Your IT team is buried in tickets, ignoring most security alerts, and relying on tribal knowledge trapped in one person's head.
AI for IT at mid-market companies. Ticket routing, security alert triage, incident response, and knowledge retrieval. Deployed in 3-6 weeks. $5-$20K.
The Problem
Your IT team was hired to build systems. Instead, they clear tickets all day.
Helpdesk volume that turns engineers into password reset machines: Your IT team handles 500+ tickets a month. About 40% are password resets, access requests, and VPN issues with identical resolution steps every time. L2 engineers spend two hours a day on L1 overflow because there is no smart routing. You are burning $1,800-$5,000/month on tickets that could be auto-resolved. Infrastructure projects sit in the backlog because nobody has time.
Security alerts that are mostly false alarms while real threats get buried: Your SIEM generates 300+ alerts per day. Your security team investigates maybe 60. The rest get marked as reviewed or ignored. Most are false positives. Analysts spend a third of their day chasing noise, so they start skimming. Alert fatigue is not a morale problem. It is a security exposure. One missed critical alert can cost more than your entire annual security budget.
Incident response that depends on whoever happens to be awake: Last month, a production database went down at 2 AM. Total downtime: two and a half hours. The fix was a known issue documented in a Confluence page nobody could find under pressure. MTTR keeps climbing because infrastructure complexity grows while your incident response process is still a PagerDuty alert and a prayer. Every hour of downtime costs $6-$18K in revenue, SLA penalties, and customer trust.
Knowledge trapped in Slack threads and one senior engineer's memory: The workaround for that legacy API integration lives in a Slack thread from 2024. The deployment checklist is in a Google Doc only one person knows about. The monitoring thresholds were set by an engineer who left six months ago. When your senior engineer goes on leave, resolution time doubles. When he resigns, years of operational knowledge leave with him.
Our Approach
Audit the chaos. Automate the obvious. Speed up what remains. We do not sell ITSM platforms. We audit your IT operations, find where AI removes toil, and build systems that plug into your existing tools. ServiceNow, Jira, PagerDuty, Slack, your SIEM. You own everything we build.
Phase 1 — IT Operations Audit (Days 1-3): We pull 90 days of ticket data, alert logs, incident reports, and resolution records. We interview IT leads, helpdesk agents, and security staff. We map every workflow from ticket creation to resolution, alert trigger to investigation, incident detection to post-mortem. We measure time-per-ticket by category, false positive rates, MTTR by severity, and knowledge retrieval patterns. Output: a prioritised map of where AI removes the most manual work. Deliverable: IT operations audit with ticket analysis, alert pipeline assessment, incident response review, knowledge gap inventory, and prioritised AI opportunity list
Phase 2 — Model Design & Integration Planning (Week 1-2): For each AI system, we design the architecture and map integration points with your current tools. Ticket auto-resolution uses classification models trained on your historical data, not generic categories. Alert triage means tuning correlation rules and context-aware scoring against your environment. Knowledge retrieval indexes your Confluence, Slack, runbooks, and post-mortems into a single searchable layer. You review and approve before we build. Deliverable: Model architecture docs, integration specs for existing IT tools, data pipeline design, and validation criteria per system
Phase 3 — Build, Train & Test (Week 2-4): We develop each system and test it against your historical data. Ticket classification gets backtested against 90 days of resolved tickets for routing and resolution accuracy. Alert triage replays historical alerts to measure how many false positives it correctly suppresses without missing real threats. Knowledge retrieval gets tested against common incident scenarios. Every system includes confidence scores and automatic fallback to human review. Deliverable: Trained and tested AI systems with accuracy benchmarks, false positive/negative analysis, and integration testing against your production tools
Phase 4 — Deploy & Handover (Week 4-6): Production deployment with a one-week parallel run. Ticket automation runs alongside your existing queue so your team can validate quality. Alert triage runs in shadow mode, scoring alerts without suppressing them, until your security team trusts it. Knowledge retrieval goes live immediately with feedback loops. Full handover includes training, documentation, monitoring dashboards, and a 90-day performance review. Deliverable: Production-deployed AI systems, team training, full documentation, monitoring dashboards, and model performance tracking
