How To Integrate Surge Pricing And Number Masking Into A Ride-Hailing Dispatch System?

The global ride-hailing market is valued at $184.49 billion in 2026 and is projected to reach $392.27 billion by 2031, according to Mordor Intelligence — and mobility startups are competing harder than ever for riders and drivers as that growth accelerates. Growth alone cannot determine a dispatch system’s profitability. Rather, it depends on how companies manage every ride, optimize supply, and incorporate variable pricing tiers within their core operational strategies — the same fundamentals covered in our guide to building taxi software like Uber. 

Whether it’s during airport rushes, bad weather, Taylor Swift’s concerts, or peak commuting hours, a ride dispatch platform encounters a demand spike that’s way more than the number of drivers available. With no real-time pricing intelligence logic, it faces longer ETAs, higher cancellations, driver dissatisfaction, and constant revenue leakage. Adjust prices aggressively, and you will end up with riders abandoning your app and preferring your competitors. 

Communication too has now become a major scaling challenge. Once established, you are most likely to expand your dispatch app’s outreach to new cities and handle increasing ride volumes. This means connecting drivers and riders directly can expose personal phone numbers. The result is increased privacy concerns, safety risks, and additional customer support overhead you didn’t prepare for. 

Given how both of these have become major operational bottlenecks, we have prepared a detailed guide to explain how to integrate surge pricing and number-masking features within your ride-hailing app. Whether it’s real-time data processing, pricing engines, or telecom API integration work, you’ll learn the role of each component in improving unit economics and marketplace scalability for any ride-hailing and taxi app development project.

What is surge pricing in a ride-hailing app?

Surge pricing is a real-time fare optimization mechanism that adjusts prices for every ride based on changes in rider demand and driver availability. For any ride-hailing startup, it acts as a marketplace balancing strategy. When designed appropriately with intelligent logic, it can help maintain reliable service availability, improve driver participation, and protect revenue during demand fluctuations.

Since the market operates in highly dynamic environments where demand changes in seconds, a fixed pricing model cannot monetize as per your target ROI. Take the example of a sudden flight arrival wave at the airport or a day of heavy rainfall. At such times, your app will receive over a thousand ride requests. Unfortunately, only a handful of drivers will be available. If you do not embed a responsive pricing mechanism, your ride-hailing app is bound to experience the following: 

• Longer pickup times
• Increased cancellations
• Frustrated riders
• Lower trip completion rates

That’s why incorporating the surge pricing logic is a crucial business decision in 2026. It lets you address these challenges by increasing a driver’s earning potential in high-demand areas. At the same time, you can regulate rider demand by adjusting ride fares based on current marketplace conditions. 

How does the surge pricing algorithm work?

Unlike traditional models relying on fixed schedules, surge pricing works dynamically to make pricing decisions based on real-time marketplace and supply-and-demand conditions. Below is an elaborative discussion on how its algorithm functions. 

Detecting real-time marketplace conditions 

The dispatch system continuously monitors how many rides are requesting trips, how many drivers are available, where they are located, and how quickly the vehicles can reach the pickup points. Suppose there’s a concert in the downtown area, due to which around 50 requests were triggered within the ride-hailing app. However, the number of drivers available remained unchanged. 

This would automatically increase the pressure on supply. Rather than blindly increasing the fare per ride, the algorithm evaluates whether the current driver capacity can realistically fulfill the upcoming requests within an acceptable waiting time. This helps in ensuring that a temporary increase in ride request volumes doesn’t always need a price surge. 

Calculating the supply-demand gap

The system then calculates how severely imbalanced the supply and demand metrics are by considering factors like:

• The number of pending ride requests
• Driver availability within the service area
• Average pickup times
• Driver acceptance behavior
• Trip completion rates

If a zone has a high number of ride requests but limited available drivers, the pricing algorithm will intervene to improve marketplace economics.

