REVEAL PLANNER + MULTI-DEPOT OPTIMIZATION
Verizon Connect’s Reveal route planner is a drag-and-drop dispatch surface with cloud optimization assistance. The product page documents time windows, vehicle capacity, driver certifications and overnight trips as supported constraints; multi-depot is not surfaced. Service and distribution organizations with three or four regional depots, contractors with branch networks, and recurring service programs across regions need something different: an optimizer that treats all depots as one problem and rebalances work between them under skill, capacity, time-window and SLA constraints, as a single solver input. eLogii owns that decision, custom-integrated against the Reveal API.
Our drag-and-drop route planner lets you assign and reassign jobs. RouteCloud analyzes time windows, vehicle capacity, driver certifications and accommodation for overnight trips when building each route.
From verizonconnect.com/solutions/route-planning-software. Multi-depot is not listed alongside the supported constraints on the public route planning page. Cross-depot rebalancing as a single optimization input is not described as a lead surface for the Reveal route planner. Verified June 2026.
The Reveal route planning page documents a planner-facing tool with cloud optimization assistance (RouteCloud). The named constraints span time windows, vehicle capacity, driver certifications and overnight trips, plus truck-legal routing. Stops, drivers and vehicles can be associated with specific depots; the planner shows the day’s assigned work for a depot or filter set on a calendar and map view.
What the Reveal route planner does not surface as a lead capability is cross-depot optimization as a first-class input: a single run that takes all depots as inputs, rebalances stops across them under shared constraints, and outputs assignments that may move work between depots. Multi-depot is not listed as a documented constraint on the public product page. That decision layer is a different shape of product.
Three concrete patterns make the case for multi-depot as a single optimization input:
In each case, the right answer is decided by the optimizer, not the planner. The dispatcher steers the rules and signs off on the run.
A regional distribution operation running four depots. Eighty drivers in the field. Daily delivery routes for recurring commercial accounts; reactive same-day urgent deliveries with SLA terms; parts pickup between depots when a route needs stock from elsewhere in the network. Verizon Connect handles the telematics cleanly: GPS trail on every vehicle, ELD compliance, Coach driver scoring, dashcam record on the heaviest vehicles.
The friction sits at the depot interface. Depot A runs hot most weeks while depot D runs underused; capacity drift accumulates because no one has time to look across depots in real time, and the Reveal route planner is configured per-depot so it can’t see the imbalance. Routes that should start at depot A, pick up at depot B, then continue to customers run as two separate trips because the planner can only see one depot view at a time. Drive time creeps. The operations director can see the problem on the end-of-week dashboard, but no one is solving it on Tuesday morning. A cross-depot optimization run takes all four depots as one input, drops drive time around 15–20% across the network, and rebalances capacity within roughly +/- 5%. Verizon Connect continues to own the GPS, ELD, behavior and dashcam record; what changes is which depot does which stop.
The workaround is the planner. The planner assigns stops to depots based on rules of thumb (closest, skill match, current capacity), then the Reveal route planner (with its cloud optimization assistance) routes within each depot. At small numbers of depots and stable work mix, this is fine. The friction shows up at scale: capacity at one depot stays underused while another runs hot; drive time grows because work isn’t reassigned to the depot that should actually do it; SLA hits depend on the planner spotting the constraint in time; the Reveal planner is hard to use cleanly across multiple depots at once because multi-depot is not a documented constraint on the public page. The longer the operation runs without a cross-depot optimization step, the more drift accumulates.
Multi-depot is a first-class input. Each depot has a location, opening hours, vehicle pool, skill mix, capacity and (where it matters) shift patterns. Drivers are associated with home depots but the optimizer can reassign work between depots when constraints allow. A single run produces one consistent plan.
Verizon Connect stays in place as the system of record for GPS, ELD, driver behavior and dashcam record. The operational system of record (FSM or ERP) stays in place for the work record. The connector is custom-built against both APIs; there is no published eLogii to Verizon Connect integration on either side. An iPaaS (Workato, MuleSoft) is a common middleware choice.
Most teams complete the connector build in 3 to 5 weeks. Typical first wave: the regional book where cross-depot rebalancing is the dominant planning task, or the service arm running parallel to the contract-maintenance business.
30-minute custom simulation with your actual depots, drivers, vehicles and recurring service programs. Projected savings in drive time, capacity utilization and SLA hit rate.
Verizon Connect supports planning per depot via the Reveal route planning module: stops, vehicles and drivers can be associated with specific depots, and the planner shows the day’s routes on a calendar and map. The Reveal route planning page documents time windows, vehicle capacity, driver certifications and overnight trips as supported constraints. Multi-depot is not explicitly listed as a supported constraint on the public product page. What it does not position as a lead surface is cross-depot optimization as a first-class input: a single optimization run that considers all depots together, rebalances stops across depots under skill and capacity constraints, and produces assignments that may move stops between depots when the math says they should move. That is the decision layer eLogii adds.
Three concrete patterns. First, regional service organizations with three or four depots: the optimizer needs to decide which depot a stop goes to based on skill availability, drive time, current capacity and SLA, not just which depot is closest. Second, service arms with branch networks: routes start and end at different depots in the same plan, and consolidation across branches drops drive time materially. Third, hub-and-spoke programs where drivers can start from one depot, pick up parts from another, then continue to customer sites. None of these are solvable as the sum of single-depot optimizations.
The workaround is planner-driven: the planner assigns work to each depot based on rules of thumb, then the Reveal route planner (with its cloud optimization assistance) routes within each depot. At small numbers of depots and stable work mix, this is fine. At more depots, fluctuating work mix, or interacting skills and SLAs, the planner has to hold the cross-depot picture in their head. The Reveal planner is hard to use cleanly across multiple depots at once because multi-depot is not a documented constraint on the planning page. Capacity at one depot stays underused while another runs hot. Drive time grows because work isn’t reassigned to the depot that should actually do it. The longer the operation runs, the more this drift accumulates.
Multi-depot is a first-class input to the optimizer. Each depot has a location, opening hours, vehicle pool, skill mix and capacity. Drivers are associated with home depots but the optimizer can reassign work between depots when constraints allow. A single optimization run produces routes that may start at one depot, end at another, or pick up at a third. The output is one consistent plan, not the sum of per-depot plans. Both the Default engine and the Advanced engine handle multi-depot inputs.
Custom integration against the Reveal API and the operational system of record (FSM or ERP). eLogii reads stops, drivers, vehicles, depots and skills from the operational systems. The optimization run treats all depots as a single problem. Routes and ETAs are written back, mapped to the appropriate depots and drivers; the driver opens Reveal Driver or Verizon Connect Navigation in the cab and follows the assigned route. Verizon Connect captures the in-cab GPS, ELD, behavior and dashcam stream. Typical connector build: 3 to 5 weeks.
Last updated: June 2026. Verizon Connect scope is drawn from the Verizon Connect route planning page and Reveal apps page. eLogii capabilities documented at elogiiapidocs.apidog.io.
Custom simulation
A 30-minute working session with our solutions team. We take a sample of your real jobs, depots, vehicles and SLAs, run them through the eLogii engine, and show you the projected delta against how you plan today. No slides, no generic benchmarks.
