We Built the Asset Intelligence Company We Wished Existed
Kendaall Tracking is an asset intelligence company headquartered in Nairobi, built for one reason: logistics operators and heavy industry leaders deserved better than GPS dots on a map. We refused to accept that industrial asset management had to mean expensive guesswork, reactive breakdowns, and operational blind spots — so we built the platform, the data science, and the company, that changes that.
This page tells that story in full: where the company came from, what we believe about the future of industrial maintenance, who is on the team turning that belief into working software and hardware, and how our platform behaves differently from the fleet-tracking tools most operators have already tried and outgrown. If you are evaluating an asset intelligence partner for a locomotive fleet, a mine, a port terminal, or a construction site, this is the fastest way to understand who you would actually be working with.
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An Asset Management Company Built on the Belief That Every Asset Deserves a Voice
Kendaall Tracking is an asset management and asset intelligence company headquartered in Nairobi, Kenya, with operational reach spanning East, Central, and Southern Africa. We specialise in the real-time monitoring of locomotives, freight wagons, mining machinery, port equipment, and heavy industrial plant — the kinds of high-value, high-consequence assets where operational visibility is a business-critical requirement, not a convenience.
We were founded by a team of logistics engineers, data scientists, and embedded systems developers who had collectively spent years watching the same problem repeat itself across African freight and industrial operations: critical assets operating in relative darkness, maintenance teams reacting to failures rather than preventing them, and operations managers unable to make informed decisions about fleet allocation, condition, or risk. That founding frustration is still the reason the company exists, and it still shapes the way every feature on the platform gets built, tested, and shipped.
Locomotives, haul trucks, ship-to-shore cranes, and excavators are not consumer products. They are long-lived, capital-intensive machines that are expected to run for decades, in conditions — dust, heat, vibration, remote terrain, patchy connectivity — that most commercial telematics products were never designed to survive. Building an asset intelligence platform for this category means starting from the operating environment, not from a generic vehicle-tracking template, and working backward to the hardware, the connectivity stack, and the analytics layer that can actually hold up on a mine haul road or inside a locomotive engine bay.
Kendaall Tracking’s platform combines ruggedised industrial IoT hardware, multi-network connectivity, machine learning–based predictive maintenance, and an enterprise-grade analytics dashboard into a single coherent system, all reporting into one fleet management account. Our approach is grounded in the principle that asset intelligence should be accessible to the operations team — not just the engineering team, and not locked inside a data science department that operations managers rarely get to see.
What “Asset Intelligence” Actually Means at Kendaall
Asset location tells you where something is. Asset intelligence tells you the condition of that asset, how its current behaviour compares to its own operating history, and the statistical risk it carries of failing before its next scheduled maintenance window. Every product decision we make is filtered through that distinction — because operations managers do not need more dashboards, they need fewer surprises.
This distinction sits at the centre of the broader shift in industrial maintenance thinking, from calendar-based servicing, to condition-based maintenance, to fully predictive strategies driven by continuous sensor data. Kendaall was built specifically to occupy that predictive end of the spectrum for the asset classes — rail, mining, ports, heavy construction — that most telematics vendors treat as an afterthought rather than a starting point.
Since our founding in 2019, we have expanded from a focused rail freight monitoring product into a comprehensive heavy industry asset intelligence platform, serving clients in rail logistics, mining, container terminal operations, and large-scale infrastructure construction. In each sector, our philosophy stays the same: understand the operational environment first, then configure the platform, the alert thresholds, and the reporting structure to speak that environment’s language, rather than forcing every client into one generic dashboard.
That environment-first approach also shapes how we think about the relationships between the assets themselves. A locomotive’s bearing temperature reading only means something in relation to that specific locomotive’s historical baseline, the corridor gradient it is running, the ambient temperature that day, and the load it is hauling. Kendaall’s models are built to understand those relationships as a connected picture of an operation, not as a set of isolated numbers on a chart.
Why We Started With Rail
Rail freight was a deliberate starting point, not an accident of circumstance. Locomotives combine everything that makes asset intelligence genuinely hard: they are enormously expensive to take out of service, they operate on fixed corridors where a single failure can halt an entire freight line rather than just one vehicle, and their failure modes — bearing degradation, traction motor faults, brake system wear — develop gradually and leave a measurable signature long before they become a breakdown. If a platform could reliably read those signatures on a moving locomotive in the Kenyan interior, we reasoned it could be adapted to almost any other heavy asset class. That has largely proven true, and it is the reason the same core sensing and modelling architecture now underpins our mining, port, and construction products.