Predicting future demand patterns 

Modern surge pricing algorithms—built through the same AI development process used across forecasting-heavy platforms—are designed to forecast how supply and demand are likely to change in the future. For example, when you feed an incoming flight’s details to the algorithm, it will analyze the patterns to anticipate the expected ride request volumes and map them against available drivers near the airport location. Apart from this, you can also use seasonal trends, historical trip data, weather conditions, traffic patterns, and local events to understand where and when demand pressure is likely to occur. By doing so, you can manage the supply proactively before the event’s occurrence rather than waiting until customers complain about delays. 

Applying the optimal fare adjustment 

Once the algorithm determines that pricing intervention is required, it uses internal business logic to calculate the appropriate fare adjustment. By doing so, you can create a pricing level that will attract more drivers and keep riders willing to wait to get picked up and complete the rides at the same time. To do so, you must design the pricing engine that will consider the following:

• The intensity of the supply shortage
• Expected duration of demand increase
• Historical customer behavior
• Local market conditions
• Platform’s revenue objectives

The key here is not to use the same surge multiplier everywhere. Rather, what you can do is design location-specific pricing models that can adjust the ride fares based on the condition of each market zone.

Surge pricing vs. prime time vs. dynamic pricing—what’s the difference?

Surge pricing refers to a temporary increase in the ride fare when the demand exceeds available driver supply in real time. Prime time pricing, on the other hand, is a scheduled adjustment logic you can use during predictable high-demand periods, like on weekends or weekday commuting hours. Dynamic pricing is a much broader concept that allows you to adjust the fares continuously using real-time ride-hailing marketplace conditions, predictive analytics, and automated decision-making. 

How to build a surge pricing engine: Architecture overview 

Core inputs—demand density, driver supply, ETA elasticity

Although dozens of variables can influence the surge pricing algorithm, a standard ride-hailing dispatch system should prioritize three foundational inputs. These include the demand density, driver supply, and the ETA elasticity. When combined together, these help the algorithm determine whether surge pricing needs to be activated, and if so, then how aggressively the fares per ride should be adjusted. 

• Demand density

It measures how many rides are requested within a specific geographic zone over a defined time window. Rather than considering an entire city as one marketplace, you should divide service regions into smaller geofenced zones. This will help the algorithm determine the supply-demand imbalance more accurately, especially during peak times. Besides, you can identify localized demand spikes without affecting the pricing rates across other parts of the city. 

• Driver supply

This metric represents the number of drivers capable of accepting newly created ride requests within a specific service zone. The best approach will be to design the surge pricing algorithm in a way that it evaluates the effective supply instead of counting how many drivers are currently online. This way, you can easily distinguish between those who are available immediately and those who are: 

• Completing rides
• Moving away from the pickup zones
• Repeatedly declining ride requests

• ETA elasticity

This variable measures the sensitivity of customer demand to increasing pickup times. If the estimated timeline continues to increase, conversion rates will begin to decline as riders consider the service to be unreliable. That’s why the algorithm checks if both the demand for rides and the ETA are increasing proportionately or not. If that’s the case, the pricing engine recognizes that additional driver incentives will be necessary to re-establish the supply-demand balance. 

How to calculate the surge multiplier?

A surge multiplier is the additional fare applied to the base price when marketplace conditions signal that the current driver supply isn’t adequate to cater to the rider demand. It’s not a constant, like 2x or 3.5x. Instead, it’s calculated on the fly dynamically using multiple variables and predefined constraints associated with your business.

The simplest calculating formula you can rely on is:

Surge multiplier = Base multiplier * Demand factor * ETA factor * Supply adjustment

Here, you can define the variables as:

• Demand factor representing the intensity of ride demands within a specific geofenced service zone
• ETA factor signaling pickup times that supersede the acceptable thresholds
• Supply adjustments reflecting the availability of drivers with respect to the demand

Let’s assume a downtown area during weekday evening rush hour. Your ride-hailing dispatch platform receives 850 ride requests. But there are only 520 drivers available within that area. At the same time, the average pickup ETA has climbed to 11 minutes from the usual 4 minutes. Considering the predefined market rules, the pricing engine will then determine if the current supply is sufficient or not for maintaining service quality. 

