IT Operations & Service Management

Vantage connects to monitoring databases, helpdesk systems, and asset management platforms to give IT teams real-time visibility into system health, automated ticket routing, SLA tracking, and infrastructure cost optimization — all through visual workflows and dashboards.

Detect Infrastructure Incidents and Reduce MTTR

Monitor infrastructure health across servers, applications, and network devices with automated incident creation.

Scenario: An IT operations team manages 500+ servers and applications. They need centralized monitoring that detects anomalies, correlates related events, and creates Jira incidents automatically — reducing MTTR (Mean Time to Resolution).

Workflow Steps:

1. Schedule Trigger — Run every 2 minutes
2. Database Query (PostgreSQL) — Pull system metrics: hostname, CPU %, memory %, disk %, response time (ms), error rate, uptime status, last check timestamp
3. Database Query (PostgreSQL) — Pull application metrics: service name, request count, error count, p99 latency, queue depth, active connections
4. Aggregation — Calculate rolling 15-minute averages and detect trends
5. Filter — Flag alerts:
   • CPU > 90% sustained for 10+ minutes
   • Memory > 95%
   • Disk > 85%
   • Error rate > 1% of requests
   • Response time > 3x baseline
   • Service unreachable (2 consecutive failed checks)
6. AI Enrichment — Correlate alerts:
   • Group related alerts (same host, same application, same network segment)
   • Classify root cause probability: resource exhaustion, application bug, dependency failure, network issue
   • Assess business impact based on service criticality tier
7. Multi-Conditional — Route by severity:
   • Service down or critical component → Create Issue (Jira, Priority: Critical) + Send Email to on-call engineer + Send Message (Slack #incidents) + Dashboard Output (Event Monitor Tile)
   • Performance degradation → Create Issue (Jira, Priority: High) + Send Message (Slack #ops-alerts)
   • Capacity warning → Dashboard Output (List Tile for capacity planning review)
8. Dashboard Output — Populate:
   • Event Monitor Tile — Active incidents with severity and assigned engineer
   • Map Tile — Data center / cloud region status with host health indicators
   • Metric Tile — Uptime %, open incidents, MTTR, alert count (24h)
   • Line Tile — CPU, memory, response time trends by critical service
   • Bar Tile — Incidents by category (infrastructure, application, network, security)
   • Stat Tile — Services: healthy / degraded / down

Key Nodes: Schedule Trigger, Database Query, Aggregation, Filter, AI Enrichment, Multi-Conditional, Create Issue (Jira), Send Email, Send Message, Dashboard Output

Route Helpdesk Tickets Intelligently and Track SLAs

Automatically classify, prioritize, and route helpdesk tickets, with SLA compliance tracking.

Scenario: An IT helpdesk receives 200+ tickets daily. They need automated classification, intelligent routing to the right support group, and real-time SLA compliance dashboards.

Workflow Steps:

1. Schedule Trigger — Run every 5 minutes
2. Zendesk — List Tickets — Pull newly created tickets: subject, description, requester, department, urgency
3. AI Enrichment — Classify each ticket:
   • Category: Hardware, Software, Network, Access/Permissions, Email, Printing, Security
   • Complexity: L1 (how-to/password reset), L2 (configuration/troubleshooting), L3 (engineering/development)
   • Priority: Critical, High, Medium, Low (based on requester VIP status, department criticality, and issue type)
4. Database Query (PostgreSQL) — Pull SLA definitions by category and priority: response time target, resolution time target
5. Multi-Conditional — Route by classification:
   • L1 tickets (password resets, how-to) → Message AI Response (generate auto-reply with knowledge base article link) + Dashboard Output
   • L2 tickets → Assign to the appropriate support group + Send Message (Teams) to the team lead
   • L3 tickets → Create Issue (Jira) for engineering + Send Email to the IT manager
   • Critical tickets (VIP or system-wide) → Send Email to IT director + Send Message (Slack #it-escalation) + Dashboard Output (Event Monitor Tile)
6. Dashboard Output — Populate:
   • Metric Tile — Open tickets, SLA compliance %, average first response time, average resolution time
   • Bar Tile — Ticket volume by category
   • Pie Tile — Tickets by support tier (L1/L2/L3)
   • Line Tile — Daily ticket volume and resolution trends
   • Table Tile — Open tickets with SLA countdown (time remaining to breach)
   • Event Feed Tile — Real-time ticket activity stream
   • Comparison Tile — SLA compliance this week vs. last week

Key Nodes: Schedule Trigger, Zendesk List Tickets, AI Enrichment, Database Query, Multi-Conditional, Message AI Response, Create Issue (Jira), Send Email, Send Message, Dashboard Output

Relevant Integrations: Zendesk, Jira, Slack, Teams Chat

Manage IT Assets from Procurement Through Retirement

Track hardware and software assets from procurement through retirement, monitor warranty status, and optimize costs.

