AI Integration Services
You have probably already seen the same pitch twice: a transformation roadmap, a demo on clean data, and a timeline that makes everything sound manageable. The part that gets left out is what happens after the demo, the source system audit that uncovers messier data than expected, the production hardening work that was never in scope, and the monitoring layer your team is expected to build after handoff. That gap between a polished pilot and a production-ready system is where many AI integration projects stall. When they do, the cost is not just the vendor invoice. It is the internal time consumed, the opportunity cost of the delayed workflow, and the technical debt of a half-built system someone has to maintain or tear down. In practice, stalled integration work often consumes meaningful budget and months of timeline before the scope is renegotiated, especially when the organization lacks the internal capacity to identify what went wrong. ...