Learn from both outcomes.
Use successful and failed backup jobs, previous errors, job durations, free repository space and server load. Only information available before job start can support the prediction.
NORTH MAPLE SYSTEMS / IT OPERATIONS SERVICES
What happened before.
What could happen next.
A proposed operations service that brings historical incidents and current signals together, helping your team investigate potential problems before they disrupt work.
Interactive concept · Synthetic data · No trained predictor or live integrations
OPERATIONS OUTLOOK / DEMONSTRATION
Today’s investigation queue.
Example snapshot: 13 Oct 2026, 12:00 UTC. Priorities and outlooks are hand-authored scenarios, not model predictions.
50Synthetic assets in the scenario
6Illustrative patterns to review
10Synthetic past incidents
0Live platform connections
FIRST PROPOSED PILOT / ZABBIX + VEEAM
Will the next backup fail?
Start with evidence.
Our first proposed pilot combines monitoring signals with backup history to investigate risk before the next scheduled job. Begin with approved exports, then add scoped read-only connections. Machine learning and live connectors remain to be implemented.
Compare a small model.
Evaluate simple rules alongside a small machine-learning model. Test on later history and measure detected failures, false warnings and advance notice. Show probabilities only after calibration.
Make the risk understandable.
A future dashboard would show evidence and next checks. An optional chatbot would explain the model’s assessment in everyday language; the LLM does not establish predictive accuracy.
Expand after validation.
Start with a small asset scope. The 50-server scenario is a planning assumption. Wazuh, Fortinet, Intune, cloud and ticket integrations follow only when they add useful evidence.
01 / HISTORYZabbix + Veeam
Timestamped signals and successful/failed job outcomes.
02 / PREDICTIONSmall ML model
Pre-start features, baseline comparison and temporal validation.
03 / REVIEWDashboard + optional chat
Supporting evidence, uncertainty and operator-reviewed next steps.
Proposed design only. No trained predictor, calibrated probability, validated savings or infrastructure action. Existing monitoring stays active. Simple disk-threshold forecasting remains a native monitoring function.
Read the proposed pilot & validation plan ↗MORE THAN A SINGLE THRESHOLD
History. Signals.
A reason to investigate.
Collect the context.
Connect approved monitoring, backup, device and incident records. The real product would preserve source timestamps, asset relationships and missing-data warnings.
Compare what preceded failures.
Learn from earlier incidents and periods where similar conditions did not cause an incident. Validate on later data before calling a pattern predictive.
Explain the outlook.
Show potential incident categories, supporting signals, relevant precedents and next checks. An incomplete history should produce uncertainty, not a confident forecast.
Keep the operator in control.
Start with read-only observation and human review. A future chatbot could prepare allowlisted actions for explicit approval. Existing monitoring stays in place; the current concept executes no infrastructure changes.
A PILOT WE CAN MEASURE
One incident category.
Evidence before expansion.
- DiscoverDefine next-backup failure using Zabbix + Veeam and agree a pre-start warning window.
- EvaluateCompare with current monitoring on held-out history.
- ObserveReview warnings without changing customer systems.
- DecideMeasure missed incidents, false warnings and advance notice.
Planned sources include Zabbix, Wazuh security events, Fortinet firewall/VPN events, backup systems such as Veeam, Microsoft Intune, cloud platforms and service-management records. Available data, licenses and permissions determine scope. No connectors, accuracy or customer savings are demonstrated by this dashboard.
Read the product documentation ↗SEE THE IDEA IN MOTION / ILLUSTRATIVE WORKFLOW
A simpler way
to connect the steps.
Illustrative workflow only. No platform connection, validated prediction, account creation or system change occurs. Reduced-motion settings show the complete static workflow.
IMPLEMENTATION AT A GLANCE / PROPOSED ARCHITECTURE
Complex tools.
A simpler conversation.
Proposed IT Operations implementation: approved connectors bring information from multiple platforms into one conversation. The LLM helps interpret the request; the workflow controls access and any future action.
01 / PEOPLEYour team
Ask a question or request a task in everyday language.
02 / CONVERSATIONOperations chatbot + LLM
Turns questions and requested commands into structured requests; explains evidence and uncertainty.
03 / CONTROLPolicy & orchestration
Scopes data reads. Future changes require an allowlisted task, authorized approval and verification.
↓ Scoped requests · Evidence and results ↑
ZabbixMonitoring signals and alert history
WazuhSecurity events and investigation context
FortinetFirewall and VPN events
VeeamBackup jobs and recovery evidence
Microsoft IntuneDevice observations and compliance
Cloud & incident recordsInfrastructure changes and labeled incident outcomes
Back to the conversationSources · task status · exceptions · audit trail
Ask
“What changed before the VPN interruptions?”
Request
“Prepare a backup and security report for this site.”
Review an action
“Prepare an approved maintenance task.” The exact change must be reviewed before future execution.
Connector data → timestamped evidence and incident history → separately evaluated risk analysis → dashboard and chat. The LLM does not itself prove prediction or authorize changes. Commands that modify systems are a future, separately approved capability; the current concept performs no actions.
TIME & COST IMPACT / ILLUSTRATIVE ESTIMATES
Make the value
visible.
Explore the potential value of reclaimed operator time. These inputs are planning examples, not a price offer or measured customer results.
LET’S START WITH YOUR BUSINESS
Which incidents
keep coming back?
Tell us what fails, which systems record it and what history you have. We’ll scope a feasible first investigation.
North Maple Systems · Ottawa, OntarioPrepare your conversation.
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