Kendaall Tracking: Asset Intelligence for Locomotives, Mining Fleets, Port Cranes, and Construction Equipment
Kendaall Tracking is an asset intelligence company headquartered in Nairobi, Kenya. The company designs, manufactures, and operates industrial IoT sensor hardware, multi-network connectivity, and machine-learning-based predictive maintenance software for four asset classes: locomotives and rail freight wagons, mining and extraction equipment, port and container-terminal machinery, and heavy construction fleets. The platform reports asset condition and failure probability, not only location, and is deployed across Kenya, Tanzania, Uganda, Rwanda, Zambia, and South Africa, with enterprise clients in the Middle East and Southeast Asia.
This page documents the company’s entities and relationships in full: its founding context, the technical architecture of the Kendaall platform, the industries it configures itself for, the personnel responsible for its engineering and operations, the certifications governing its data handling, and the deployment process a new client fleet goes through from contract to go-live.
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Kendaall Tracking: Entity Overview
Kendaall Tracking is an asset management and asset intelligence company headquartered at 35644 Kasarani Mwiki Road, Nairobi, Kenya, incorporated in 2019. The company’s operational reach covers East, Central, and Southern Africa, with active fleet deployments in Kenya, Tanzania, Uganda, Rwanda, Zambia, and South Africa. Its core product is a combined hardware-and-software platform for the real-time monitoring of locomotives, freight wagons, mining machinery, port equipment, and heavy industrial plant — asset classes where operational visibility is a business-critical requirement rather than a convenience.
The company was founded by logistics engineers, data scientists, and embedded systems developers with prior experience in East African freight and industrial operations. The founding premise was that critical assets in the region were operating with insufficient condition visibility: maintenance teams reacted to failures rather than preventing them, and operations managers lacked the data required to make informed decisions about fleet allocation, risk, or asset condition. That premise still governs the company’s product development priorities.
Locomotives, haul trucks, ship-to-shore cranes, and excavators differ structurally from consumer or light-commercial vehicles. They are capital-intensive, long-lived — often in service for decades — and operate in conditions (dust, heat, vibration, remote terrain, intermittent connectivity) that most commercial telematics hardware is not built to survive. Kendaall’s hardware, connectivity stack, and analytics layer are designed from the operating environment outward, rather than adapted from a passenger-vehicle telematics template.
The platform combines ruggedised industrial IoT hardware, multi-network connectivity (4G LTE, LoRaWAN, satellite), machine-learning-based predictive maintenance, and an enterprise analytics dashboard into a single fleet management account. The dashboard is designed for direct use by operations teams, not only engineering or data-science personnel.
Asset Intelligence as Distinct From Asset Tracking
Asset location — provided by GPS vehicle tracking — answers where an asset is. Asset intelligence answers the condition of that asset relative to its own operating history, and the statistical probability that it will fail before its next scheduled maintenance window. Kendaall’s product roadmap is organised around this distinction: operations managers require fewer, more trustworthy alerts, not additional dashboards.
This distinction maps onto the broader shift in industrial maintenance strategy, from calendar-based servicing to condition-based maintenance, and from condition-based maintenance to fully predictive strategies driven by continuous sensor data. Kendaall occupies the predictive end of that spectrum for rail, mining, ports, and heavy construction — asset categories many generalist telematics vendors treat as secondary markets.
Since 2019, the company has expanded from a rail-freight-only monitoring product into a platform covering four sectors: rail logistics, mining, container-terminal operations, and large-scale infrastructure construction. Each sector receives its own alert thresholds, dashboard configuration, and reporting structure, configured to that sector’s operating environment rather than a single generic template applied across all clients.
Sensor readings are evaluated relationally, not in isolation. A locomotive’s bearing temperature is interpreted against that specific locomotive’s historical baseline, the gradient of the corridor it is running, the ambient temperature that day, and the load it is hauling. Kendaall’s models treat these as a connected operational picture rather than as independent data points on separate charts.