Deliverables
Audit & Design (Week 1-2)
- IT operations audit with ticket volume analysis, category breakdown, and cost-per-ticket by type
- Security alert pipeline assessment with false positive rates and triage bottleneck mapping
- Incident response review with MTTR analysis by severity and root cause patterns
- Knowledge gap inventory across wikis, runbooks, Slack, and undocumented tribal knowledge
- Prioritised AI roadmap with projected ROI per system
Build & Test (Week 2-4)
- Ticket classification and auto-resolution engine for L1 issues (password resets, access requests, standard provisioning)
- Security alert triage model that scores, correlates, and suppresses false positives while escalating real threats
- Incident response accelerator with runbook retrieval, root cause suggestions, and resolution recommendations
- Knowledge retrieval layer that indexes Confluence, Slack, runbooks, and post-mortems into a searchable interface
Deploy & Handover (Week 4-6)
- Production deployment with parallel testing and shadow mode for security systems
- Practical training for IT team, helpdesk agents, and security analysts (recorded)
- Full documentation covering model logic, integration maps, escalation paths, and maintenance guides
- Monitoring dashboards that track auto-resolution rate, alert suppression accuracy, MTTR, and knowledge retrieval hit rate
Who This Is For
Right for you if: You are a mid-market company (50-500 employees, $2M+ revenue) where your IT team spends more than half their time on repetitive L1 tickets instead of infrastructure and security work.. You have a security team or SOC that is overwhelmed by alert volume and you know critical alerts are getting missed.. Your mean time to resolve incidents keeps climbing because knowledge is scattered and response depends on specific people being available.. You have existing IT tools (ServiceNow, Jira, Freshservice, PagerDuty, or similar) with at least 90 days of historical data..
Not right if: You have fewer than 50 employees or a one-person IT function. At that scale, better tooling and processes will help more than AI models.. You do not have a ticketing system or structured IT operations data. AI models need historical data to train on. We can help you set up that foundation, but it is a different engagement.. You want a managed security operations centre (SOC-as-a-service). We create AI that makes your existing security team faster. We do not replace them..
Use Cases
SaaS / Technology: A 220-person SaaS company processed 1,800 IT tickets per month with a six-person IT team. Password resets, access provisioning, and VPN issues were 45% of volume. L2 engineers spent three hours per day on L1 overflow. Infrastructure projects were consistently delayed because nobody had uninterrupted time. MTTR for P1 incidents had crept to 3.5 hours because runbooks were scattered across Confluence, Notion, and Slack bookmarks. — Built a ticket classification model trained on 90 days of historical tickets. Deployed an auto-resolution engine for the top 8 ticket categories: password resets, access requests, software installation, and VPN troubleshooting. Built a knowledge retrieval layer that indexed 400+ pages of documentation and 18 months of Slack threads into one search interface for the IT team. Integrated with Jira Service Management.. Outcome: Auto-resolution handled 42% of incoming tickets without human intervention. L2 engineers reclaimed 2.5 hours per day for infrastructure work. MTTR for P1 incidents dropped from 3.5 hours to 55 minutes. Three delayed infrastructure projects were completed within 60 days of deployment.
Financial Services: A digital lending platform with 350 employees generated 600+ security alerts per day from their SIEM. Their three-person security team could investigate about 120 daily. The rest were bulk-dismissed or rolled into a weekly review that was always two weeks behind. A compliance audit flagged that most alerts went uninvestigated. The CISO knew the team was exposed but could not justify hiring two more analysts at $15-$18K each. — Built a security alert triage model that correlated alerts with asset criticality, user behaviour baselines, and threat intelligence feeds. The model scored every alert on a 1-100 severity scale with context-aware explanations. Alerts below the confidence threshold were auto-suppressed with audit logs. Critical alerts were enriched with investigation context and pushed to the team with recommended response actions. Integrated with Splunk SIEM and Cortex XSOAR.. Outcome: False positive suppression reduced actionable alerts from 600+ to 90 per day. The existing three-person team now investigates 100% of high-confidence alerts instead of 20%. Mean time to investigate dropped from 45 minutes to 12 minutes per alert due to pre-enriched context. The platform passed its next compliance audit with zero findings on alert coverage.