How the Team Thinks About the Problem
Internally, we describe our job as closing three gaps that most industrial operations still carry: a visibility gap, where nobody can say with confidence what condition an asset is actually in right now; a prediction gap, where maintenance teams only find out about a developing fault once it has already become a failure; and a coordination gap, where the data that could prevent downtime exists somewhere in the organisation but never reaches the person who needs it in time to act. Every feature we build is designed to close one of those three gaps, and we deliberately avoid building features that don’t map back to at least one of them, because dashboard sprawl is its own kind of operational blind spot.
To empower logistics operators and heavy industry businesses to achieve optimal operational efficiency by transforming raw asset data into actionable intelligence — delivering the visibility, predictability, and control that eliminates downtime, reduces risk, and enables confident decision-making at every level of the organisation.
Operational Integrity
We build for environments where failure is not an option and data accuracy carries real operational and financial consequence.
Relentless Reliability
Our platform is built to perform when conditions are worst — in tunnels, in dust, in heat, where connectivity is a challenge, not a given.
Client Partnership
Every deployment is a long-term partnership. Our success is measured by what clients achieve, not by what features we ship.
Intelligence, Not Noise
We are obsessed with turning data into decisions. Every alert and dashboard element must reduce uncertainty and improve outcomes.
How We Talk About Asset Intelligence
These are the underlying concepts behind everything on this page. Understanding the difference between them is a useful starting point for evaluating any asset monitoring vendor, not just Kendaall.
Asset Intelligence
Converting continuous sensor data from industrial equipment into condition insight, failure-risk scoring, and maintenance recommendations — the layer above simple GPS tracking.
Predictive Maintenance
A maintenance strategy that uses sensor data and machine learning to forecast when an asset is likely to fail, so intervention happens before breakdown. See predictive maintenance on Wikipedia for the general concept.
Condition-Based Maintenance
Maintenance triggered by an asset’s measured, real-time condition rather than a fixed calendar or usage schedule. Read more on condition monitoring.
Industrial IoT (IIoT)
Networks of ruggedised, connected sensors deployed on industrial equipment to collect operational and condition data continuously, even in remote or harsh environments. See Industrial Internet of Things.
How Data Moves From an Asset to an Actionable Alert
Understanding how the Kendaall platform actually works, end to end, is the clearest way to understand what we mean when we describe ourselves as an asset intelligence company rather than a tracking company. The pipeline has four stages, and each one exists to solve a specific problem we saw operators run into with earlier-generation telematics.
Field-Grade Sensing
Ruggedised, IP67-rated sensor units are installed directly on the asset — on bogies, bearings, engine bays, hydraulic lines, or structural members, depending on the equipment class. Each unit captures vibration, temperature, pressure, acoustic, load, and GNSS position data continuously, sampling far more frequently than a typical GPS unit because condition signatures often show up in changes that unfold over minutes, not just over a shift.
Multi-Network Transmission
Data moves off the asset over whichever network is available and most efficient at that moment — 4G LTE where coverage is strong, LoRaWAN for low-power, long-range transmission on fixed sites like mines and ports, and satellite backup on remote corridors where terrestrial networks drop out entirely. Edge processing on the sensor unit itself means an asset does not go dark just because it loses one connection type.
Modelling Against the Asset’s Own History
Incoming data is evaluated against that specific asset’s historical operating baseline, not a generic manufacturer specification. A bearing temperature that would be unremarkable on one locomotive can be an early warning sign on another, depending on its age, duty cycle, and maintenance history. Our machine learning models, trained on more than two million asset-hours of regional failure data, weigh all of this together to produce a failure-risk score with an estimated time horizon.
Routed, Prioritised Alerts
A risk score only creates value if it reaches the right person, through the right channel, before it matters. Alerts are routed based on severity, asset criticality, and shift schedule, and every alert links directly to the underlying sensor trend so the recipient can see why the system raised it, not just that it did. This is also the stage where alerts sync into your CMMS as a work order, closing the loop between detection and action.
From a Single Rail Corridor to a Continental Platform
Kendaall Tracking’s story is the story of a problem that kept presenting itself, a team that refused to accept inadequate answers, and a platform that grew from those convictions into one of the region’s most capable asset intelligence systems.