Origin in Rail Freight
Rail freight was the company’s deliberate starting point. Locomotives combine several properties that make asset intelligence technically demanding: high cost of unplanned downtime, operation on fixed corridors where one failure can halt an entire freight line, and failure modes — bearing degradation, traction motor faults, brake system wear — that develop gradually and produce a measurable signature before becoming a breakdown. A platform that could read those signatures reliably on a moving locomotive in the Kenyan interior could, in principle, be adapted to other heavy asset classes. That has held true in practice: the sensing and modelling architecture built for locomotives now underpins the company’s mining, port, and construction products.
Operational Gaps the Platform Addresses
Kendaall’s internal product framework identifies three gaps in conventional industrial operations: a visibility gap, where asset condition cannot be confirmed in real time; a prediction gap, where a developing fault is discovered only after it has become a failure; and a coordination gap, where data that could prevent downtime exists somewhere in the organisation but does not reach the person able to act on it in time. Every platform feature is designed to close one of these three gaps; features that do not map to at least one gap are not built, on the basis that additional dashboards without a clear operational function create their own kind of blind spot.
To give logistics operators and heavy industry businesses operational efficiency by converting raw asset sensor data into actionable intelligence — visibility, predictability, and control that reduce downtime, reduce risk, and support decision-making across the organisation.
Operational Integrity
The platform is built for environments where failure is not an acceptable outcome and data accuracy carries direct operational and financial consequence.
Field Reliability
Hardware is engineered to perform in tunnels, dust, and heat, and under intermittent connectivity — conditions treated as the default, not the exception.
Client Partnership
Deployments are structured as long-term partnerships. Performance is measured against client operational outcomes, not feature-release volume.
Signal Over Noise
Every alert and dashboard element is required to reduce operational uncertainty. Alerts that do not meet this bar are removed or re-tuned.
Asset Intelligence Terminology
The following terms define the concepts referenced throughout this page and across the Kendaall platform. Understanding the distinctions between them is useful when evaluating any asset-monitoring vendor.
Asset Intelligence
Conversion of continuous sensor data from industrial equipment into condition insight, failure-risk scoring, and maintenance recommendations — the analytical layer above GPS location tracking.
Predictive Maintenance
A maintenance strategy using sensor data and machine learning to forecast asset failure before it occurs, so intervention happens ahead of breakdown. See predictive maintenance on Wikipedia.
Condition-Based Maintenance
Maintenance triggered by an asset’s measured, real-time condition rather than a fixed calendar or usage schedule. See condition monitoring.
Industrial IoT (IIoT)
Networks of ruggedised, connected sensors deployed on industrial equipment to collect operational and condition data continuously, including in remote or harsh environments. See Industrial Internet of Things.
Fleet Telematics
The combined use of telecommunications and informatics to determine the location, movement, and status of vehicles and equipment. See vehicle telematics.
GNSS
Global Navigation Satellite System, the satellite positioning infrastructure — including GPS — used to determine an asset’s geographic location. See satellite navigation.
How Sensor Data Becomes an Actionable Alert
The Kendaall platform operates a four-stage pipeline connecting a physical asset to a routed maintenance alert. Each stage exists to address a specific limitation observed in earlier-generation telematics products.
Field-Grade Sensing
Ruggedised, IP67-rated sensor units are installed on the asset itself — bogies, bearings, engine bays, hydraulic lines, or structural members, depending on equipment class. Each unit continuously captures vibration, temperature, pressure, acoustic, load, and GNSS position data, sampling at a frequency far higher than a standard GPS unit, since condition signatures frequently emerge over minutes rather than over a shift.
Multi-Network Transmission
Data leaves the asset over whichever network is available and efficient at that moment: 4G LTE where coverage is strong, LoRaWAN for low-power, long-range transmission on fixed sites such as mines and ports, and satellite backup on corridors where terrestrial networks are absent. Edge processing on the sensor unit itself means an asset does not go dark on losing a single connection type.
