Results,notpromises.

The challenge

A UAE retail bank's legacy IVR system couldn't understand Gulf Arabic dialect variations — routing 72% of inbound calls to human agents unnecessarily. With 10,000+ daily calls, the cost in agent time and customer satisfaction was severe. We built and fine-tuned a real-time Arabic ASR + intent classification model, integrating it into their existing telephony stack without a rip-and-replace.

The challenge

A UAE utility operator maintained 200 km of high-voltage transmission infrastructure on fixed calendar schedules — regardless of actual asset condition. Unplanned outages were costly and dangerous. We deployed a computer vision + sensor fusion model that continuously analyses drone footage and IoT telemetry to surface anomalies weeks before they become failures.

The challenge

Associates at a GCC-based law firm spent over 60% of their billable hours on first-pass contract review — reading Arabic and English agreements to extract key clauses, obligations, deadlines, and risk flags. The work was repetitive, error-prone, and expensive. We built a bilingual NLP engine trained on regional legal language that structures any contract into a reviewable summary in under two minutes.

The challenge

A regional e-commerce platform serving customers across 6 GCC markets relied on rules-based product recommendations that ignored browsing behaviour, seasonal patterns, and cultural purchasing differences between markets. We replaced their recommendation stack with a real-time ML model trained on 18 months of anonymised transaction data.