Depending on the formula we just described above, the pricing algorithm will consider a multiplier of 1.6x for this specific downtown area. If the normal fare for a specific ride was $25, then the adjusted fare becomes $40 ($25 * 1.6).  

Coupling the pricing engine to your matching/dispatch system

Integrating both the surge pricing engine and your ride-hailing dispatch app should work two ways. Only by building consistent data pipelines between the two systems can you maintain marketplace liquidity, improve trip fulfillment, and maximize fleet utilization. So, below we have briefly explained how you should proceed with the coupling process.

• Begin by directly integrating the pricing engine into the dispatch orchestration layer. Do not treat it as a post-booking ride fare calculator. This ensures that the pricing decision can become an integral part of the driver assignment workflow. 
• Configure the dispatch system in a way that it can share live operational data with the surge pricing algorithm continuously. The primary information pieces you need to pass include ride demand, available drivers, pickup ETAs, trip cancellations, and geofence-level activity. By doing so, you can ensure that the price surges are aligned with the factors that drive dispatch decisions in the real world.
• Now connect the pricing engine back to the dispatch system. This way, the surge outputs can easily influence operational decisions and not just customer fares. Based on the marketplace conditions, your ride-hailing dispatch platform will then prioritize driver allocation, expand matching radius, trigger driver repositioning campaigns, or activate incentives to restore supply. 
• Establish a continuous feedback loop between both systems. By doing so, you can ensure that both respond proportionately to any sudden change in the marketplace conditions. 

Make sure to build an integration logic that allows the pricing engine and the dispatch system to operate on synchronized geofences. 

Why does surge pricing cause driver shortages or price oscillation?

The fare-rejection problem

The pricing engine often assumes that every dollar increase in the ride fare will produce additional driver supply without affecting rider demand materially. However, it fails to consider that both sides of the ride-hailing marketplace respond to any event simultaneously. 

When surge multipliers shoot straight through the roof all of a sudden, price-sensitive riders tend to:

• Postpone bookings 
• Compare prices across other competing platforms
• Abandon the ride request altogether

At the same time, experienced drivers are likely to:

• Reject ride requests that are paying less than the usual threshold 
• Remain offline for longer minutes
• Relocate towards airports, downtown entertainment districts, or event venues

When this happens, your dispatch engine has fewer drivers willing to accept ordinary ride requests, even when the fleet volume is sufficient within the broader network. This leads to phantom supply where drivers appear available on the platform but, in reality, are unavailable for immediate dispatch. Once booking conversions start declining and pickup ETAs continue increasing, the surge pricing algorithm raises the fare per ride again. The cycle gets reinforced instead of correcting the supply-demand gap. 

How does anticipatory pricing reduce wait-time variability? 

Instead of waiting for pickup ETAs to exceed the pre-decided service thresholds, you can integrate a predictive model to estimate where demand is likely to emerge 15-30 minutes in advance. That’s because it uses different data types to anticipate the pricing changes, including: 

• Historical booking patterns
• Flight arrival schedules
• Commuter flows
• Live event calendars
• Weather forecasts
• Traffic telemetry

This way, your ride-hailing dispatch platform can identify demand before the marketplace liquidity begins deteriorating. Driver earnings then increase gradually, or targeted drivers’ incentives get deployed before riders’ wait times increase. Investing in an AI-powered anticipatory pricing algorithm will help you in many ways, such as:

• Lower ETA variance
• Higher driver utilization
• Stronger booking conversion
• Fewer cancellation events

Is surge pricing fair? The privacy and trust problem behind dynamic pricing 

How can competing platforms reverse-engineer your pricing model?