Modelling Against Asset-Specific History
Incoming data is evaluated against that specific asset’s own historical operating baseline, not a generic manufacturer specification. A bearing temperature unremarkable on one locomotive can be an early warning on another, depending on age, duty cycle, and maintenance record. Machine learning models, trained on more than two million asset-hours of regional failure data, weigh these factors to produce a failure-risk score with an estimated time horizon.
Routed, Prioritised Alerts
A risk score creates operational value only when it reaches the correct person through the correct channel before it matters. Alerts route by severity, asset criticality, and shift schedule, and every alert links to the underlying sensor trend so the recipient can evaluate why the system raised it. Alerts also sync into the client’s CMMS as a work order, closing the loop between detection and action.
From the Nairobi–Mombasa Corridor to a Six-Country Platform
The following timeline records the company’s founding, product milestones, and geographic expansion from 2019 to the present.
Incorporated Following Locomotive Failure-Pattern Analysis
Kendaall Tracking was incorporated in Nairobi following analysis of locomotive failure patterns on Kenya’s freight rail network. The founding team identified that available telematics technology was not built for the African freight context: intermittent connectivity, harsh operating environments, and limited on-site maintenance capacity along parts of the corridor. The first hardware prototype was tested over six months on the Nairobi–Mombasa corridor, on a working locomotive rather than in a laboratory.
First Commercial Deployments
The platform reached commercial availability in mid-2020. The first three enterprise deployments covered a combined locomotive fleet of 47 units across Kenya and Tanzania. Feedback from operations teams during this period shaped the dashboard design: operations managers and maintenance supervisors are the primary users, with deeper diagnostic detail available one click below the surface for engineers.
Predictive Maintenance Engine Reaches General Availability
The predictive maintenance module moved from beta to general availability. Within twelve months, client fleet data showed an average 34% reduction in unplanned stoppages. The platform expanded to cover mining haul trucks and excavators, and satellite connectivity became standard across all new hardware generations, closing the coverage gap on remote mine sites and unlit rural corridors.
SAP PM, Oracle Utilities, and IBM Maximo Connectors Released
Kendaall released its integration suite, with pre-built connectors for SAP PM, Oracle Utilities, and IBM Maximo, plus a documented REST API. Average integration timeline dropped from twelve weeks to under three weeks.
Platform Extended to Port Terminals and Construction Fleets
Following pilots at two East African container terminals and a major infrastructure project, the platform extended into port terminal operations and heavy construction fleet management, adding structural health monitoring for ship-to-shore cranes and utilisation-cycle analysis for construction equipment.
ISO/IEC 27001 Certification and Six-Country Scale
Kendaall Tracking achieved ISO/IEC 27001 certification for its information security management system in early 2024. The platform now supports fleets across Kenya, Tanzania, Uganda, Rwanda, Zambia, and South Africa, with a failure-pattern research dataset exceeding two million asset-hours and growing with every additional monitored asset.
Three Core Capabilities
The platform is organised around three capabilities, each addressing a specific limitation in conventional industrial asset management: fragmented systems, dashboards that go unused, and alerts that lose operator trust.
System Integration
Asset intelligence flows directly into the systems a client already operates — work orders, procurement, finance, and compliance documentation — without a separate system of record.
Native SAP PM, Oracle Utilities, IBM Maximo, and IFS connectors · REST API · SSO via SAML 2.0 / OAuth 2.0 · integrations complete in 2–4 weeks.
Operations-First Interface
The dashboard is built for operations managers and maintenance supervisors as primary users, with detailed diagnostics available for engineers when required.
Fleet-wide health heatmaps · predictive maintenance calendar · offline-capable mobile app · QR/NFC asset access on site.
Prioritised Operational Alerts
Every alert is delivered to the correct recipient through the correct channel, with false-positive rates tracked and reduced over time.
