The problem with reactive monitoring
Traditional monitoring works on thresholds: CPU above 90%, disk above 85%, queue depth above 1,000. Those alerts are useful — but they fire when the damage is already happening. By the time the dashboard turns red, transactions are timing out and the support phone is ringing.
Worse, generic tools cover only generic metrics. They see that a Java process is consuming memory; they don't see that a specific batch job inside your ERP is queueing 4× more records than the same window last Tuesday. That visibility gap is where production incidents live.
What proactive monitoring actually means
Proactive monitoring shifts the question from "is this metric over the line?" to "is this system behaving like itself?". The platform we operate for managed-services customers is built around four ideas:
Pattern Behaviour Recognition
Every monitored asset has a learned baseline. Alerts fire when behaviour drifts from the pattern — not when an arbitrary threshold is crossed. That's how memory leaks, slow disks and creeping query plans get caught before they page anyone.
5 years of history
Long-horizon retention means we can compare today's load against the same week last quarter or last year. Capacity planning and anomaly scoring stop being guesswork.
Hundreds of custom plugins
Off-the-shelf monitoring stops at the obvious metrics. Over the years we've built hundreds of plugins and connectors for proprietary databases, legacy middleware, niche storage arrays and customer-specific business processes.
Alert routing that respects the contract
Each alert is bound to a notification rule and a contract model — the right person, the right channel, the right severity, with suppression for known maintenance windows.
What we monitor
Customers usually start with a couple of obvious assets — a production database, a critical API — and grow coverage from there. Today the same platform watches everything from bare-metal storage to SaaS ERPs, with a unified alerting model.
| Layer | What we cover |
|---|---|
| Servers & OS | Linux, Windows, AIX, Solaris — CPU, memory, processes, kernel events |
| Databases | Oracle, PostgreSQL, SQL Server, MySQL, MongoDB — sessions, locks, tablespaces, slow queries |
| Middleware | WebLogic, Tomcat, JBoss, IIS, Kafka, RabbitMQ — JVM, queues, threads |
| Storage & backup | SAN/NAS arrays, snapshot health, backup window compliance, restore tests |
| Network | Firewalls, switches, load balancers, VPN tunnels, latency and packet loss |
| Cloud & SaaS | AWS, Azure, OCI, Microsoft 365, NetSuite — quotas, billing anomalies, API health |
| Business APIs | Custom REST/SOAP endpoints, response time, error budgets, contract drift |
| ERP & business apps | NetSuite, Oracle Fusion, SAP — batch jobs, integrations, transactional KPIs |
The plugin layer is the real moat
The base platform is enterprise-grade and well documented. The reason customers stay is the plugin and connector library we've built on top of it: hundreds of probes for systems that no commercial agent supports out of the box.
- Legacy databases and middleware — agents for old Oracle E-Business modules, mainframe gateways and custom Java stacks still running the business.
- Business-process monitors — synthetic checks that simulate a real user journey (login, place order, post invoice) and alert on functional regressions, not just infrastructure.
- ERP and finance probes — NetSuite saved-search watchers, Oracle Fusion integration health, SAP IDoc backlog checks.
- Industry-specific connectors — payment switch health for fintech customers, RPA bot status for shared-service centres, IoT telemetry for facilities.
From alert to outcome
Detection is only the first half. Every alert lands inside a defined response workflow:
- Enriched event — alerts arrive with the asset, owner, last similar incident and runbook attached.
- Routing by contract — different customers have different escalation paths, on-call rotations and severity definitions. The platform respects them.
- Auto-remediation where safe — restart a stuck service, reclaim tablespace, rotate a log, kill a runaway session — all logged, all reversible.
- Trend evidence on every ticket — when an engineer opens an incident, they get the last 12 months of the same metric, not just the spike.
What changes for the business
- Outages caught in pre-failure state, not post-mortem.
- Fewer "why didn't we see this coming?" conversations after incidents.
- Capacity decisions based on five years of behaviour, not vendor heuristics.
- One alerting model across cloud, on-prem, SaaS and custom apps.
- Audit-ready evidence for ISO 27001 and SOC-style controls.
How to start
We typically run a two-week discovery: inventory the estate, identify the top ten failure modes the current tooling misses, and stand up monitoring for those first. If a system isn't covered by an existing plugin, we build one — that's how the library got to its current size.
If you're tired of finding out about incidents from your users, that's the conversation to have.