Spark AI

The core loop is simple: your AI calls SparkAI via API when it hits an edge case, a trained specialist reviews the event using ML-assisted tooling, and a resolution comes back in seconds — directly into your live system. No edge case queue you drain the next morning. No retraining sprint before you can ship. The vendor states this model is designed for teams that need to launch with an imperfect model and tighten accuracy over time in production, rather than waiting for lab-perfect performance. The wall appears when your edge case volume is so high that human review latency — even seconds — introduces unacceptable downstream delay. Teams in that position typically move toward fully automated fallback logic instead.