Reverse-engineering the surge pricing model is the process of inferring how your ride-hailing dispatch platform calculates increased fares. It includes repeated observation of the pricing behavior under various marketplace conditions. Your competitors won’t have access to your technical model or business logic. Rather, they analyze demand patterns, fare estimates, driver availability, and timing to understand what rules you have built for triggering surge pricing workflows. This can create competitive intelligence risks for your mobility business, as the pricing algorithm will become your valuable proprietary asset in the long run. 

Signals that competitors analyze to reverse-engineer your pricing model

Your competitors can gradually uncover the behavior of your surge pricing model by analyzing patterns like the following:

• How pickup ETAs influence fare increases in different geofenced neighborhoods
• Whether surge pricing is triggered when demand supersedes a specific rider-to-driver ratio
• Whether airports, downtown business districts, stadiums, or convention centers consistently produce higher surge multipliers
• How quickly does surge pricing activate after a demand spike and what’s the estimated timeline before the original thresholds are restored
• Whether pricing changes differ across ride categories, like Economy, XL, Premium, or Shared

An illustrative scenario on reverse-engineering technique 

Let’s assume you and your competitor are operating in Chicago. Your competitor’s ride-hailing dispatch system continuously requests fare estimates every two minutes for trips between O’Hare International Airport and The Loop during the Monday evening rush hour. 

However, over a period of time, they observe that your dispatch engine increases the fares when the average pickup ETA exceeds 8 minutes, and driver availability drops sharply around the airport zone. They also detected that your surge pricing algorithm remained active for 20-25 minutes before returning to the baseline. 

Using these insights, your competitor can modify their dispatch platform so that targeted driver incentives get activated 15 minutes before your expected surge window. This way, they can encourage more drivers to move towards O’Hare before demand spikes. As a result, they will maintain lower fares, attract additional riders, and capture trips that otherwise would have logged into your system. 

What is differential privacy in dynamic pricing?

Differential privacy is a security technique with which you can ensure your dynamic pricing model learns from aggregated marketplace behavior and does not rely on individual user information. Usually, most ride-hailing dispatch systems use a pricing engine that consumes millions of data points from parameters like the following:

• Ride requests
• GPS locations
• Pickup and drop-off patterns
• Trip frequencies
• Payment history
• Driver availability

If no proper privacy safeguards are in place, it can expose sensitive information to the outside world. It can be the rider’s house address, payment information, or daily commute habits. Cybersecurity and regulatory violation risks become inevitable. That’s why you should plan for a differential privacy mechanism so that your pricing engine can easily flag demand trends and optimize surge decisions without relying on PII. 

Key features of differential privacy and their benefits 

• Aggregate data analysis allows the model to use marketplace-wide demand and supply trends, rather than studying individual rider or driver information, to make pricing decisions
• Statistical noise injection helps anonymize individual trip information and preserve analytical accuracy
• Protection against re-identification prevents attackers or your competitors from reconstructing individuals’ behavior from aggregated pricing data
• Privacy-preserving AI training enables machine learning models to improve rider demand forecasting and surge pricing without exposing PII
• Geofence-level intelligence generates pricing insights at the neighborhood or service-zone level 

What is number masking (call masking) in ride-hailing apps?

Number or call masking is a privacy feature embedded within a ride-hailing dispatch platform to ensure drivers and riders communicate without revealing personal phone numbers. This means that if a rider wants to ask the driver’s exact location after the ride gets accepted or vice versa, neither of them will have to call directly or send an SMS. Instead, your dispatch platform will assign a virtual phone number, thereby securely routing the communication through a cloud telephony infrastructure. 

How does masked calling work?

Masking the call is done by assigning a dummy phone number to both the rider’s and driver’s actual contact information. Below, we have explained the working mechanism in detail to help you understand how the feature plays a crucial role in PII protection. 

