Problem
Logistics operations still rely on fragmented data, disconnected systems, and human intervention to understand what's happening with cargo and assets during transportation. When there is a delay, deviation, loss of signal, or change in operation, the analyst must cross information from different systems to reconstruct the situation and decide what to do. This reduces predictability, increases costs, and delays response to risks. Fenrir solves this problem by creating an operational intelligence layer capable of integrating different data sources, maintaining a continuous representation of the state of assets, predicting trajectories, identifying anomalies, and automating operational responses.
Solution
Fenrir creates a layer of operational intelligence on top of existing systems and data sources. The platform integrates planning data, positioning, geographical and road context, operational events and, progressively, terrestrial and satellite sensors and other multimodal sources. This data is normalized and processed by its own association, merger, state estimation, uncertainty analysis, prediction, and anomaly detection core. The result is a continuous representation of the asset and the operation, allowing to predict ETA, identify deviations and risks, rebuild operations, and transform events into automated actions or communications.
Business model
The model combines B2B contracts for platform, deployment, integration, and data/service consumption. The business strategy begins with low-risk POCs to validate the technology and operational impact, evolving to deployment and continuous operation at scale. In road operation, the current POC is structured in R$ 120 thousand, while subsequent expansion and multimodal tracking stages have higher estimated values. The final ticket varies according to the number of assets, coverage, data sources, integrations, and level of autonomy, with the potential for recurring contracts of greater value as the operation scales.
Market
Fenrir operates at the intersection of global logistics, supply chain visibility, asset tracking, operational intelligence, and risk management markets. The addressable market includes logistics operators, shippers, ports, insurance companies, industry, foreign trade, and government applications. As a reference, the global cargo insurance market alone was estimated at approximately US$ 29,3 billions in 2024, while the global cold chain generates hundreds of billions of dollars annually. Fenrir's potential market is therefore transversal to multiple logistics and risk management segments, with progressive expansion from terrestrial tracking to multimodal and global.
Competitors
The main competitors vary by segment, including global visibility and tracking platforms such as FourKites, MarineTraffic, Orbcomm, CargoSmart, and VesselTrack, as well as IoT and asset tracking solutions such as Nexxiot. In road transport, we also compete indirectly with TMS, telemetry platforms, and traditional tracking systems. The difference is that these players normally focus on a specific modality, data source, or layer of the operation. Fenrir seeks to act above these sources, integrating heterogeneous data and applying fusion, inference, forecasting, and risk analysis in a single operational layer.
Competitive differentiation
Fenrir does not rely on a single positioning source nor does it treat tracking as simple coordinate collection. Its differential is to combine operational data, geographical context, behavior, physical constraints, and multiple sources of observation to continuously infer the most probable state of an asset and its degree of trust. The architecture was designed to operate even when a source presents noise, unavailability, or inconsistency, adjusting weights, uncertainty, and assumptions. This allows you to evolve from “where is the asset? ” for “what is the most likely state, what should happen next, and what is the operational risk? ”.
Entry barrier
The main barrier lies in the combination of intellectual property, architecture, data, and accumulated knowledge. The Fenrir core incorporates its own association algorithms, multimodal fusion, uncertainty modeling, physical and behavioral coherence, and motion prediction. As the platform operates, it also accumulates historical data, behavioral patterns, events, and relationships between different sources and contexts. This combination creates a learning cycle that is difficult to reproduce with conventional software alone or access to the same APIs. In addition, the architecture was designed to integrate heterogeneous sources and operate resiliently in degraded environments, progressively expanding the advantage as coverage and volume of data increase.
Traction
Fenrir already has structured POCs with large companies, including an operational front with BidFood, with a defined scope for validation in a real environment. The POC provides asset monitoring, continuous ETA forecasting, identification of deviations and anomalies, communication automation, auditing, and integration with existing systems. The architecture also has a technical demonstration of tracking and multisensory fusion. The company also has a commercial relationship and interest from potential customers in different logistics segments, in addition to initiatives with ports and multimodal applications.