SMS, email, in-app, and CMMS webhook delivery · context-aware thresholds · 73% fewer nuisance alerts within 90 days · shift-aware routing.
Four Industries, One Platform
Asset intelligence requirements differ by operational context. A bearing failure on a locomotive, a hydraulic fault on an excavator, and structural fatigue on a ship-to-shore crane are distinct engineering problems that share a common data infrastructure. The four sections below describe how the platform is configured for each sector.
Rail Freight & Locomotives
Bogie and bearing vibration analysis and corridor-specific thermal baselines for scheduled rail freight services across East African rail networks, with gradient-aware load modelling.
Mining & Extraction Equipment
Haul truck and excavator monitoring built for dust exposure and remote-site connectivity, with satellite failover for open-cast and underground operations where cellular coverage is unreliable.
Port & Container Terminals
Structural health monitoring for ship-to-shore cranes and terminal-equipment utilisation mapping for operators managing tight vessel turnaround windows.
Heavy Construction Fleets
Geofencing-based theft prevention and predictive maintenance for excavators and boring machines on multi-year, multi-site infrastructure projects.
Rail Freight
On rail corridors, a single asset failure carries disproportionate operational consequence: one locomotive going out of service on a single-track corridor can delay every service scheduled behind it, not only the train it was hauling. Rail deployments focus on bogie and axle-bearing vibration analysis, since bearing failure remains a leading cause of unscheduled locomotive downtime and has one of the longest measurable lead times — often days, not hours — when the correct sensor frequencies are monitored. Corridor-specific thermal baselines are built because ambient temperature on the Nairobi–Mombasa corridor differs materially from a highland route; a model that does not account for this either misses real faults or produces false positives.
Mining & Extraction
Mine sites present the opposite structural challenge to rail: instead of one fixed corridor, a fleet of haul trucks, excavators, and loaders operates across a site that changes shape as extraction progresses. Connectivity cannot be assumed, which is why mining deployments rely on LoRaWAN mesh coverage with satellite failover, and why edge processing on the sensor unit matters more here than elsewhere in the platform — an asset several kilometres from the nearest tower must still be able to flag a developing hydraulic or engine fault the moment it appears.
Port & Terminal Operations
Port terminal economics run on vessel turnaround time, making an unplanned crane outage one of the costliest failure modes across the sectors served. Port deployments add structural health monitoring for ship-to-shore cranes — tracking fatigue-relevant strain and vibration on load-bearing structural members over time — alongside utilisation-cycle analysis that shows operators not only whether a crane is available, but how intensively it has been run and what that implies for its maintenance schedule.
Heavy Construction
Construction fleets are defined by mobility: excavators, boring machines, and compactors move between sites over a project’s multi-year lifecycle, often changing operators and crews en route. The construction module combines geofencing-based theft and unauthorised-use prevention with the same predictive maintenance core used across the rest of the platform, so equipment condition data follows the asset from site to site rather than resetting on redeployment.
First 12 months of deployment
Guaranteed across all environments
Powering predictive ML models
Africa, the Middle East & SE Asia
The Dataset Behind the Predictions
A predictive maintenance claim is only as reliable as the failure data underlying it. Kendaall’s models train on a proprietary dataset exceeding two million asset-hours, built from real operating conditions on the corridors, mines, and terminals the company monitors, rather than licensed from a global fleet dataset built for different climates and duty cycles.
Every confirmed failure event on a monitored asset — whether flagged early by the system or identified independently by a client’s maintenance team — is fed back into the training pipeline together with the full preceding sensor history. This closed feedback loop allows prediction accuracy to improve with each deployment rather than plateauing after initial release. Regional specificity matters as much as raw dataset size: a bearing failure signature captured on a locomotive running through Rift Valley heat carries information a model trained in a temperate climate does not have access to.
Every failure-risk score shown on the dashboard carries an associated confidence interval and time horizon, rather than a single number presented as certainty, on the basis that operational decisions built on false precision can be as costly as a missed failure.