• When your dispatch engine successfully matches a rider with a driver, the platform automatically creates a temporary communication session linked to that specific trip.
• A cloud telephony provider, like Twilio, Vonage, or Sinch, assigns one or more virtual phone numbers that function as intermediaries between users.
• When either party initiates a call or sends an SMS, the request is first routed through the virtual phone number. 
• The communication platform translates the virtual number to the recipient’s actual number behind the scenes, thereby delivering a call or a message without exposing sensitive information.
• Once the ride is completed, the virtual number gets disconnected automatically, preventing future calls or messages between the users. 

Why do Uber and Lyft use number masking instead of real phone numbers?

Uber and Lyft — both featured in our roundup of the top taxi booking apps in the USA — use a number masking technique within their ride-hailing platform to protect user privacy, maintain trust, and safely operate a two-sided marketplace. It prevents both riders and drivers from contacting one another after the ride is marked completed in the system. This further reduces incidents like:

• Harassment
• Stalking
• Unsolicited marketing
• Attempts to arrange off-platform rides

In addition, this feature also ensures every communication detail remains within the dispatch platform. As a result, these two mobility giants can maintain transaction integrity and reduce revenue leakage. 

How to integrate call masking into a ride-hailing app?

Twilio Proxy vs Twilio Conversations API: Which one to use

Twilio Proxy is purpose-built for anonymized voice calls and SMS between a rider and a driver. Twilio Conversations API, on the other hand, is designed for a persistent, multi-party messaging experience. That’s why Twilio Proxy is a much better choice to ensure temporary interactions can continue without any hindrance during every trip. 

Step-by-step: Implementing masked calling and SMS

Step 1: Provision a pool of virtual phone numbers

Start by purchasing or leasing a pool of local or toll-free virtual numbers from a cloud communication provider. This way, you won’t have to assign a dedicated number to each rider or driver. That’s because these numbers will act as temporary communication bridges and can be reused across thousands of trips. 

Step 2: Create a temporary communication session

When the dispatch engine matches the rider with a driver successfully, it will create a temporary communication session for that trip. Make sure to map both users to an available virtual number so that you won’t have to worry about exposing their original contact information to the external world.

Step 3: Route calls and SMS through the virtual phone number

Route every test message or call through the virtual assistant number from the pool you purchased or leased. Once you do so, the cloud telephony platform will automatically establish a forward communication link to the intended recipient. Both users’ actual phone numbers are thus masked throughout the conversation.

Step 4: Synchronize communication session with ride status

Synchronize every communication session with your ride-hailing dispatch engine. If the driver gets reassigned, the rider cancels the trip request, or a new driver accepts the request, your platform will automatically update or recreate the masking session. 

Step 5: Log communication metadata for safety and support

Do not store call recordings in your dispatch system as a default feature. Instead, design a logger that will store communication metadata in the data repository, including details like timestamp, delivery status, and call duration. These records will further help you in fraud investigations, dispute resolution, lost-items claims, and safety reviews.

Step 6: Expire the session and recycle the virtual number

You also need to ensure that your ride-hailing dispatch platform kills the communication room instantly once the ride is cancelled or completed. This will ensure the virtual number becomes available for reuse within the pool.

How long should a masked number stay active?

A masked number should remain active and usable only for the duration necessary to complete the trip and resolve immediate post-ride issues. If you do not put any auto-termination logic, the session will continue, thereby increasing privacy risks. Similarly, expiring the communication pipeline too early will disrupt the link between the rider and the driver much before the trip is completed.

One of the best approaches you can follow is a time-boxed session model. Here, the virtual number gets activated when a ride gets accepted within your ride-hailing dispatch system and becomes expired after a pre-defined period. Rather than syncing the expiry logic with ride completion, allow a grace period of 30 to 60 minutes. This will help you handle post-trip communication legitimately, like allowing the rider to coordinate with the driver about a lost item. 

After the session expires, recycle the virtual number by returning it to the provider’s pool for future trips. Make sure that before you reuse, all the previous rider-driver mappings are removed permanently.

