Reduction of empty miles
Decrease in non-productive trips through better truck allocation and more coherent mission sequencing.
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EVA
Artificial intelligence that automates truck transport of waste, materials, and containers
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Context
Thelogisticsoftransportingwaste,materials,andcontainersbytruckstilllargelyreliesonmanualplanningandphoneexchanges.Theseflowsarevariable,constrained,andfrequentlydisrupted.Thiscomplexitygeneratesinefficienciesandoperationaluncertainties.Intelligentautomationisbecomingnecessarytostabilizeandoptimizetheentiresystem.
Challenges
Automating and securing truck flows in a complex and variable environment
Truck optimization
We are developing a system capable of automatically selecting the appropriate truck type for each order (roll-off, crane, specific capacity) and assigning it based on availability, real-time location, and technical constraints. The objective is to reduce unnecessary trips and improve mission execution rates.
Dynamic recalculation
We are designing a mechanism that immediately integrates delays, cancellations, or new requests into the schedule. The system must recalculate consistent scenarios without disrupting all routes, maintaining operational stability despite unexpected events.
Automated vision
We are working on vision models capable of analyzing photos taken by drivers to detect containers and identify transported waste or materials. The objective is to strengthen proof-of-execution reliability and ensure flow compliance.
100% digital process
We are structuring an architecture connecting request, planning, field execution, and billing into a continuous data flow. This integration aims to eliminate manual exchanges, reduce administrative errors, and secure the entire logistics process.
Expected results
Stabilization of daily schedules
Ability to absorb delays, cancellations, and changes without disrupting all routes, reducing last-minute manual adjustments.
Reliable proof of execution
Automated validation of photos and field data to limit errors, disputes, and inconsistencies related to transported waste and containers.
Acceleration of administrative processes
Automation and synchronization of operational data to simplify billing and reduce processing times.
Toachievetheseobjectives,EVAcombinesalgorithmicoptimization,predictiveanalysis,andautomatedprocessingoffielddatawithinamulti-agentarchitecturecapableoforchestratingtruckflowsinrealtime.
Optimization agents
They process transport requests and operational constraints (truck type, capacities, time windows, distances, mission sequencing) to propose coherent truck assignments. They are designed to recalculate schedules in case of changes and progressively improve overall route performance.
Detection agents
They analyze photos and field data to detect containers, identify transported waste or materials, and verify proof-of-execution compliance. Their role is to strengthen flow reliability and reduce manual checks.
Coordination agent
It ensures information flow between the different agents and triggers necessary actions based on events (new order, cancellation, photo received). It guarantees overall system consistency and integration of decisions into operational planning.
Theorchestratoragentisthecoordinationcoreofthesystem.Itcentralizesevents(neworders,modifications,photos,cancellations),identifiestherelevantagents,andtriggersappropriateprocesses.Itensuressynchronizationbetweenoptimization,detection,andoperationalplanning,whileguaranteeingoverallconsistencyandtraceabilityofdecisionsmadebyNereva.
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Technology and research
From prototype to autonomous intelligence
EVA evolves step by step, moving from research to real-world application. Each year marks a key milestone toward a more intelligent, more autonomous system, fully integrated into transport and construction operations.
2025
Foundations
Implementation of the architecture and first intelligent agents.
Centralization of field data collected through Nereva.
Initial prototypes validated on real-world use cases.
2026
Field intelligence
Training detection agents to identify waste types.
Integration of models into the mobile application.
Optimization tests conducted with logistics partners.
2027
Cooperation and integration
Agent collaboration to enrich and consolidate data.
Incorporation of new variables such as weather conditions.
Deployment of EVA in real environments as an assistant.
2028
Autonomy and explainability
Agents capable of adjusting schedules in real time.
Predictive models to anticipate demand.
Explainable and traceable decisions to build trust.
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