What the Dataset Currently Covers
- 2M+ asset-hours of labelled sensor and failure data
- Locomotives, haul trucks, excavators, and terminal cranes
- Six countries of regionally specific operating conditions
- 240+ sensor data points per asset per minute
- Continuous retraining as new confirmed failures are logged
- 72–120 hour predictive lead time on core failure modes
How Kendaall Differs From Generalist Fleet Platforms
Most competing products in the asset-tracking market were originally built for light commercial vehicle fleets — delivery vans, sales cars, last-mile logistics — and later extended into heavy industry. That lineage produces specific limitations: sensor hardware rated for a passenger-vehicle engine bay rather than a locomotive traction motor, alert thresholds tuned for predictable urban duty cycles rather than variable-load freight corridors, and dashboards designed around a fleet manager checking on drivers rather than a maintenance supervisor triaging a health heatmap across two hundred assets. Kendaall was not adapted from a lighter-duty product; every layer of the stack, from the ruggedised sensor casing to the failure-risk model, was built for the asset classes described on this page.
Condition Over Location
Most tracking platforms answer where an asset is. Kendaall answers what condition it is in, and the probability it will require maintenance intervention within the next 72 to 120 hours. Sensor arrays collect over 240 data points per asset per minute across vibration, temperature, pressure, GNSS position, power draw, and acoustic signatures, weighed against each asset’s own historical baseline rather than a generic manufacturer specification sheet.
Sector-Specific Configuration
A rail freight operator on the Nairobi–Mombasa corridor has different requirements from a mining operator running haul trucks in a copper belt. Before deployment, solutions engineers configure alert thresholds, dashboards, and report structures to that specific operational reality rather than applying a generic template drawn from a consumer vehicle-telematics product.
24/7 Operations Support
Asset failures do not observe business hours. Every enterprise client is assigned a named Customer Success Manager with domain expertise in their industry, backed by round-the-clock technical support, so an anomalous reading on a night shift receives a human response rather than a queued ticket.
Measured Operational Impact
Every deployment includes a baseline assessment before go-live and a structured impact review at six and twelve months. Across the client portfolio: an average 38% reduction in unplanned downtime, and maintenance cost reductions of $1.8M–$2.4M annually per 50-asset rail fleet, tracked deployment by deployment rather than quoted as an industry average.
The Team Operating the Platform
Four functions govern the platform’s engineering and operation, based in Nairobi alongside the client base the company serves.
Jane Kamau
Chief Technology OfficerFourteen years in embedded systems and machine learning. Designed the platform’s connectivity architecture and predictive maintenance engine, and reviews every new sensor firmware release before it ships to a client site.
Simon Kibaki
Operations ManagerOversees client deployment operations. Eleven years in rail freight operations management across East and Southern Africa, including corridor scheduling under the same unplanned-downtime pressure the platform is built to prevent.
Mary Johnson
Lead AnalystLeads data science and predictive maintenance model training. PhD in Statistical Learning; nine years in industrial failure analysis, focused on making model confidence intervals meaningful to non-technical operations teams.
James Mwangi
Customer Support LeadLeads customer success and technical support. Eight years in enterprise logistics technology, with a mechanical engineering background that routes client-facing escalations to someone who understands the equipment, not only the software.
These four functions — hardware and machine-learning engineering, field deployment operations, data science, and customer support — are structured to sit in constant contact. A predictive maintenance platform that separates the people who build the models from the people who see those models perform in a real mine or on a real corridor tends to drift out of touch with the operational reality it is supposed to serve.
From Contract to Go-Live
The deployment sequence below describes the stages a fleet operator goes through from initial assessment to ongoing operation, stage by stage.
Operational Context Assessment
Before hardware is quoted, a solutions engineer assesses the fleet: asset types and ages, existing maintenance practices, current CMMS if one exists, site connectivity conditions, and the failure modes that have historically caused the most downtime. This assessment determines sensor configuration and pricing, and produces the preliminary ROI model the client uses internally to justify the deployment.