Common pitfalls when integrating surge pricing and number masking 

Even after building a mature ride-hailing dispatch system, integrating call masking and surge pricing features can introduce both operational and compliance challenges. These include:

• Number pool exhaustion: During peak periods, limited virtual numbers will prevent riders and drivers from connecting with one another. This isn’t unique to ride-hailing—the same recycling logic applies to logistics and delivery app development, so implement efficient number recycling regardless of vertical.
• Regional surge caps: U.S. states and cities can restrict surge pricing during emergencies or public crises, which is why you should configure pricing rules as per the jurisdiction where your business will operate.
• Algorithmic fairness disputes: Inconsistent fares for similar trips can trigger customer complaints, damage trust, and invite regulatory scrutiny over pricing transparency.
• Pricing recalculation latency: Slow fare updates can cause stale prices, mismatched driver assignments, higher cancellations, and inaccurate ETAs during demand spikes.
• Communication session failures: Poor synchronization between the dispatch system and the masking service can cause the virtual numbers to expire prematurely or keep them active even after the trip ends. 

Is surge pricing at legal risk in 2026? The surveillance pricing distinction that matters

There’s real regulatory movement in 2026, but it’s aimed at something more specific than surge pricing itself—and understanding the distinction matters for how you architect your pricing engine.

The trend is “surveillance pricing” legislation: laws that restrict businesses from using an individual’s personal data—browsing history, device type, location history, and purchase patterns—to charge that specific person a different price than everyone else for the same service. New York passed legislation in this area in June 2026—and if your dispatch platform operates in that market, working with a New York-based development team that tracks these rules directly can save you from finding out about a compliance gap after launch.

Critically, this is not a ban on traditional surge pricing. Supply-and-demand-driven fare adjustment — the kind your dispatch engine calculates based on aggregate ride requests, driver availability, and pickup ETAs in a zone — is explicitly not what this legislation targets. Uber and Lyft continue operating standard surge pricing under these laws. What’s restricted is pricing that varies by individual rider, based on who they are or what’s known about them, rather than by conditions in the market.

This is exactly why the differential privacy approach covered earlier in this guide isn’t just a technical best practice — it’s increasingly a compliance requirement. A pricing engine built to reason only about aggregate zone-level demand and supply (never individual rider profiles, device signals, or behavioral history) is already positioned correctly for this regulatory direction. A pricing engine that incorporates any per-rider personalization signal — even something as seemingly harmless as “this rider has historically paid surge fares without cancelling” — is the exact pattern regulators are now targeting, and retrofitting that out of a production system is far more expensive than not building it in the first place.

Practical takeaway: architect your surge multiplier from zone-level, aggregate inputs only (the demand density, driver supply, and ETA elasticity variables already covered in this guide), and treat any request to add individual rider data into the pricing formula as a regulatory red flag, not just a UX judgment call.

Cost to build surge pricing and number masking features 

The cost to integrate surge pricing and number masking features within your ride-hailing dispatch system in 2026 ranges between $40K and $120K—on top of the base cost to develop a taxi booking app itself.

Startups validating demand first often start with an MVP development scope limited to rule-based surge pricing before adding AI forecasting. It depends on the sophistication of the pricing algorithm, dispatch integration complexity, cloud telephony setup, scalability requirements, and compliance needs. 

How does GMTA help you build trust-first dispatch systems?

Building a successful ride-hailing and taxi app requires a development approach that can help build trust with every trip. Our team will build scalable dispatch solutions that can balance operational performance with user trust, backed by the same custom software development approach we bring to every enterprise-grade build. That’s why at GMTA Software, we design these systems in a way that pricing transparency, rider privacy, and marketplace efficiency can work together to deliver a better user experience. Whether you are launching a new mobility startup in the US or modernizing an existing cab platform, our team will build scalable dispatch solutions that can balance operational performance with user trust.

