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Practical reads on earning more and losing less with data and AI. One idea, one number, one thing to try.
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AI agents in finance: what they can do today, and what still needs you
AI agents can now gather data, run checks and draft the next step across several systems. In finance that is useful and risky in equal measure. A practical look at where they help, where they don't, and how to start.
Continuous controls monitoring: a buyer’s guide
Continuous controls monitoring promises to test every transaction, every day. Before you buy, know which controls to start with, what an alert should look like and who will act on it.
People stay in charge: designing human approval into autonomous workflows
Autonomous workflows can test, match and draft faster than any team. Deciding still belongs to people. Here is how to design approval into the flow so speed and accountability go together.
The intelligence moves, your data doesn’t: private AI in practice
Most AI services ask you to send your data to them. Private AI reverses the direction: the models come to your environment and the data stays where it is. Here is what that looks like in practice.
Sampling is a habit, not a method: why every transaction matters
Sampling began as a workaround for paper and limited time. The constraint is gone; the habit remains. Testing every transaction is now a choice, and it changes what assurance means.
Pay for what you deploy: the end of shelfware
Enterprise software is often bought in bundles and used in parts. The rest becomes shelfware: paid for every year, used by nobody. Paying for what you deploy changes who carries that risk.
80% ready, 20% yours: why configuration beats customisation
Custom code makes software fit today and break tomorrow. Configuration makes it fit and keeps it maintainable. Here is where to draw the line between the two.
What is CoSaaS? Services, delivered as software
Software gives you a tool. Consulting gives you people. CoSaaS gives you the outcome: a proven solution, configured to your business and run with you until it pays.
See it in practice
Use cases from our own project work, anonymised, and scenarios that show the method: one problem, the checks we run, what they find.