Kartik Express Deliveries operates an eight-rider electric scooter fleet out of Baner in Pune. They joined the BatteryPool pilot in October 2025, about three weeks into our Q4 launch. Before onboarding, their operations lead tracked rider downtime manually in a spreadsheet. Dead-battery incidents, defined as a rider stopping their shift early or losing more than 30 minutes to an unplanned charging stop, were averaging close to two per day across the fleet.
That number sounds manageable until you account for the second-order effects. A rider who stops early means reassigned deliveries or failed SLA commitments. A rider stuck at a charging point for 45-50 minutes during peak hours is unavailable during the window when order density is highest. The direct cost of the dead-battery incident understates the total operational impact.
The Baseline: What We Measured Before
We conducted a two-week baseline measurement period before Kartik Express formally started using BatteryPool stations. The goal was to establish a clear pre-intervention benchmark for their specific corridors: Baner, Balewadi, and the eastern stretch toward Aundh.
During the baseline period, the fleet averaged 1.8 dead-battery incidents per day. Average incident downtime was 62 minutes per event, because riders were typically hunting for an available charger, not just waiting at one. Their primary charging point, a commercial charger at a petrol station on the Baner main road, had a queue during peak hours. Riders who arrived at 12:30pm were sometimes waiting until 1:45pm for an open charger, by which point the lunch delivery window had mostly closed.
Total energy-related downtime across the eight-rider fleet ran approximately 110 minutes per day. Roughly 14 minutes per rider per shift, which on a 10-hour shift translates to about 2.3% of total available rider time spent on unplanned energy stops.
The First Month: Onboarding and Corridor Coverage
Kartik Express went live on BatteryPool in late October 2025, using three of our then-12 stations in the Baner and Balewadi corridors. The first two weeks showed only moderate improvement: dead-battery incidents dropped from 1.8 to around 1.3 per day. The issue was familiarity. Riders were accustomed to their old charging habits and did not always check app-indicated station inventory before heading to a station. A few early visits found stations with all packs in the charging cycle, not ready to swap.
We worked with their operations lead to adjust the fleet's routing and brief riders on how to read station availability in the app before heading to a swap point. By week three, rider behavior had adjusted. Unplanned downtime incidents dropped sharply in the final two weeks of November. The pattern was consistent: riders checking station inventory 10-15 minutes before their predicted need, adjusting their route accordingly, and arriving at a station with a charged pack ready.
Three Months In: The Q1 2026 Numbers
By January 2026, Kartik Express had been on the BatteryPool network for three full months. Dead-battery incidents had dropped to approximately 0.9 per day across the fleet, down from 1.8 at baseline, a reduction of roughly half. Average incident downtime when events did occur also fell, from 62 minutes to around 28 minutes, because riders now had a swap station as a fallback rather than a charger queue.
Total energy-related downtime across the fleet was running around 55 minutes per day in January 2026, compared to 110 minutes at baseline. This is the number that matters to fleet economics because it translates directly to deliverable capacity: a fleet that reclaims 55 minutes per day of previously lost rider time can handle more orders with the same headcount, or can run a leaner fleet to cover the same order volume.
We frame these numbers as pilot-period data from a single fleet in specific Pune corridors, not as a universal outcome. The corridors Kartik Express operates in had good BatteryPool station density relative to their rider count. A fleet operating in a corridor with sparser station coverage would see different results.
What Made the Difference: Not Just Speed
The obvious factor in uptime improvement is the 90-second swap versus the 45-minute charge wait. But the more durable factor is predictability. Before BatteryPool, their riders could not know whether a charger would be available when they needed it. After, they could check station inventory before leaving a delivery stop and make routing decisions accordingly. The uncertainty cost, the mental overhead of not knowing whether your next energy stop would take 10 minutes or 90, was eliminated even when an incident did occur.
Their operations lead made an observation in February 2026 that stuck with us: "The riders' confidence changed before the numbers changed." Riders who knew a swap station was nearby and stocked behaved differently than riders who were always partially anxious about range. The measurable outcome in the incident log was downstream of a behavioral shift that happened earlier.
Limitations of This Case Study
Kartik Express is a small fleet in a corridor where we had concentrated our initial station placement. Their baseline incident rate was relatively high, which creates more room for improvement than a fleet that was already better-managed. The data covers Q4 2025 and into Q1 2026, which is a single season. We do not have a full annual cycle to assess whether summer heat or monsoon conditions change the pattern.
We also cannot isolate the BatteryPool network effect from other operational changes Kartik Express made during the same period. They also adjusted their dispatch software and changed some rider shift start times during this period. The overall improvement in outcomes is real, but claiming a precise attribution percentage to BatteryPool specifically would overstate what the data supports.
What the data does support clearly: a transition from charging-dependent operations to swap-network operations in well-covered corridors reduced energy-related downtime substantially for this fleet. That is the finding we are willing to state, and it is the finding that informs how we think about corridor coverage priorities as we expand.