Hardware Installation
Installation takes one to three days per asset, depending on fleet size and asset type, scheduled around existing maintenance windows where possible. Field technicians commission each sensor unit on site and confirm connectivity across whichever network combination — 4G, LoRaWAN, or satellite — fits that location.
Baseline Data Collection
Newly installed sensors establish each asset’s own operating baseline before predictive alerting is fully activated. A failure-risk model is only as reliable as its understanding of that asset’s normal operating range; skipping this step produces noisy, low-trust alerting.
System Integration
In parallel with baseline collection, the integration team connects the platform to the client’s CMMS or ERP — most commonly SAP PM, IBM Maximo, Oracle Utilities, or IFS — so predictive alerts generate work orders automatically. Full enterprise integration is typically complete within two to four weeks of kickoff.
Named Customer Success Management
From go-live onward, every enterprise client has a named Customer Success Manager with relevant industry background, backed by 24/7 technical support, responsible for reviewing alert threshold tuning as seasonal conditions shift.
Structured Impact Reviews
At six and twelve months, a formal review runs against the pre-go-live baseline assessment, quantifying downtime reduction, alert accuracy, and maintenance cost impact. Reviews are shared directly with the client and used to recalibrate thresholds and reporting.
Security, Support, and Accountability
Kendaall Tracking operates within an industry where operational failure carries financial consequences that can run into millions of dollars, and human consequences that are harder to quantify. That responsibility governs decisions about how the platform is built, deployed, and supported.
The company’s commitment to clients extends beyond hardware and software delivery: to being reachable when an anomalous alert fires on a critical asset, and to reviewing alert thresholds proactively as seasonal conditions change, rather than waiting for a client to report an issue.
Kendaall treats the relationship between its platform and a client’s own maintenance team as a partnership, not a replacement. The platform is designed to make an experienced maintenance engineer more effective by surfacing signal earlier, not to substitute a dashboard for the judgement of the person acting on it. Every predictive alert explains its underlying evidence, so the receiving engineer can evaluate it rather than trust a black-box score, and can override or annotate a prediction when on-the-ground knowledge of an asset contradicts what the sensor data shows.
Data security is governed by ISO/IEC 27001 certification and SOC 2 Type II attestation. Local capability development means investing in Kenyan engineering and operations talent, on the premise that African logistics problems are best solved by people who understand African logistics directly.
- 24/7 technical support coverage across all operational time zones
- ISO/IEC 27001 certified information security management system
- Named Customer Success Manager assigned to every enterprise deployment
- Quantified 6- and 12-month impact reviews for every client deployment
- Five-year data retention for compliance and regulatory requirements
- Hardware designed for 7+ year operational life with minimal maintenance
Security First
AES-256 encryption across all data paths, ISO/IEC 27001 certified infrastructure, and SOC 2 Type II attestation covering all client data.
People-Led Support
Support staff with logistics domain expertise, available continuously. Every escalation reaches a specialist who understands the operational context.
Built for African Conditions
Designed from first principles for intermittent connectivity, extreme operating environments, and remote-site operations.
Outcome Accountability
Success is measured by client operational outcomes. Every deployment includes structured impact reviews.
Regulatory Confidence
Automated compliance documentation and tamper-evident maintenance logs support ongoing audit-readiness.
Long-Term Partnership
The business model is built on long-term client relationships rather than one-time deployments.
Internal Review Average
Based on 186 deployment reviews covering support responsiveness, platform reliability, and measured operational outcomes. See full testimonials.
— Rail Freight Operator, Kenya
— Open-Cast Mining Operator, Zambia
— Container Terminal Operator, Tanzania
Asset Intelligence in East and Southern African Logistics
Heavy industry and freight logistics underpin economic activity across East and Southern Africa, and the assets that move goods, extract resources, and build infrastructure represent some of the largest capital investments a company will make. When a locomotive, haul truck, or ship-to-shore crane fails unexpectedly, the cost is rarely limited to the repair bill — it extends into missed delivery windows, idle labour, delayed vessel departures, and contractual penalties that can exceed the maintenance cost itself.