Our expertise with trust-first dispatch systems covers multiple domains, including:

• AI-powered surge pricing
• Real-time dispatch optimization
• Twilio-based number masking
• Predictive demand forecasting
• Geofencing
• Driver-rider matching
• Cloud-native ride-hailing architecture 

Every solution is tailored to your specific business model so that the dispatch system can reduce wait times, improve marketplace liquidity, protect user privacy, and maximize long-term profitability — the same advanced-tier approach we took when building a taxi booking app like Yango Ride for the Dubai market. Whether you want to integrate an advanced surge pricing algorithm, secure rider-driver communication, or develop a complete Uber-like app, our mobile experts will deliver enterprise-grade solutions for your US business. 

FAQs

What is surge pricing in ride-hailing apps?

Surge pricing is a dynamic fare adjustment system that increases ride prices when local demand exceeds driver supply. It operates at the geofence level, instead of considering an entire city. The pricing engine evaluates demand density, online drivers, pickup ETA, trip acceptance rates, cancellations, traffic conditions, and historical demand patterns to calculate the surge multiplier. It helps businesses restore marketplace liquidity, reduce rider wait times, and encourage drivers — the same earning dynamic covered in our guide to driving apps that help drivers make money — to move toward high-demand areas easily.

How is the surge multiplier calculated?

Rather than using a fixed formula, most pricing engines calculate the surge multiplier based on real-time marketplace data. It continuously analyzes rider demand, active driver supply, ETA elasticity, booking velocity, acceptance rates, and localized traffic conditions. If you want to build an advanced ride-hailing platform, you can even incorporate an AI model that forecasts demand 15-30 minutes ahead using historical trip data, weather forecasts, and local events.

Why does surge pricing cause driver shortages?

Surge pricing alone doesn’t create driver shortages. Rather, it’s a poorly calibrated pricing algorithm that forces drivers to reject nearby trips or reposition towards anticipated surge hotspots, leaving surrounding areas underserved. This leads to phantom supply, a condition where drivers appear online but are temporarily unavailable for dispatch. The result is longer pickup ETAs, lower trip fulfillment rates, and recurring price oscillations.

Is surge pricing fair to riders?

Yes, surge pricing is fair to the riders only if it can reflect genuine marketplace conditions and follow transparent pricing rules. Fairness will depend on consistency, explainability, and proportional fare adjustments rather than the price increase alone. You can configure your ride-hailing platform in a way that it displays surge pricing before booking, applies localized geofence-based pricing, and enforces pricing guardrails during emergencies.

What is call masking, and why do ride-hailing apps use it?

Call masking replaces riders’ and drivers’ personal phone numbers with temporary virtual numbers during an active ride. Both parties, thus, can communicate normally without seeing each other’s actual contact details. You can embed this feature in your ride-hailing dispatch app to protect user privacy, prevent off-platform ride arrangements, reduce harassment risks, and maintain secure communication records. 

How long do masked phone numbers stay active?

Masked phone numbers can stay active from the moment a ride is accepted until 30-60 minutes after the trip ends. The grace period will allow riders and drivers to coordinate missed pickups, return lost belongings, or resolve post-trip issues without exposing contact information. 

How much does it cost to add surge pricing to a taxi app?

Adding a surge pricing engine to an existing ride-hailing dispatch system costs around $20K to $45K. An AI-powered predictive model can drive the investments to $30K to $70K, as you need more engineering efforts for ML algorithms, demand forecasting, and continuous model optimization.

Gaurav Gautam

Head of Delivery

He is the Head of Delivery at GMTA Software, where he leads AI and software architecture decisions for the firm’s enterprise clients. He oversees end-to-end product delivery across mobile and web platforms. With 14+ years of hands-on engineering experience, he has taken products from architecture design through deployment for clients in on-demand services, enterprise SaaS, and HIPAA-regulated healthcare—working directly with US, UK, and Singapore-based teams. At GMTA, he is responsible for solution architecture, technical planning, team execution, infrastructure decisions, and quality delivery across iOS, Android, and web stacks. His writing draws on real delivery experience: what breaks at scale, where architecture decisions have long-term cost consequences, and how technical tradeoffs translate into business outcomes.