Frequently asked questions.

Nathan Claire Group (NCG) is a global performance engineering company. We build and operate EDGE — an enterprise-grade unified observability and AIOps platform that gives technology teams complete visibility into their applications, infrastructure, users, and data in real time. We combine our proprietary platform with deep consulting expertise to help enterprises proactively detect, investigate, and resolve performance and reliability issues before they affect business outcomes.

NCG provides enterprise observability, AIOps, application performance monitoring (APM), real user monitoring (RUM), infrastructure monitoring, log monitoring, synthetic monitoring, transaction monitoring, database monitoring, network monitoring, and OpenTelemetry integration — all through the EDGE platform. We also offer Monitoring as a Service (MaaS) for organisations that want fully managed observability without the operational overhead.

NCG works with enterprises, banks, telcos, fintech companies, and technology-driven organisations that require high-reliability visibility into their digital operations. Our clients include tier-one financial institutions, telecommunications providers, and large enterprises across multiple industries and regions.

NCG serves banking and financial services, telecommunications, fintech, insurance, healthcare technology, retail, and enterprise technology organisations. Our platform is designed for any industry where application performance, system reliability, and digital customer experience are business-critical.

NCG operates globally with offices in Kenya (Nairobi), Nigeria (Lagos), and the United Kingdom (London). We actively support clients across multiple regions including East Africa, West Africa, Europe, and beyond. Our distributed team enables us to support enterprise environments across different time zones.

Yes. NCG is a global technology company. While we have deep roots in Africa and strong regional expertise, we serve and support clients internationally. Our platform, tooling, and consulting capability are not limited to any single region.

Yes. Our platform is designed for enterprise environments regardless of geography. We support international organisations and have the capability to deploy, integrate, and support EDGE across distributed global infrastructure.

AIOps (Artificial Intelligence for IT Operations) uses machine learning and data analytics to enhance and automate IT operations. It correlates data from multiple monitoring sources — metrics, logs, events, traces — to identify patterns, detect anomalies, prioritise incidents, and recommend or automate remediation actions. The result is faster incident detection, reduced alert noise, and more proactive IT operations.

Enterprise AIOps applies AI-powered operational intelligence at the scale required by large organisations — processing millions of events, correlating signals across hundreds of services, and providing root cause analysis fast enough to protect business-critical systems. NCG's Overwatch module delivers enterprise AIOps natively within the EDGE platform, combining learned baselines, anomaly detection, and intelligent alert grouping.

AIOps improves IT operations by reducing alert noise, accelerating root cause analysis, detecting issues before they escalate, and enabling teams to focus on high-impact problems rather than managing alert floods. By correlating signals across the full observability stack — APM, infrastructure, logs, RUM, transactions — AIOps can identify the root cause of incidents that would otherwise take hours to diagnose manually.

AIOps reduces incident response time by automatically correlating related alerts into a single incident, ranking likely root causes by business impact, and surfacing relevant evidence from across the monitoring stack. What might take a team 90 minutes of manual investigation can often be reduced to minutes with AI-powered correlation and root cause analysis.

AI helps detect IT problems by learning what "normal" looks like for each service, metric, and user journey — and then flagging statistically significant deviations in real time. Unlike static threshold-based alerting, AI-powered detection adapts to seasonal patterns, deployment changes, and traffic fluctuations, catching subtle anomalies that would otherwise go unnoticed.

AI-powered IT operations ingests telemetry data (metrics, traces, logs, events) from across the technology stack, applies machine learning models to learn normal behaviour, detects deviations and anomalies, correlates related signals, and surfaces actionable intelligence to operations teams. On EDGE, this is managed by the Overwatch module, which provides continuous AIOps across all eleven integrated monitoring capabilities.

Enterprise observability is the ability to understand the internal state of complex distributed systems by examining the outputs they produce — metrics, traces, logs, and events — at the scale and depth required by large organisations. It goes beyond basic monitoring to provide the context needed to understand why a system is behaving a certain way, not just that something is wrong.