Founded in Response to a Real Problem
Kendaall Tracking was incorporated in Nairobi following a detailed analysis of locomotive failure patterns on Kenya’s freight rail network. The founding team identified that the missing piece was technology integrated specifically for the African freight context: intermittent connectivity, harsh operating environments, and limited on-site maintenance capacity in some corridor sections. The first hardware prototype was tested over six months on the Nairobi–Mombasa corridor, instrumented on a working locomotive rather than in a lab, so that every design decision reflected real operating conditions from day one.
First Commercial Deployments Go Live
The platform reached commercial availability in mid-2020, with the first three enterprise deployments covering a combined locomotive fleet of 47 units across Kenya and Tanzania. Feedback from operations teams during this period fundamentally shaped our dashboard design philosophy: build for operations managers and maintenance supervisors first, and let the deeper diagnostic detail live one click below the surface for the engineers who need it.
Predictive Maintenance Engine Launched
Our predictive maintenance module moved out of beta into general availability. Within twelve months, client fleet data confirmed an average 34% reduction in unplanned stoppages. The platform expanded to cover mining haul trucks and excavators, and a satellite connectivity module became standard across all new hardware generations, closing the coverage gap on remote mine sites and unlit rural corridors.
SAP, Oracle, and CMMS Integration Released
Kendaall released its native integration suite, with pre-built connectors for SAP PM, Oracle Utilities, and IBM Maximo, plus a fully documented REST API. Average integration timeline dropped from twelve weeks to under three weeks, removing what had been the single largest barrier to enterprise adoption.
Port Terminal and Construction Fleet Modules Launched
The platform extended into port terminal operations and heavy construction fleet management following successful pilots at two East African container terminals and a major infrastructure project, adding structural health monitoring for ship-to-shore cranes and utilisation cycle analysis for construction equipment.
ISO 27001 Certification and Continental Scale
Kendaall Tracking achieved ISO/IEC 27001 certification for its information security management system in early 2024. The platform now supports fleets across six countries, with a research team continuing to expand a failure-pattern database that now exceeds two million asset-hours of training data — a dataset that grows, and gets more accurate, with every additional asset we monitor.
Three Core Features That Define How We Work
Kendaall’s platform is built around three foundational capabilities, each engineered to address a specific failure mode in how industrial asset management has traditionally been approached: fragmented systems, dashboards nobody uses, and alerts nobody trusts.
Seamless System Integration
Kendaall enhances the systems your team already depends on. Asset intelligence flows automatically into work orders, procurement, finance, and compliance documentation — without months-long IT projects or a second system of record to maintain.
Native SAP PM, Oracle, IBM Maximo and IFS connectors · full REST API · SSO via SAML 2.0 / OAuth 2.0 · integrations live in 2–4 weeks.
User-Friendly Interface
Asset intelligence creates no value if only specialists can interpret it. Kendaall’s dashboard is built for operations managers and maintenance supervisors first, with detailed diagnostics available for engineers exactly when they need to go deeper.
Fleet-wide health heatmaps · maintenance calendar with predictive alerts · offline-capable mobile app · QR/NFC asset access on site.
Custom Operational Alerts
Every alert that fires should be genuinely actionable, correctly prioritised, and delivered to the right person through the right channel — never noise that trains your team to start ignoring the system.
SMS, email, in-app and CMMS webhook delivery · context-aware thresholds · 73% fewer nuisance alerts within 90 days · shift-aware routing.
The Industries Kendaall Tracking Was Built For
Asset intelligence means something different in every operational context. A bearing failure on a locomotive, a hydraulic fault on an excavator, and structural fatigue on a ship-to-shore crane are entirely different engineering problems that happen to share a common data infrastructure. Here is how Kendaall configures itself for the four sectors we know best.
Rail Freight & Locomotives
Bogie and bearing vibration analysis and corridor-specific thermal baselines for scheduled rail freight services across East African rail networks, including gradient-aware load modelling.
Mining & Extraction Equipment
Haul truck and excavator monitoring built for dust and remote-site connectivity, with satellite failover for open-cast and underground operations where cellular coverage cannot be assumed.
Port & Container Terminals
Structural health monitoring for ship-to-shore cranes and terminal equipment utilisation mapping for high-throughput operators managing tight vessel turnaround windows.