Manufacturing / Enterprise: A manufacturing company with 450 employees across four plants ran IT operations with an eight-person team at headquarters. Remote plants relied on a shared helpdesk email that averaged 72-hour response times. Shopfloor systems had different monitoring tools than corporate IT, and incident response for plant OT systems required one specific engineer who understood the legacy SCADA integrations. When he went on leave, a two-hour outage at one plant took 11 hours to resolve. — Built a multi-channel ticket intake system with AI classification that routed plant-specific issues to the right resolver group immediately. Deployed a knowledge assistant trained on the legacy SCADA documentation, OT monitoring runbooks, and 14 months of incident records. Created an incident response accelerator that pulled relevant resolution history and recommended fixes based on symptom matching against past incidents.. Outcome: Remote plant ticket response time dropped from 72 hours to 4 hours. Resolution time for recurring plant IT issues reduced by 60%. The knowledge assistant successfully guided junior engineers through three SCADA-related incidents that previously required the senior specialist. Single-point-of-failure risk on the senior engineer was eliminated.
Results
How a typical project runs
SaaS — Helpdesk automation & incident response: 42% tickets auto-resolved, MTTR down 74%, $35K annual savings. A 220-person SaaS company with $10M in revenue had a six-person IT team spending most of its time on repetitive tickets. 45% were routine L1 issues. L2 engineers pulled three hours daily to clear the backlog. P1 MTTR had crept to 3.5 hours because runbooks were scattered across three platforms. We built a ticket classification model, auto-resolution engine for the top 8 ticket categories, and a knowledge retrieval layer indexing Confluence, Notion, and Slack. Backtested against 5,400 historical tickets. Total investment: $9,000. Within three months, 42% of tickets were auto-resolved. L2 engineers reclaimed 2.5 hours per day. P1 MTTR dropped from 3.5 hours to 55 minutes. Annual savings: $35,000.
Frequently Asked Questions
How much historical data do we need?
Ticket automation needs 90 days of resolved tickets with category labels and resolution notes. Alert triage needs 60-90 days of alert logs with investigation outcomes. The knowledge layer needs access to your existing documentation, however scattered. If data is incomplete, the audit will assess what is feasible and we scope accordingly.
Will the auto-resolution engine handle tickets incorrectly?
Every auto-resolution includes a confidence score. The engine only acts when confidence exceeds the threshold your team sets, typically 95% or higher. Below that, tickets route to the right person with a recommended resolution attached. For the first two weeks, the system runs in suggestion mode: it recommends but waits for human approval before executing. Your engineers review and correct, which improves the model. After validation, you decide which categories to fully automate.
Does this replace our existing ITSM platform?
No. We build on top of ServiceNow, Jira Service Management, Freshservice, Zendesk, or whatever you use now. Your IT team keeps working in their existing tools. The AI layer adds auto-classification, smart routing, resolution suggestions, and knowledge retrieval. We integrate via APIs, so there is no platform migration.
How do you handle security concerns with the alert triage model?
The alert triage model runs within your environment. Alert data does not leave your infrastructure. The model scores and enriches alerts but does not act on security events unless you explicitly configure it to. Auto-suppressed alerts are logged with full audit trails for compliance. Your security team keeps full override capability.
What does this cost?
$5-$20K depending on scope. A focused engagement covering one area (ticket automation or alert triage) runs $5-$8K. A full engagement covering helpdesk automation, alert triage, and knowledge retrieval runs $10-$20K. The operations audit in the first week gives you a precise scope and cost before you commit to the full build. Every engagement includes a projected ROI based on your actual ticket volumes and operational costs.
How long before we see measurable results?
Ticket auto-resolution shows results right away. You will see ticket volume drop in the first week. Alert triage improvements show up within the first week of production scoring, though most teams run shadow mode for two weeks before trusting the suppression. MTTR improvements depend on incident frequency but are typically measurable within 30-60 days. Knowledge retrieval impact shows in resolution time metrics within the first month.
What if our IT team resists using AI tools?
Common and expected. Every system is built to make your IT team faster. The auto-resolution engine handles the work nobody wants: password resets, access provisioning, VPN troubleshooting. That frees your engineers for the infrastructure and security projects they were hired for. Adoption follows the same pattern: scepticism in week one, cautious testing in week two, active reliance by week four once the ticket queue shrinks.
Can this work if we have a hybrid or multi-cloud environment?
Yes. The AI systems integrate with your monitoring and ITSM tools, not directly with your infrastructure. Whether you run AWS, Azure, GCP, on-premise, or a mix, the models work with the data your existing tools already collect. For incident response, we index runbooks and resolution history regardless of which environment the incident occurred in.