Observability is a measure of how well the internal state of a system can be inferred from its external outputs. In software and IT operations, observable systems produce rich telemetry — metrics, distributed traces, structured logs, and events — that together allow teams to understand system behaviour, detect problems, and diagnose root causes without having to predict failure modes in advance.

Modern applications are distributed, dynamic, and complex. Traditional monitoring — checking whether a server is up or a threshold has been crossed — cannot provide sufficient context when something goes wrong in a microservices architecture or cloud-native environment. Observability gives teams the data and tools to ask arbitrary questions about system behaviour and get useful answers quickly, which directly reduces incident duration and prevents repeat failures.

Observability improves system reliability by giving teams the information they need to detect problems early, understand their scope, and resolve them quickly. It also supports proactive reliability work — identifying bottlenecks, understanding dependencies, and validating that changes have the expected effect — before problems reach production.

Observability helps identify root causes by providing correlated data across every layer of the stack — from user experience to application code to infrastructure to the network. When a checkout page slows down, an observable system can correlate that degradation with a specific service's increased latency, a database query's performance change, and a corresponding infrastructure metric — giving engineers a clear, evidence-based path to the root cause.

Application Performance Monitoring (APM) is the practice of tracking the performance and availability of software applications in real time. APM tools collect metrics such as response time, error rate, throughput, and resource utilisation, as well as distributed traces that show how requests flow through application components. APM enables teams to detect performance degradation, identify slow transactions, and understand where time is being spent in application code.

APM works by instrumenting application code — either via agents, SDKs, or OpenTelemetry — to capture performance data as requests are processed. This data is sent to a centralised platform where it is aggregated, correlated, and visualised. Teams can then see service-level metrics, distributed traces showing end-to-end request paths, error rates, and code-level performance details.

Businesses can monitor application performance by deploying APM agents or OpenTelemetry instrumentation within their applications, and routing the resulting telemetry to an observability platform. On EDGE, APM data is automatically correlated with infrastructure metrics, logs, and real user monitoring data, giving teams a complete picture of application health.

Application performance issues can be identified through APM by monitoring response time trends, error rates, and throughput. Anomaly detection can flag unexpected deviations automatically, while distributed tracing allows engineers to follow a slow or failing request through every service it touches to identify exactly where time is being lost or errors are being introduced.

Infrastructure monitoring is the practice of tracking the health, performance, and availability of the underlying IT components that support applications and services — including servers, virtual machines, containers, Kubernetes clusters, cloud instances, network devices, and storage systems.

Infrastructure monitoring works by deploying lightweight agents on monitored hosts, or by using agentless collection via APIs and protocols such as SNMP, WMI, or cloud provider APIs. These agents collect system metrics — CPU, memory, disk I/O, network utilisation — and send them to a central platform for aggregation, visualisation, and alerting. On EDGE, infrastructure data is automatically correlated with APM, logs, and other signals.

With EDGE, organisations can monitor physical servers, virtual machines, containers, Kubernetes clusters, cloud infrastructure (AWS, Azure, GCP), on-premises hardware, network devices, databases, and more. The platform supports over 100 integrations, enabling broad coverage of heterogeneous environments.

Infrastructure monitoring improves system reliability by providing early warning of resource contention, capacity issues, and hardware degradation before they affect applications. By correlating infrastructure metrics with application performance data, teams can quickly determine whether an application problem is caused by an underlying infrastructure issue, reducing mean time to resolution.

Real User Monitoring (RUM) captures and analyses the actual experience of real users as they interact with web applications and mobile apps. Unlike synthetic monitoring, which uses scripted tests, RUM collects data from genuine user sessions — including page load times, interaction delays, JavaScript errors, and network timing — giving organisations an accurate picture of the digital experience their users actually receive.

RUM stands for Real User Monitoring. It is the practice of collecting performance and experience data directly from end users' browsers and devices as they use a web or mobile application. RUM data includes metrics such as Core Web Vitals, page load time, time to interactive, session replays, and error rates — all tied to real user sessions rather than simulated tests.