Heavy Construction Fleets
Geofencing-based theft prevention and predictive maintenance for excavators and boring machines on large infrastructure projects with multi-year, multi-site equipment schedules.
Rail Freight in Depth
On rail corridors, the operational stakes of a single asset failure are disproportionately high: one locomotive going out of service on a single-track corridor can delay every service scheduled behind it that day, not just the one train it was hauling. Our rail deployments focus heavily on bogie and axle-bearing vibration analysis, because bearing failure remains one of the most common causes of unscheduled locomotive downdime, and it is also one of the failure modes with the longest measurable lead time — often days, not hours — when the right sensors are watching the right frequencies. We build corridor-specific thermal baselines because ambient temperature on the Nairobi–Mombasa corridor behaves very differently from a highland route, and a model that doesn’t account for that will either miss real faults or drown operators in false positives.
Mining & Extraction in Depth
Mine sites present the opposite challenge from rail: instead of one long, fixed corridor, you have a constantly shifting fleet of haul trucks, excavators, and loaders operating across a site that itself changes shape as extraction progresses. Connectivity cannot be assumed, which is why our mining deployments lean heavily on LoRaWAN mesh coverage with satellite failover, and why edge processing on the sensor unit matters more here than almost anywhere else in the platform — an asset several kilometres from the nearest tower still needs to be able to flag a developing hydraulic or engine fault the moment it appears.
Port & Terminal Operations in Depth
Port terminal economics run on vessel turnaround time, which makes an unplanned crane outage one of the costliest failure modes in the industries we serve. Our 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 helps terminal operators understand not just whether a crane is available, but how intensively it has been run and what that means for its maintenance schedule.
Heavy Construction in Depth
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 along the way. Kendaall’s 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 every time it’s redeployed.
First 12 months of deployment
Guaranteed across all environments
Powering our predictive ML models
Africa, the Middle East & SE Asia
The Dataset Behind the Predictions
A predictive maintenance claim is only as credible as the failure data it was trained on. Kendaall’s models are trained on a proprietary dataset that now exceeds two million asset-hours, built entirely from real operating conditions on the corridors, mines, and terminals we monitor — not licensed from a global fleet dataset built around different climates, different duty cycles, and different maintenance practices.
Every confirmed failure event that occurs on a monitored asset — whether flagged early by our system or discovered independently by a client’s maintenance team — is fed back into the training pipeline, together with the full sensor history that preceded it. This closed feedback loop is what allows prediction accuracy to keep improving deployment by deployment, rather than plateauing after the initial model release. It is also why regional specificity matters as much as raw dataset size: a bearing failure signature captured on a locomotive running through the Rift Valley heat carries information a generic model trained in temperate climates simply does not have access to.
Our lead analyst’s academic background in statistical learning shapes how conservatively the team treats model claims: every failure-risk score shown on the dashboard carries an associated confidence interval and time horizon, rather than a single number presented as certainty, because operations decisions made on the back of a false sense of 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
What Sets Kendaall Apart in Asset Management
The asset tracking market is crowded with point solutions that solve narrow problems. Kendaall was built to solve the whole problem — in environments and for asset types that generic platforms are not equipped to handle.
Most competing products in this space were originally built for light commercial vehicle fleets — delivery vans, sales cars, last-mile logistics — and later extended, sometimes awkwardly, into heavy industry. That lineage shows up in subtle but important ways: 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 never adapted from a lighter-duty product; every layer of the stack, from the ruggedised sensor casing to the failure-risk model, was built specifically for the asset classes described on this page.
Comprehensive Asset Insight
Most tracking platforms answer where an asset is right now. Kendaall answers the harder questions: what condition is it in, and what is the probability it will need a maintenance intervention in the next 72 to 120 hours? Our sensor arrays collect over 240 data points per asset per minute across vibration, temperature, pressure, GNSS position, power draw, and acoustic signatures, and our models weigh those signals against each asset’s own historical baseline rather than a generic manufacturer specification sheet.
Tailored Solutions for Every Operational Context
A rail freight operator on the Nairobi–Mombasa corridor has fundamentally different requirements from a mining operator running haul trucks in the copper belt. Before any deployment, our solutions engineers configure alert thresholds, dashboards, and report structures around that specific reality — not a generic template borrowed from a consumer vehicle-telematics product.
Around-the-Clock 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 24/7 technical support regardless of time zone, so an anomalous reading on a night shift gets a human response, not a queued ticket.
