CodiLime
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Capability

Observability

Observability gives you end-to-end visibility into the health, performance, and behavior of increasingly complex modern data centers. It lets teams prevent disruptions before the business feels them, across networks, infrastructure, applications and cloud. We pair deep networking, infrastructure and observability knowledge with software engineering, automation and AI, and build observability around your environment.

Measured impact

What we deliver

  • Observability for a modernized data center

    We extend the observability you already have, or build it from scratch, so a modernization program runs with full sight.

    Read the case study →

  • AI-driven observability

    Forecasting, anomaly detection and AI-assisted analysis turn telemetry into answers about what happened, why, and what comes next.

    See Into the Future →

  • Data visualization built for operators

    Dashboards and investigation flows built around how infrastructure teams work, with unified views, topology and drill-downs.

    Read the case study →

  • Application reliability and traffic control

    Observability becomes the trigger for action, feeding remediation, scaling and traffic management already running in your environment.

    Read the case study →

  • End-to-end security visibility

    One view across infrastructure, workloads and traffic, so security signals are read in the same context as everything else.

    Read the case study →

How we do it

  1. Data sources & telemetry

    Everything that emits a signal

    Network · Compute · Storage · Hypervisors · VMs · Kubernetes · Applications · Cloud · Hardware

  2. Data collection & normalization

    One shape for every source

    Telemetry pipelines · Agents · APIs · Syslog · SNMP · Flow data · Data transformation

  3. Context & correlation

    What depends on what

    Topology · Service dependencies · Infrastructure relationships · Application and traffic context

  4. Analytics & intelligence

    From signal to explanation

    Thresholds · Baselining · Anomaly detection · Forecasting · RCA · ML · AI-assisted analysis

    1. Visualization & experience

      Shows people what’s going on

      Dashboards · Unified views · Investigation workflows · Drill-downs · UX tailored to operators

      Operators and platform teams

      see what happened and why

    2. Automation & control

      Acts on it directly

      Alerting · Remediation · Scaling · Traffic control · Security actions · Policy enforcement

      Systems already in place

      act without waiting for a human

How a project runs

  1. Understand the environment and observability goals

    We start with the architecture, the operational model, the existing tooling, and the questions your observability has to answer. That tells us what to monitor, at what level of detail, and which parts of your current stack are worth keeping rather than replacing.

  2. Collect telemetry across all infrastructure layers

    We instrument the environment using the interfaces best suited to each layer: network devices, servers, storage, hypervisors, VMs, Kubernetes, applications, and cloud services. Depending on the environment, this may include metrics, logs, traces, events, flow data, SNMP, APIs, syslog, OpenTelemetry, or native agents.

  3. Normalize, enrich and correlate the data

    Raw telemetry becomes more useful once it is connected with infrastructure and application context. We build pipelines that normalize and enrich the data with topology, service dependencies, resource relationships, traffic patterns, and operational context to create a coherent view across the data center.

  4. Turn telemetry into actionable insight

    On top of the data layer, we apply the right combination of thresholds, rules, baselining, anomaly detection, forecasting, root cause analysis, and ML/AI techniques. The goal is not only to detect that something changed. It is to show what happened, why, and what is likely to happen next.

  5. Design visualization and investigation workflows for operators

    We build dashboards and investigation flows around how infrastructure and platform teams actually work. That includes unified views, intuitive drill-downs, topology views, historical trends, and alert context. We can also make a contextual dashboard available through a simple chat interface: integrated with AI agents, it retrieves contextual data from multiple layers and boosts situational awareness.

  6. Connect observability with automation and control

    Where it makes sense, observability becomes the trigger for action. Alerts and insights can feed remediation workflows, scaling decisions, traffic management, incident response, security controls, or other automation you already run.

  7. Validate the architecture before scaling it

    Instead of committing to a stack on paper, we evaluate candidate technologies against the client’s actual requirements and validate them through PoCs. This helps confirm integration, usability, performance, licensing, and operational fit before the solution is rolled out more broadly.

Technology

  • Monitoring & metrics

    • Prometheus
    • Grafana
    • Zabbix
    • InfluxDB
    • VictoriaMetrics
    • Splunk

  • Logs & tracing

    • Grafana Loki
    • OpenSearch
    • Graylog
    • VictoriaLogs
    • Elasticsearch
    • OpenTelemetry

  • Platforms & service mesh

    • Kubernetes
    • Istio
    • Envoy Proxy
    • Docker

  • Forecasting & AI

    • Prophet
    • LangChain
    • LangGraph
    • Holt-Winters

  • Test & lab environments

    • GNS3
    • EVE-NG
    • Containerlab

Proof of capability

Why CodiLime

  • We have no stake in which tools you buy

    CodiLime does not resell monitoring platforms, so no recommendation we make carries a licence fee for us. We work with open-source, commercial and in-house tools, and the first question is always which parts of your current stack already answer your questions.