The region has historically been served by asset monitoring technology designed for different climates, different connectivity assumptions, and different asset classes, then adapted superficially for local conditions. Building a platform specifically for African freight, mining, port, and construction environments — rather than retrofitting a product built elsewhere — is why the hardware survives conditions that would degrade a generic sensor, and why the predictive models capture failure patterns a model trained elsewhere would miss.
Operators who adopt asset intelligence earliest are not only avoiding individual breakdowns; they are building an institutional record of how their own fleet behaves — data that compounds in value with every month it keeps running, in a way a spreadsheet of odometer readings and service dates does not.
Common Questions About Kendaall Tracking
Where is Kendaall Tracking based and which regions do you serve?
Kendaall Tracking is headquartered at 35644 Kasarani Mwiki Road, Nairobi, Kenya. Active deployment regions span Kenya, Tanzania, Uganda, and Rwanda, as well as Zambia and South Africa, with enterprise partnerships in the Middle East and Southeast Asia served through remote monitoring and integration.
What industries does Kendaall Tracking specialise in?
Four core specialisations: rail freight and locomotive fleet management, mining and extraction equipment monitoring, port and container terminal operations, and heavy construction fleet management — each a distinct configuration layer on the same core platform.
How does Kendaall Tracking secure client data?
AES-256 end-to-end encryption across every transmission path. Infrastructure is ISO/IEC 27001 certified with SOC 2 Type II attestation. Client data is logically isolated in dedicated tenant environments with role-based access down to the individual asset.
How is Kendaall Tracking different from a standard GPS fleet management company?
Standard fleet platforms treat location as the primary output. Kendaall reports asset condition and the probability of failure, using 240+ sensor data points per minute and machine learning trained specifically for African heavy logistics environments.
Can Kendaall Tracking work alongside our existing asset management systems?
Yes — native connectors for SAP PM, Oracle Utilities, IBM Maximo, and IFS enable bidirectional data flow into workflows already in use, plus a REST API and webhook architecture for custom integrations. Most integrations go live within two to four weeks.
How much does it cost to deploy Kendaall Tracking across a fleet?
Pricing depends on fleet size, asset type, sensor configuration, and integration scope. Enterprise clients receive a tailored quote after an operational context assessment, with a documented ROI model produced before signing. See pricing.
Does Kendaall Tracking work in areas with poor connectivity?
Yes. Hardware uses multi-network connectivity — 4G LTE, LoRaWAN, and satellite backup — so assets on remote freight corridors, mines, and construction sites keep transmitting where terrestrial coverage is unreliable.
What makes Kendaall’s predictive maintenance models accurate?
Models train on more than two million asset-hours of regionally specific failure data, covering vibration, thermal, acoustic, and load signatures from locomotives, haul trucks, and terminal equipment operating in African conditions, rather than generic global fleet averages built on different climates and duty cycles.
Who owns the data collected by Kendaall’s sensors?
Clients retain full ownership of all data their assets generate. Kendaall processes that data solely to deliver contracted monitoring, analytics, and predictive maintenance services, under the data protection terms in each client agreement.
What happens after a Kendaall deployment goes live?
Every client is assigned a named Customer Success Manager and moves into a structured review cycle, with a baseline operational assessment at go-live followed by formal impact reviews at six and twelve months to quantify downtime reduction and cost savings.
Speak With a Solutions Engineer
Schedule a 45-minute conversation with a Kendaall solutions engineer working in your industry. The session maps the platform to your operational context and produces a preliminary deployment and ROI model based on your fleet, using the operational context assessment described earlier on this page.
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+254 105 152 896 · support@kendaalltracking.co.ke · 35644 Kasarani Mwiki Road, Nairobi, Kenya