AI engineering

Calling a language model is easy. Building an AI application that runs on real data every day, catches errors and stays explainable is a craft. AI engineering sits between data science and software development: it is less about training models and more about everything around them. Having such an application built to measure is what our custom AI software work is for.

An AI engineer builds the parts you never see in a demo: RAG so the model draws on your own sources, integrations with your systems through APIs, evals that measure whether a change improves anything, and logging, permissions and checkpoints for a person. The model itself is replaceable; the layer around it is where the quality lives.

For DigiSocial we first settled on one architecture: a web app with a single login and a shared AI layer, on which three modules run. We then built module by module, refining prompts and sources based on real use. That is AI engineering in practice: not one clever prompt, but a system that grows with the business.