One idea at a time.
Practical reads on earning more and losing less with data and AI. One idea, one number, one thing to try.
Blog
Leave clients stronger: self-sufficiency as a design goal
Many service models quietly depend on the client needing them forever. CoSaaS is designed the other way: every engagement should leave your team more capable and less dependent on us.
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.
Orchestrated, not abandoned: the hidden cost of self-service analytics
Self-service analytics promised that everyone would find their own answers. Many teams got a hundred dashboards and no decisions. The fix is not less access; it is someone running the system.
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.