That IT manager you are paying $45,000 a year? He is spending four hours a day triaging tickets that a model could classify, route, and resolve in seconds.
We build AI systems that auto-resolve L1 tickets, cut security alert noise by 80%, reduce incident response time from hours to minutes, and turn scattered documentation into an instant knowledge layer. For mid-market companies with 50-500 employees whose IT teams are stuck firefighting instead of building infrastructure that scales.
Your IT team was hired to build systems. Instead, they are a ticket queue with a pulse.
Helpdesk volume that turns engineers into password reset machines
Your IT team handles 500+ tickets a month. Roughly 40% are password resets, access requests, and VPN issues that follow identical resolution steps every time. L2 engineers spend two hours a day on L1 overflow because there is no intelligent routing. You are burning $1,800-$5,000 per month on tickets that could be auto-resolved. Infrastructure projects sit in the backlog because nobody has time.
Security alerts where the vast majority are false alarms and 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 and developing shortcuts. 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 (hybrid cloud, microservices, third-party integrations) 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, personal notes, 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 walk out the door.
Audit the chaos. Automate the obvious. Accelerate what remains.
We do not sell IT service management platforms. We audit your IT operations, identify where AI eliminates toil and accelerates response, and build systems that integrate with your existing tools. ServiceNow, Jira, PagerDuty, Slack, your SIEM. You own everything we build.
IT Operations Audit
Days 1-3
We pull 90 days of ticket data, alert logs, incident reports, and resolution records. We interview your IT leads, helpdesk agents, and security team. We map every workflow: ticket creation to resolution, alert trigger to investigation, incident detection to post-mortem. We measure time-per-ticket by category, false positive rates on your alert pipeline, mean time to resolve by severity, and knowledge retrieval patterns. The output is a clear map of where AI cuts the most manual work in your IT function.
Deliverable: IT operations audit with ticket analysis, alert pipeline assessment, incident response review, knowledge gap inventory, and prioritised AI opportunity list
Model Design & Integration Planning
Week 1-2
For each AI system in scope, we design the architecture and map the integration points with your existing tools. For ticket auto-resolution, this means building classification models trained on your historical ticket data, not generic categories. For alert triage, it means tuning correlation rules and building context-aware scoring against your specific environment. For knowledge retrieval, it means indexing your Confluence, Slack, runbooks, and incident post-mortems into a unified retrieval layer. You review and approve the design before we build.
Deliverable: Model architecture documents, integration specifications for existing IT tools, data pipeline design, and validation criteria for each system
Build, Train & Test
Week 2-4
We build the AI systems and test them against your historical data. For ticket classification, we back-test against 90 days of resolved tickets to verify accuracy of auto-routing and auto-resolution. For alert triage, we replay historical alerts through the model and measure how many false positives it correctly suppresses without missing genuine threats. For the knowledge layer, we test retrieval accuracy against common incident scenarios. Every system includes confidence scoring and fallback to human review when the model is uncertain.
Deliverable: Trained and tested AI systems with accuracy benchmarks, false positive and false negative analysis, and integration testing with your production tools
Deploy & Handover
Week 4-6
We deploy to production with a one-week parallel run. Ticket automation runs alongside your existing queue so your team validates routing and resolution quality. Alert triage runs in shadow mode, scoring alerts without suppressing them, until your security team trusts the scoring. Knowledge retrieval goes live immediately with feedback loops so the system improves with use. Full handover includes training for every user, documentation, monitoring dashboards, and a 90-day performance review.
Deliverable: Production-deployed AI systems, team training sessions, complete documentation, monitoring dashboards, and model performance tracking
IT Operations Audit
Days 1-3
We pull 90 days of ticket data, alert logs, incident reports, and resolution records. We interview your IT leads, helpdesk agents, and security team. We map every workflow: ticket creation to resolution, alert trigger to investigation, incident detection to post-mortem. We measure time-per-ticket by category, false positive rates on your alert pipeline, mean time to resolve by severity, and knowledge retrieval patterns. The output is a clear map of where AI cuts the most manual work in your IT function.
Deliverable: IT operations audit with ticket analysis, alert pipeline assessment, incident response review, knowledge gap inventory, and prioritised AI opportunity list
Model Design & Integration Planning
Week 1-2
For each AI system in scope, we design the architecture and map the integration points with your existing tools. For ticket auto-resolution, this means building classification models trained on your historical ticket data, not generic categories. For alert triage, it means tuning correlation rules and building context-aware scoring against your specific environment. For knowledge retrieval, it means indexing your Confluence, Slack, runbooks, and incident post-mortems into a unified retrieval layer. You review and approve the design before we build.
