Case Studies
Real world results. See how we've helped other enterprises navigate complexity.
Teleia: The AI-Native Learning Platform
The Challenge
Traditional platforms were failing to engage students or provide timely feedback at scale. Tutors were overwhelmed with administrative grading, leaving little time for mentorship.
The Solution
We architected an AI-native platform from the ground up. By integrating autonomous AI agents, we enabled 24/7 student support for coursework queries and real-time concept explanation. For faculty, our automated grading pipelines handle complex assessments with human-level accuracy.
The Impact
The system now powers adaptive learning paths that evolve instantly based on student performance, ensuring no learner is left behind while significantly reducing operational overhead for the institution.
Restructuring AI Workflows for Scale & Efficiency
The Challenge
A leading UK-based agency (revenue £10M+) had a capable AI system for campaign management, but its fragmented structure was inefficient. Each task ran through a separate AI call, causing constant context resets. This made the system slow to respond and expensive to operate.
The Solution
We rebuilt the architecture to enable a single, coordinated AI workflow. This allowed the system to carry context from one stage to the next—understanding the campaign goal from strategy to asset generation—rather than starting from scratch each time.
The Impact
The results were immediate. API spend dropped from £15k to £6k per month, saving over £100k annually. The agency recouped their investment in just seven months, proving that structural AI improvements drive massive business value.
Adaptive Forecasting to Eliminate Overstocking
The Challenge
A regional logistics provider with 50+ warehouses across the GCC faced recurring forecasting failures around Ramadan and Eid. They often overstocked to avoid shortages or missed demand spikes entirely, forcing them to rely on costly last-minute air freight to fulfill orders.
The Solution
We implemented an AI-assisted demand forecasting tool that identifies subtle shifts in buying patterns. Crucially, the model was trained on three years of "planner overrides," allowing it to learn from human intuition and reduce false alerts from local market anomalies.
The Impact
In the first year, the company saw a $130k reduction in express air freight costs and $80k–$100k in staffing savings. Excess stock dropped by 8%, freeing up ~$320k in working capital. In total, the project delivered over $500k in annualized savings, paying for itself within months.