  • One team across the network, the platform and the software

    Observability in a data center tends to break at the seams between the network, the platform and the application. Our teams cover all three, so a problem at a seam stays inside one conversation instead of moving between suppliers. Clients describe this in their own words, most often as expertise in both network engineering and software development.

  • Delivered in phases, with a real handover

    An Engineering Manager owns the project on our side, technically and organizationally, and the team is assembled around your problem. We work in phases, and can be used as soon as it is delivered. Training, documentation and handover are part of the scope, and we stay on afterwards only if you want us to.

Insights

FAQ

Good to know

  • How do you cover a data center that runs legacy hardware, virtual machines, Kubernetes and cloud at the same time?

    CodiLime instruments each layer through the interface best suited to it, instead of forcing one agent across the whole estate. Depending on the environment that can mean metrics, logs, traces, events, flow data, SNMP, APIs, syslog, OpenTelemetry or native agents, applied across network devices, servers, storage, hypervisors, virtual machines, Kubernetes, applications and cloud services. The collected data is then normalized and enriched with topology and service dependencies, so the result is one coherent view of the data center rather than a set of separate tools.

  • How do you extend observability across AWS, Azure and Google Cloud without configuring every environment by hand?

    CodiLime automates the onboarding so a new environment is added by code rather than by hand. For a cloud-based network performance management platform, CodiLime configured AWS, Azure and Google Cloud environments, including the flow logs that feed the platform, and built Terraform and Ansible modules that attach new virtual private clouds to it automatically. Manual integration is slow and error-prone, and it is usually what caps how quickly observability coverage can grow. Once the modules exist, adding a VPC is a repeatable step rather than a small project.

  • Can observability act on a service-level objective, not just report that it was missed?

    Yes, when the telemetry is wired into control and not only into dashboards. CodiLime built a reliability-as-code solution that continuously collects telemetry, classifies network traffic, and automatically activates flow control policies in response to service-level objective violations, preventing the service outage that would otherwise follow. The policies are built as automatic control circuits, so they react to changing network behavior automatically. It was implemented in Python and Go.

  • How do you get security visibility into traffic between workloads inside a data center?

    Security visibility inside a data center depends on seeing east-west traffic between workloads, not only what crosses the perimeter. CodiLime built that capability for Mesh7, a cloud-native security observability platform, delivering policy generation with API and service discovery using Envoy Proxy as the insertion point, plugins for the service mesh and Istio ecosystem, and a Go control plane running as a Kubernetes operator. CodiLime also built the service graph that shows those workload interactions from L4 to L7, so a security team can see which services talk to each other and where sensitive data flows. It runs on infrastructure the customer already has, with no changes to the applications themselves.

  • Where do you start with AI in observability if you have none today?

    You start with the data, not with the models. The first stage is a reliable pipeline that collects and normalizes telemetry across systems, because semantic clustering, forecasting and GenAI agents all read from it. CodiLime treats that foundation as its own piece of work with its own result, so it is usable before any AI is switched on. The later stages are optional, and we build only the ones that answer a question you actually have.

  • Can observability alerts predict a failure before it happens?

    Yes, when alerting is driven by a forecast instead of by a threshold that has already been crossed. CodiLime uses time-series models such as Linear Regression, Holt-Winters and Prophet, picked to match the shape of the signal. The alert fires ahead of the breach, for example that latency is projected to cross 200 ms within the next two hours. It carries a confidence level, so an operator gets a timeframe to plan against and a sense of how far to trust it.

  • Can AI identify the root cause of an incident in a data center?

    It can get much closer than correlation alone, because the limit is usually missing context rather than missing data. Most platforms correlate metrics, logs and traces, and some add topology, which still leaves the analysis guessing. CodiLime builds GenAI agents that query network devices directly for their current state, and search internal knowledge bases for similar tickets that were already resolved. Root cause analysis then runs against what the environment looks like right now, not only against patterns from the past.

  • Can observability solutions be tested without touching production?

    Yes. CodiLime builds and runs lab environments on physical, virtual and cloud infrastructure, with emulated topologies deployed on demand through GNS3, EVE-NG, ContainerLab or custom orchestrators. The full lifecycle is automated, from lab bring-up to teardown, so a candidate architecture can be exercised instead of reviewed on paper. Integration, performance and operational fit are proven there before the rollout decision.

Tell us what you already have running.

Which tools are in place, and what they still don’t tell you. CodiLime starts by mapping what can be reused before anything gets replaced.