AI Agents
Agents that understand the organization they act in.
AI agents are everywhere in the news. But an agent cannot act in an organization it does not understand. The old data equation still holds: garbage in, garbage out. However capable the model, if the data is bad, so is the result.
In recent years, every need has translated into one more SaaS: standard building blocks, rented, not mastered, and data fragmented with every addition. Deploying agents on that landscape means automating the error.
The approach
1. Make the data AI-ready. Audit of the application landscape, structuring, construction of the context layer agents consume: the digital twin, a unified, governed representation maintained by AI.
2. Deploy agents on the workflows. Document processing, claims, reporting: tailor-made solutions, under control, adaptable at will. Every action is traced to its source, and binding decisions go through humans.
The context layer, in detail: For organizations →