Demonstrated Track Record of Operational Impact
Every deployment includes a baseline assessment before go-live and a structured impact review at six and twelve months. Across our portfolio: an average 38% reduction in unplanned downtime, and maintenance cost reductions of $1.8M–$2.4M annually per 50-asset rail fleet, figures we track deployment by deployment rather than quote as an industry average.
Meet the Experts Driving Kendaall’s Innovation
Every capability on the Kendaall platform reflects the expertise, discipline, and commitment of the team that built it and continues to develop it, in Nairobi, alongside the clients who depend on it.
Jane Kamau
Chief Technology OfficerFourteen years in embedded systems and machine learning. Designed the platform’s connectivity architecture and predictive maintenance engine, and still reviews every new sensor firmware release personally before it ships to a client site.
Simon Kibaki
Operations ManagerOversees all client deployment operations. Eleven years in rail freight operations management across East and Southern Africa, including several years managing corridor scheduling under exactly the kind of unplanned-downtime pressure Kendaall now helps clients avoid.
Mary Johnson
Lead AnalystLeads data science, training the predictive maintenance models at the platform’s core. PhD in Statistical Learning; nine years in industrial failure analysis, with a particular focus 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 background in mechanical engineering that means client-facing escalations get routed to someone who understands the equipment, not just the software.
These four functions — hardware and machine learning engineering, field deployment operations, data science, and customer support — sit deliberately close together at Kendaall. A predictive maintenance platform that separates the people who build the models from the people who see how 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. Keeping those functions in constant contact is a structural decision, not an accident of company size.
What a Kendaall Deployment Actually Looks Like
Clients rarely ask us abstract questions about our platform’s capabilities in isolation — they ask what the first ninety days will actually involve. Here is the honest answer, stage by stage.
Operational Context Assessment
Before any hardware is quoted, a solutions engineer spends time understanding the fleet: asset types and ages, existing maintenance practices, current CMMS if one exists, connectivity conditions on site, and the specific failure modes that have caused the most downtime historically. This assessment shapes the sensor configuration and the pricing model, and it is also where we build the preliminary ROI model a client will use internally to justify the deployment.
Hardware Installation
Installation typically takes one to three days per asset, depending on fleet size and asset type, and is scheduled around a client’s existing maintenance windows wherever possible so it does not create additional downtime of its own. 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 spend an initial period establishing each asset’s own operating baseline before predictive alerting is fully activated. This step matters because a failure-risk model is only as good as its understanding of what “normal” looks like for that specific asset, and rushing past it produces exactly the kind of noisy, low-trust alerting we design the platform to avoid.
System Integration
In parallel with baseline collection, our integration team connects the platform to the client’s CMMS or ERP — most commonly SAP PM, IBM Maximo, Oracle Utilities, or IFS — so that predictive alerts generate work orders automatically rather than living in a separate system nobody checks. 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. This is the same person who reviews alert threshold tuning as seasonal conditions shift, rather than a rotating support queue that has to relearn the account each time.
Structured Impact Reviews
At six and twelve months, we run a formal review against the baseline assessment captured before go-live, quantifying downtime reduction, alert accuracy, and maintenance cost impact. These reviews are shared directly with the client and used to recalibrate thresholds and reporting as the deployment matures.
What Kendaall Tracking Stands For
Kendaall Tracking exists within an industry where the consequences of operational failure are measured in financial terms that run into millions of dollars — and in human terms that are harder to quantify. We take that responsibility seriously in every decision we make about how our platform is built, deployed, and supported.
Our commitment to clients extends beyond software and hardware delivery. We commit to being the organisation that picks up the phone at two in the morning 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 flag that something feels wrong.
We also treat the relationship between Kendaall and a client’s own maintenance team as a partnership rather than a replacement. Our platform is designed to make an experienced maintenance engineer more effective by surfacing the signal earlier, not to substitute a dashboard for the judgement of the people who ultimately have to act on it. That distinction shows up in how we design escalation workflows: every predictive alert is written to explain the underlying evidence, so the receiving engineer can evaluate it critically rather than simply trusting a black-box score, and can override or annotate a prediction when their own on-the-ground knowledge of an asset says something the sensor data hasn’t yet fully captured.
Data security is non-negotiable — our ISO/IEC 27001 certification and SOC 2 Type II attestation reflect the rigour we apply to protecting client data. Local capability development means investing in Kenyan engineering and operations talent, because the problems of African logistics deserve solutions built by people who understand African logistics from the inside.