Scenario: An IT asset manager oversees 2,000 laptops, 500 servers, and 150 software licenses. They need lifecycle tracking, warranty monitoring, and license compliance dashboards.

Workflow Steps:

1. Schedule Trigger — Run weekly
2. Database Query (PostgreSQL) — Pull asset inventory: asset tag, type, make/model, serial number, assigned user, purchase date, warranty expiration, cost, status (active, spare, repair, retired)
3. Database Query (MSSQL) — Pull software license inventory: product, license type, seats purchased, seats used, renewal date, annual cost
4. Computed Column — Calculate:
   • Asset age: months since purchase
   • Warranty remaining: months until expiration
   • Replacement eligibility: assets > 4 years old or out of warranty
   • License utilization: seats_used / seats_purchased * 100
   • Cost per user: annual_license_cost / seats_used
5. Aggregation — Summarize by department:
   • Total asset value
   • Average asset age
   • Replacement forecast: units eligible for refresh next quarter
   • License spend vs. utilization
6. Filter — Flag action items:
   • Warranties expiring in next 90 days
   • Assets > 5 years old still in active use
   • Software licenses < 10% utilized (optimization candidates)
   • Software licenses > 95% utilized (need additional seats)
7. Multi-Conditional — Route by type:
   • Warranty expiration → Send Email to procurement with renewal list
   • Over-licensed software → Send Email to IT finance for cost optimization review
   • Under-licensed (usage > purchased) → Send Email to compliance with gap analysis
   • Aging hardware → Dashboard Output to refresh planning queue
8. Dashboard Output — Populate:
   • Table Tile — Full asset inventory with status, age, warranty, and assigned user
   • Bar Tile — Asset distribution by type and age bracket
   • Pie Tile — Software license utilization (utilized vs. shelfware)
   • Metric Tile — Total asset value, average age, upcoming renewals, shelfware cost
   • Forecast Tile — Hardware refresh budget projection for next 4 quarters
   • Line Tile — Monthly IT asset spend trend

Key Nodes: Schedule Trigger, Database Query (PostgreSQL, MSSQL), Computed Column, Aggregation, Filter, Multi-Conditional, Send Email, Dashboard Output

Track Changes and Deployments with DORA Metrics

Track IT change requests through approval, implementation, and post-deployment verification.

Scenario: An IT change management team needs to track all system changes (patches, deployments, configurations), ensure proper approval workflow, and correlate changes with post-deployment incidents.

Workflow Steps:

1. Schedule Trigger — Run every 30 minutes
2. Database Query (PostgreSQL) — Pull change records: change ID, system, type (standard, normal, emergency), requested by, approved by, scheduled date, status (pending, approved, in-progress, completed, rolled back)
3. Search Issues (Jira) — Pull related deployment tickets and their status
4. Join — Link change records to Jira deployment tickets
5. Computed Column — Calculate:
   • Change lead time: days from request to implementation
   • Change failure rate: changes that required rollback / total changes
   • Post-change incident correlation: incidents within 4 hours of a change
6. Aggregation — Calculate change management KPIs (rolling 30 days):
   • Total changes, standard vs. emergency ratio
   • Approval cycle time
   • Change success rate
   • DORA metrics: deployment frequency, lead time, failure rate, MTTR
7. Filter — Flag issues:
   • Emergency changes (require post-hoc review)
   • Changes without proper approval
   • Changes with post-deployment incidents
8. Dashboard Output — Populate:
   • Gantt Tile — Change schedule with implementation windows
   • Metric Tile — Change success rate, DORA metrics, compliance rate
   • Bar Tile — Changes by type and system
   • Timeline Tile — Change → deployment → verification sequence
   • Line Tile — Monthly change volume and failure rate trend
   • Table Tile — Change backlog with approval status

Key Nodes: Schedule Trigger, Database Query, Search Issues (Jira), Join, Computed Column, Aggregation, Filter, Dashboard Output

Example Dashboard: IT Service & Infrastructure Dashboard

Build this dashboard to give your IT leadership visibility into system health, helpdesk performance, asset status, and change management.

Row 1 — IT Health

Row 2 — Infrastructure Status & Alerts

Row 3 — Helpdesk & Asset

Row 4 — Asset Management & Change

Row 5 — Performance & Trends

Getting Started

To build IT operations workflows:

1. Connect your monitoring databases — Add your infrastructure and application monitoring data sources under Integrations
2. Connect your helpdesk — Authenticate Zendesk and/or Jira under Integrations
3. Start with monitoring — Build a Schedule Trigger → Database Query → Filter → Multi-Conditional workflow for system health alerts
4. Automate ticket routing — Use AI Enrichment to classify helpdesk tickets and Multi-Conditional to route them
5. Build the IT dashboard — Event Monitor, Metric, and Bar tiles for system status at a glance