AI-Driven Decision Engines

For structured, predictable tasks, rule-based automation is 100% accurate and preferred. AI shines on unstructured or variable inputs — classifying free-text enquiries or extracting data from varied invoice formats. Well-designed AI decision engines typically achieve 90–97% accuracy on these tasks with human-in-the-loop for the rest.

We implement confidence scoring on all AI outputs. Below a configurable threshold, the item is automatically routed to a human review queue with the AI's suggested output pre-filled for one-click approval or correction. No AI decision in a critical workflow acts autonomously unless you explicitly configure it to.

We implement semantic caching, model tiering (cheap models for simple tasks, GPT-4o only for complex reasoning), token budget limits per workflow, and cost dashboards per workflow. Most AI automation workflows average $0.001–$0.01 per execution.

Yes. We select multilingual models (GPT-4o, Claude 3.5) with strong Arabic capability and validate accuracy on Arabic test data before go-live. For dialect-heavy inputs (Gulf Arabic customer messages), we add a normalisation preprocessing step to improve classification accuracy.

Monthly accuracy audits sampling AI outputs vs. human ground truth, drift alerts when accuracy drops below threshold, quarterly prompt improvement cycles using human correction data, and model version tracking with rollback capability if a model update degrades performance.