AI and Business Automation Blog
What we learn automating real companies: what works, what does not, and where it makes sense to start.
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We write about what we see when automating real companies: what works, what does not, and what it actually costs to put artificial intelligence to work in a business. No hype, no unnecessary jargon.
Articles
WhatsApp chatbot: official API or a connected phone (and why it matters)
There are two technical ways to put a bot to work on WhatsApp, and they cost you very different things. From the outside they look alike: a number, a chat, automatic replies. Underneath they have nothing in common, and the difference only shows once you have paid, once the volume climbs, or the Tuesday morning your number stops working. This explains both routes, what breaks in each one, and what to ask before you sign.
Read the article →What does automating customer service cost?
Nobody asks for a quote out of curiosity. If you are reading this, it is because someone on your team spends the day answering the same question, or because the messages that arrive at eleven at night are still unopened at nine the next morning. The number you are looking for does exist, but it will not fit in a price table on a website. What can be explained upfront is what that number depends on, and how to get to it without going round in circles.
Read the article →AI voice agents for calls: what they do well and what they do not
There is a cost that hardly ever shows up in the accounts: the phone that rings and nobody answers. It leaves a missed call in the log and nothing else: you do not know what they wanted. They hang up and dial the next number. This article is for anyone weighing up putting an AI on those calls, and who wants to know, before signing anything, where its work starts and where it ends.
Read the article →Automating restaurant bookings without losing the personal touch
A lunch service has a rhythm of its own: someone asks for the bill, a couple walks in without a booking, and the phone rings for the third time. Whoever answers it stops looking after the person standing in front of them; whoever ignores it loses a table. That is the real problem, and it is not fixed with more friendliness or more people on shift. It is fixed by deciding which calls and which messages do not need a person behind them.
Read the article →What you should not automate (even if you can)
You usually arrive with a list of tasks you want off your plate. Some of them are good candidates. Others are not, and building an automation on top makes the problem worse, hides it, or makes it more expensive to fix. This piece is about those others: how to spot them before you pay for them.
Read the article →How to automate customer service with AI: where to start
Customer service is usually the first process a company automates, and for good reason: it is repetitive, measurable, and customers notice the improvement immediately. Start with the boring part — review the last three months of enquiries and count how many are always answered the same way. In most businesses, 60 % to 80 % of messages are a handful of repeated questions: opening hours, order status, prices, returns. That is where AI performs well from day one.
Read the article →Want to apply this in your company?
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