the two inbound AI posts. How AI assembles a draft quote from your own data — customer history, contracted pricing, stock, lead times — and lands it on the salesperson's desk. They set the margin and press send. With a look at where this naturally leads next: customer and quote portals.
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Customer & quote portals: where customers do the work themselves
The natural follow-on to the AI-drafted quotes post. What a portal actually is, where AI sits inside it, and the awkward bits nobody mentions until you're three months in — pricing visibility, the self-service/human boundary, and how to roll one out without upsetting customers who love email.
Read postLet’s talk about your back office
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