RUM works by injecting a small JavaScript agent into web pages (or using a mobile SDK for apps). This agent captures performance timings, user interactions, network requests, and errors as users navigate the application. The collected data is sent to the observability platform, where it is aggregated and analysed to identify performance issues, geographic trends, device-specific problems, and user experience degradation.

RUM improves digital customer experience by revealing exactly what users are experiencing, rather than what synthetic tests predict they should experience. It surfaces slow pages, high error rates, poor Core Web Vitals scores, and session-level issues that correlate with user abandonment or business impact — enabling teams to prioritise improvements that will have the greatest effect on real customers.

Businesses can monitor real user experience by deploying RUM instrumentation in their web or mobile applications. EDGE RUM provides session-level data, geographic performance breakdowns, device and browser comparisons, error tracking, and correlation with APM and backend performance data — providing a complete view from user experience through to application and infrastructure root cause.

Digital experience monitoring (DEM) is the practice of measuring and improving the quality of experience that users have when interacting with digital products and services — including web applications, mobile apps, APIs, and digital workflows. It combines real user monitoring, synthetic monitoring, and performance analytics to give organisations a complete view of their digital customer experience.

Businesses can monitor digital customer experience using a combination of RUM (to capture real user sessions), synthetic monitoring (to proactively test critical user journeys), and session analytics (to understand user behaviour and identify friction). EDGE provides all of these capabilities in a single integrated platform, with data correlated against backend performance for full-stack visibility.

Poor digital experiences can be identified through metrics such as high page load times, elevated error rates, poor Core Web Vitals scores, low Apdex scores, high bounce rates on key pages, and failed synthetic test results. EDGE surfaces these signals proactively, with anomaly detection alerting teams when digital experience degrades below acceptable thresholds.

Research consistently shows that digital performance directly affects user behaviour and business outcomes. Slow page loads increase bounce rates, reduce conversion, and damage brand perception. A one-second delay in page response can reduce conversions by up to 7%. By monitoring and optimising digital performance, organisations can measurably improve customer satisfaction and revenue outcomes.

Incident management is the process of detecting, responding to, resolving, and learning from unplanned disruptions to IT services. Effective incident management minimises the business impact of outages and degradation by reducing time to detect, time to respond, and time to resolve — supported by clear processes, the right tooling, and correlated observability data.

Businesses can detect incidents faster by combining real-time monitoring across the full stack — applications, infrastructure, users, and transactions — with AI-powered anomaly detection that identifies deviations before they escalate into visible outages. EDGE's Overwatch module correlates signals from across all monitoring data to surface incidents and their likely causes automatically.

Incident response time can be reduced through automated alert correlation (reducing alert noise), AI-powered root cause analysis (providing context immediately), and pre-built escalation workflows. When an incident is detected, EDGE provides correlated evidence from APM, infrastructure, logs, RUM, and transaction data, enabling engineers to understand the scope and likely cause within seconds rather than hours.

Root cause analysis (RCA) is the process of identifying the underlying cause of an incident or problem — not just its symptoms. In IT operations, effective RCA requires correlating data from multiple sources: application performance, infrastructure health, deployment history, log events, and user impact. EDGE's AIOps capability automates much of this correlation, surfacing likely root causes with supporting evidence.

Observability improves incident resolution by giving teams the data they need to understand what happened, where it started, and what it affected — without requiring them to know in advance what questions to ask. Observable systems allow engineers to trace a problem from its user impact back through the application stack to its infrastructure root cause, dramatically reducing investigation time and the risk of incorrect diagnosis.

Monitoring as a Service (MaaS) is a fully managed observability model in which a specialist provider deploys, operates, and continuously optimises monitoring infrastructure on behalf of a client organisation. Rather than building and maintaining an internal monitoring capability, organisations consume observability as an ongoing managed service — gaining enterprise-grade visibility without the associated operational overhead.

With NCG's Monitoring as a Service, we deploy the EDGE platform into your environment (or as a managed cloud service), configure monitoring for your specific applications, infrastructure, and user journeys, and provide ongoing support, tuning, and escalation services. Your team benefits from full observability coverage managed by specialists, allowing your engineers to focus on building and operating your products.

