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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 1–6 of 11

A pair of developers sitting in a real client workplace observing the team work, notebooks open.

Why we spend a whole week on discovery before quoting

The two-week discovery sprint isn't a sales tactic. It's the only honest way to estimate a bespoke software project — and the reason our final invoices are within 10% of our estimates.

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Two laptop screens side by side: one showing a generic SaaS dashboard, the other a bespoke web app — the build-vs-buy decision in physical form.

Build vs buy: the question we get asked the most

After 20 years of these conversations, here's the honest framework we use to help businesses decide whether to build something bespoke or live with off-the-shelf software.

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A customer-services agent at her desk reviewing an AI-drafted reply on screen, finger hovering over the send button.

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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A scanned purchase order on the left of a monitor, beside a clean structured-data view of the same document — line items extracted, products matched against the catalogue, exceptions flagged.

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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A salesperson reviewing an AI-drafted quote on screen — line items, prices, stock indicators and lead times all assembled from internal systems, ready for her to set the margin and send.

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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A well-presented business owner standing in a bright modern kitchen at 7am in pyjamas, checking her phone, while children in school uniform eat breakfast in the background with their faces not visible.

Built for your Tuesday morning

On why software for businesses needs to be built by people who've felt what it's like when the system isn't there. Not a snipe at developer culture — an observation about whose Tuesday morning the software actually has to survive.

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