- 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 protecting every byte of client data.
People-Led Support
Real people, with real logistics expertise, available around the clock. Every escalation reaches a specialist who understands the operational context.
Built for Africa
Designed from first principles for the realities of African logistics: intermittent connectivity, extreme environments, and remote operations.
Outcome Accountability
We measure our success by the outcomes our clients achieve, not the features we ship. Every deployment includes structured impact reviews.
Regulatory Confidence
Automated compliance documentation and tamper-evident maintenance logs keep audit-readiness built into normal operations.
Long-Term Partnership
Our business model is built on long-term client relationships, not one-time deployments, aligned with our clients’ ongoing success.
Our Internal Review Score
Based on 186 deployment reviews tracking support responsiveness, platform reliability, and measured operational outcomes.
— Rail Freight Operator, Kenya
— Open-Cast Mining Operator, Zambia
— Container Terminal Operator, Tanzania
The Broader Case for Asset Intelligence in African Logistics
Heavy industry and freight logistics are foundational to economic growth across East and Southern Africa, and the assets that move goods, extract resources, and build infrastructure are some of the most capital-intensive investments a company will make. When a locomotive, a haul truck, or a ship-to-shore crane goes down unexpectedly, the cost is rarely confined to the repair bill — it cascades into missed delivery windows, idle labour, delayed vessel departures, and, in some cases, contractual penalties that dwarf the maintenance cost itself.
We think the region has been underserved by asset monitoring technology that was designed elsewhere, for different climates, different connectivity assumptions, and different asset classes, then adapted only superficially for local conditions. Building a platform from the ground up for African freight, mining, port, and construction environments — rather than retrofitting a Western telematics product — has been the harder path, but it is also the reason our hardware survives conditions that would degrade a generic sensor, and the reason our predictive models understand failure patterns that a generic model trained elsewhere would simply miss.
That is ultimately the case for asset intelligence as a category, and for Kendaall specifically: the operators who adopt it earliest are not just avoiding individual breakdowns, they are building an institutional memory of how their own fleet actually behaves — data that compounds in value every month it keeps running, and that a spreadsheet of odometer readings and service dates was never going to capture.
What Clients Ask Us Most
Where is Kendaall Tracking based and which regions do you serve?
Kendaall Tracking is headquartered at 35644 Kasarani Mwiki Road in Nairobi, Kenya. Our active deployment regions span East Africa — 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?
Our four core specialisations are 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 ensure data security for client operations?
All platform data is protected by AES-256 end-to-end encryption across every transmission path. Our infrastructure is ISO/IEC 27001 certified with SOC 2 Type II attestation, client data is logically isolated in dedicated tenant environments, and access is role-based down to the individual asset.
How is Kendaall Tracking different from a standard GPS fleet management company?
Standard fleet platforms are built around location as the primary value. Kendaall answers a harder set of questions — what condition an asset is in and what it will need before it fails — using 240+ sensor data points per minute and machine learning trained 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 your team already uses, plus a REST API and webhook architecture for custom integrations. Most 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 the level of predictive maintenance and integration required. Enterprise clients receive a tailored quote after an operational context assessment, with a documented ROI model before signing.
Does Kendaall Tracking work in areas with poor connectivity?
Yes. Our hardware is built with multi-network connectivity — 4G LTE, LoRaWAN, and satellite backup — so assets on remote freight corridors, mines, and construction sites keep transmitting even where terrestrial coverage is unreliable.
What makes Kendaall’s predictive maintenance models accurate?
Our models are trained on more than two million asset-hours of real, 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 that assume different climates and duty cycles.
Who owns the data collected by Kendaall’s sensors?
Clients retain full ownership of all data generated by their assets. Kendaall processes that data solely to deliver the contracted monitoring, analytics, and predictive maintenance services, under the data protection terms set out 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.
Ready to Meet the Team Behind the Platform?
Schedule a 45-minute conversation with a Kendaall solutions engineer who specialises in your industry. We will map the platform to your operational context and build a preliminary deployment and ROI model based on your fleet — using the same operational context assessment described earlier on this page, not a generic sales template.
Book a Conversation See the Platform
+254 105 152 896 · support@kendaalltracking.co.ke · 35644 Kasarani Mwiki Road, Nairobi, Kenya