The benefits of managed monitoring include faster time to value (no need to build internal capability from scratch), access to specialised expertise, continuous optimisation of monitoring configurations, reduced internal operational overhead, and a predictable service model backed by defined SLAs. For organisations without dedicated observability engineering teams, MaaS provides enterprise-grade coverage from day one.

Monitoring as a Service is particularly valuable for organisations that want enterprise-grade observability without the overhead of building and maintaining it internally — including rapidly scaling technology companies, enterprises undergoing digital transformation, organisations with limited internal monitoring expertise, and businesses that want to focus engineering resources on core product development.

Existing clients experiencing an active incident should go directly to the NCG Support platform. The Support platform is managed by our technical support team and provides the fastest path to incident resolution. Do not use the general contact form for live incidents — go to the Support page and use the client support pathway.

Existing clients can access technical support through the NCG Support platform at /support. The platform provides incident reporting, support request management, and direct access to the technical support team. For general enquiries, use the Contact page.

If you are an existing NCG client experiencing an active live incident, go directly to the Support platform — do not use the general contact form. The support team is managed by Charles Oodo and Tonny Magaki and is equipped to handle active incident response. Provide as much detail as possible about the affected services, the symptoms you are observing, and the business impact.

The NCG Support platform is accessible at /support on this website. Existing clients can submit support requests, report incidents, and access the client support pathway directly from the Support page.

For technical support, existing clients should use the NCG Support platform. The support team is led by Charles Oodo and Tonny Magaki. For new enquiries and demonstrations, use the Contact page or Book a Demo.

NCG operates globally with offices in Kenya, Nigeria, and the United Kingdom. Our clients span multiple regions and our platform is designed to support enterprise environments regardless of geography. Our distributed team provides coverage across time zones.

Yes. NCG is a global technology company serving enterprises across multiple markets worldwide. Our platform, tooling, and consulting expertise support organisations internationally, not just in any single region.

NCG has active operations in East Africa (Kenya), West Africa (Nigeria), and Europe (United Kingdom), and supports clients across these and other regions. Our global reach continues to expand as we serve enterprises in new markets.

Yes. EDGE is an enterprise-grade platform built for global deployment. International organisations can use EDGE to monitor their applications, infrastructure, and digital experience regardless of where their systems are hosted or where their users are located. NCG's consulting team supports international deployments and integration projects.

Yes. NCG provides global enterprise technology solutions including unified observability, AIOps, application performance monitoring, infrastructure monitoring, digital experience monitoring, and managed monitoring services. Our platform and expertise are available to enterprises worldwide.

Technology teams can improve system reliability by adopting a proactive observability approach: monitoring applications, infrastructure, user experience, and transactions in real time; setting SLO-based alerting thresholds; using AI-powered anomaly detection to catch degradation before it becomes an outage; and establishing clear incident response workflows backed by correlated telemetry data. EDGE provides all of these capabilities in a single integrated platform.

Businesses can monitor critical applications using a combination of APM (application performance monitoring) for service-level metrics and distributed tracing, RUM (real user monitoring) to capture actual user experience, synthetic monitoring for proactive availability testing, and log monitoring for error and exception visibility. Correlating these signals in one platform provides the full picture of application health required to protect business-critical services.

Enterprises can improve IT visibility by unifying monitoring data — applications, infrastructure, users, logs, and transactions — into a single observability platform rather than managing separate point tools. This eliminates data silos, reduces investigation time, and ensures that every team has access to the same correlated view of system health. EDGE is designed specifically for this enterprise observability use case.

Organisations can proactively detect system problems using AI-powered anomaly detection that learns normal behaviour patterns and alerts on statistically significant deviations — before users notice a problem. Combined with synthetic monitoring (proactive testing of key user journeys) and predictive alerting on infrastructure resource trends, EDGE enables organisations to shift from reactive incident response to proactive reliability management.