AI Integration & AI Workflow Automation for Existing Systems

Most AI integration work isn't about the model itself — it's about plumbing. Getting the right data to the model at the right moment, usually through an API call to your CRM or document store, and getting the model's output back into your system of record via a webhook or API write. The quality of an AI integration depends more on this data flow than on which language model you pick; a well-fed average model usually outperforms a top-tier model working with poor or missing context.

Data privacy needs deliberate design, not an afterthought. Under GDPR/AVG, you need to know which personal data is sent to an external model provider, whether it's processed within the EU or under adequate safeguards, and whether it's retained or used for training by the provider. For many businesses this means choosing providers with clear data processing agreements and configuring integrations to send only the fields actually needed, rather than entire records.

Monitoring matters because AI outputs vary in ways rule-based systems don't. A production integration needs logging of inputs and outputs, a way to flag low-confidence or unusual responses for review, and a clear owner who checks performance over time — not just at launch. Integrations that connect to multiple systems (CRM, email, documents) also need to handle what happens when one system is down or returns unexpected data, which is a normal engineering concern that's easy to skip when the focus is on the AI itself.