Data Integration
API architecture: governance, performance and integration
An API portfolio needs architecture standards, governance and suitable tooling. This article reviews common risks and the components used to secure and maintain interfaces.
Our expertise is integration technology, and specifically middleware: the layer that lets applications inside a company talk to each other, and to the outside. Internal communication is one half of it. The other is exchanging data with partners in formats they impose, which means building interfaces that extract the right information, format it to the applicable standard, send it, handle the response and update the systems behind it.
AI inside the integration layer
Managing transaction flows is more than synchronising data, and that position gives us an unusual opportunity. Integration platforms produce a large volume of data about their own behaviour. An AI built carefully on top of that data goes beyond its initial function and becomes a detection tool: fraud prevention, better system security, and anticipating a failure early enough to propose the fix rather than report the outage.
We see it in a wider frame, where AI changes how we operate and improves the predictability, responsiveness and performance of everything we deliver. Knowing the integration technology is what makes the AI result usable.
Patrick Luc
How AI changes engineering skills
AI is moving fast enough that staying current is itself a workload. It is also reshaping what an IT professional does. Engineers who were firmly in the driver's seat now work alongside something that writes, reads and audits code at a speed no human matches. Our answer is to bring AI into the business deliberately, ahead of the change rather than after it.
We are a consulting firm with long-term engagements, not a body shop. We are integrating AI internally as a kind of augmented memory in the service of our consultants.
Alain Kunnen
The aim is to give consultants a shared internal reference that goes beyond what any individual happens to know. That improves what clients get, and it keeps our engineers credible in a technical environment that keeps widening. Our own R&D work measures the quality of the answers the system produces, which is the part most organisations skip.
Data quality for AI
AI projects get launched hoping for a striking result from raw data, and then discover the work that has to happen upstream. Validation, cleaning and standardisation are what make the answers reliable and relevant.
Patrick Luc
We run our internal projects that way, and it shapes how we help clients bring AI into their own processes.