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Swiss-based technology advisory firm, specialising in Software Due Diligence and Tech Advisory. https://teapawt.net/t.co/LET5ZaDFS2

Switzerland
Joined March 2018
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Most organisations already know the baseline for AI agents: least-privilege access, human sign-off on risky actions, logging, and a way to shut them down. The NCSC’s recent post covers this well. How much autonomy do AI agents have in your organisation? ncsc.gov.uk/blogs/managing-t…
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Two productive companies do not automatically make one. After an acquisition, teams still need to deliver while connecting systems and adapting to shared processes. The integration itself takes capacity that the growth plan needs to account for.
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A decade of Avalia Systems. 🎂 What we’ve built comes down to our people: their experience and the work they put in for our clients every day. Thank you to our clients and partners for the trust you place in us. Here’s to the next one! Happy birthday, Avalia!
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DOT Digital Group cut a 224-hour task to 48. Not with a better tool. With a better workflow. Which process would you redesign from scratch?
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224 hours → 48 hours. Same task. Each square is one hour spent on [the task]. Together with DOT Digital Group, we rebuilt the process around it and gave back 176 of them. More on this monday! #AvaliaTechForum #ProcessImprovement
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AI pilots are easy to start. Scaling them is harder. At Avalia Tech Forum 2026 in São Paulo, Ana Paula Gutierrez from DOT Digital Group shared what changes when AI moves into real operations. We summarized the main points here: avalia.io/post/scaling-ai-in…
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1. Waiting: where work sits still: review queues, approvals, blocked releases, dependencies. 2. Rework: what keeps coming back: reopened tickets, hotfixes, rollbacks, repeated fixes. 3. Unplanned work: what starts displacing the plan: incidents, firefighting, production fixes.
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Read your tech spend as a story, and check that the story matches your strategy.
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Engineering metrics can show that delivery is getting faster. But outside the engineering team, numbers like deployment frequency or lead time often raise another question: What does this actually change for the business?
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Recent research, including Microsoft’s EngThrive framework, reinforces something we see as increasingly important: the same engineering data needs to answer very different questions depending on who is looking at it.
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DX Hub makes your portfolio something leaders can actually explore. This 3D view is one way to see it. You spot where complexity concentrates and where action is needed, then take it to the board with evidence.
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Most "productivity" gains are just future problems you haven't paid for yet. Is durable productivity a process problem or a culture problem? youtu.be/b39qApl1lAM
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How the right visibility turns engineering reality into the language your board actually acts on. Worth a read if you've ever left a leadership meeting knowing your teams delivered and wishing you could show it. avalia.io/post/cto-prove-bus…
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A team can deliver more features, tickets or releases, but that alone does not show whether the work improved the business, reduced costs, solved a real problem or justified the money and time invested.
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Productivity should connect technology spending to actual impact, not just visible activity.
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