Ride-Hailing

Business problem

• Tens of millions of trip requests a day must be assigned in milliseconds
• Nearest-driver matching degrades network-wide efficiency

AI solution

• Batched window matching combines ML predictions (conversion, ETA, cancellation) with combinatorial optimization
• Reinforcement learning values each dispatch by its long-term effect on the marketplace (semi-MDP formulation)

Business value

• Operational efficiency — more completed trips, shorter waits
• Higher driver utilization

Business problem

• Demand outstrips supply by zone and minute — rain, concerts, rush hour
• Without price signals reliability collapses; without accurate upfront fares conversion drops

AI solution

• Real-time supply/demand pricing on geofenced grids from millions of location updates per second
• ML forecasts market conditions; upfront pricing predicts the fare before dispatch

Business value

• Revenue growth — the primary revenue and reliability lever
• Surge attracts supply and tempers demand

Business problem

• Every marketplace lever depends on demand and supply per geo-cell per time slice
• Under- and oversupply by zone and minute

AI solution

• Deep time-series and cohort forecasting at multiple horizons — real-time zone forecasts to 52-week planning
• Specialized airport demand models

Business value

• Operational efficiency — the foundational input to pricing, incentives and repositioning

Business problem

• Idle drivers cluster where trips just ended while nearby demand goes unserved
• Lost trips on one side, dead miles on the other

AI solution

• Forecast-driven heatmaps and RL agents recommend where drivers should go next
• Optimizes long-run marketplace balance, not just the next trip

Business value

• Operational efficiency — higher earnings, shorter waits, fewer cancellations

Business problem

• ETA errors cascade into matching, pricing, delivery windows and rider trust
• Physical routing engines are systematically wrong in real-world conditions

AI solution

• Deep-learning post-processing: an encoder with self-attention predicts the residual between routing-engine ETA and reality
• Serves every four-wheel ETA request, typically the highest-QPS model in the estate

Business value

• Customer experience — trusted ETAs
• Accuracy ripples through matching and pricing

Business problem

• Account sharing and impersonation — an unvetted person driving under a verified account
• Severe safety, trust and regulatory exposure

AI solution

• Periodic in-app selfie challenge with ML face verification against the account photo and quality screening
• Low-confidence cases go to human reviewers; facial recognition detects duplicate accounts

Business value

• Safety — table-stakes for rider trust and regulators

Business problem

• Crashes, route deviations and dangerous incidents mid-trip surfaced only if someone reported them

AI solution

• Fusion of phone GPS, accelerometer and gyroscope streams with ML classifiers detects possible crashes or long stops
• Proactive check-ins and emergency tools; ML screens out false positives such as a dropped phone

Business value

• Safety — flagship capability shaping brand trust and regulatory posture

Business problem

• Stolen cards, promo abuse, refund scams and collusion attack a low-margin marketplace
• New attacks emerge in new markets within hours

AI solution

• Layered defence — a real-time rules engine, ML risk scores and unsupervised anomaly detection
• AI-generated rules with analyst approval for early detection, scoring behavioural signals in real time

Business value

• Risk reduction — direct loss prevention on billions of transactions

Business problem

• Enormous volumes of support contacts — fare disputes, lost items, cancellations
• Human-only support is slow and expensive

AI solution

• LLM assistant resolves routine requests end-to-end with account and trip context
• Routes complex or sensitive cases to human specialists

Business value

• Cost reduction — “millions of dollars” saved
• Faster resolution for riders and drivers

Business problem

• Enormous weekly budgets on driver incentives and rider promotions
• Heuristic allocation — who, where, when, how much — wastes a large share

AI solution

• Causal/uplift models estimate each offer's incremental effect
• Knapsack/CP-SAT optimizers and budget pacing allocate spend across cities, cohorts and levers

Business value

• Revenue growth — a discretionary cost line becomes a measurable ROI engine