Deliverable: Model architecture documents, integration specifications for existing IT tools, data pipeline design, and validation criteria for each system
Build, Train & Test
Week 2-4
We build the AI systems and test them against your historical data. For ticket classification, we back-test against 90 days of resolved tickets to verify accuracy of auto-routing and auto-resolution. For alert triage, we replay historical alerts through the model and measure how many false positives it correctly suppresses without missing genuine threats. For the knowledge layer, we test retrieval accuracy against common incident scenarios. Every system includes confidence scoring and fallback to human review when the model is uncertain.
Deliverable: Trained and tested AI systems with accuracy benchmarks, false positive and false negative analysis, and integration testing with your production tools
Deploy & Handover
Week 4-6
We deploy to production with a one-week parallel run. Ticket automation runs alongside your existing queue so your team validates routing and resolution quality. Alert triage runs in shadow mode, scoring alerts without suppressing them, until your security team trusts the scoring. Knowledge retrieval goes live immediately with feedback loops so the system improves with use. Full handover includes training for every user, documentation, monitoring dashboards, and a 90-day performance review.
Deliverable: Production-deployed AI systems, team training sessions, complete documentation, monitoring dashboards, and model performance tracking
AI systems that plug into your existing IT stack. No rip-and-replace.
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 rate measurement 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 for each system
Build & Test (Week 2-4)
- AI-powered 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 genuine threats
- Incident response accelerator with automated runbook retrieval, root cause suggestions, and resolution recommendations
- Unified knowledge retrieval layer indexing Confluence, Slack, runbooks, and post-mortems into a searchable AI interface
Deploy & Handover (Week 4-6)
- Production deployment with parallel testing and shadow mode for security systems
- Hands-on training for IT team, helpdesk agents, and security analysts (recorded)
- Complete documentation: model logic, integration maps, escalation paths, and maintenance guides
- Monitoring dashboards tracking auto-resolution rate, alert suppression accuracy, MTTR, and knowledge retrieval hit rate
We build AI for IT operations. These are different engagements.
We scope tightly so timelines stay honest and results stay measurable. Each of these is available as a separate engagement.
Autonomous AI agents that execute multi-step IT workflows end-to-end
If you want AI that goes beyond recommendations and executes actions autonomously (provisioning infrastructure, remediating security incidents, orchestrating complex change management) that is an agentic AI engagement with a different architecture and risk profile.
Agentic AIBroader process automation beyond IT (finance, HR, operations)
If your automation needs extend beyond IT into finance workflows, HR processes, or cross-departmental operations, that is a business automation engagement. IT automation is often the starting point, but the scope and integration complexity are different.
Business AutomationOngoing model retraining, monitoring, and continuous improvement
AI models drift as your ticket patterns, infrastructure, and threat environment evolve. After handover, ongoing model management, accuracy monitoring, retraining, and enhancement is covered under a separate retainer.
AI Operations & Managed SupportIs this right for you?
Right for you if
- You are a mid-market company (50-500 employees, $2M+ revenue) where your IT team spends more than 50% of 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 in the noise.
- Your mean time to resolve incidents keeps climbing because knowledge is scattered and your response process depends on specific individuals being available.
- You have existing IT tools (ServiceNow, Jira, Freshservice, PagerDuty, or similar) with at least 90 days of historical data that AI models can learn from.
Not right if
- You have fewer than 50 employees or a one-person IT function. At that scale, you need better tooling and processes, not AI models. Start with structured ITSM implementation.
- You do not have a ticketing system or any structured IT operations data. AI models need historical data to train on. We can help you set up the data foundation, but that is a different engagement.
- You are looking for a managed security operations centre (SOC-as-a-service). We build AI that makes your existing security team faster. We do not replace them.
What this looks like in practice.
Problem
A 220-person SaaS company was processing 1,800 IT tickets per month with a six-person IT team. Password resets, access provisioning, and VPN issues accounted for 45% of volume. L2 engineers were spending three hours per day on L1 overflow. Infrastructure projects were consistently delayed because nobody had uninterrupted time to work on them. The team's MTTR for P1 incidents had crept up to 3.5 hours because runbooks were scattered across Confluence, Notion, and Slack bookmarks.
What we did
Built a ticket classification model trained on 90 days of historical tickets. Deployed an auto-resolution engine for the top 8 ticket categories covering 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 a unified search interface for the IT team. Integrated with their Jira Service Management instance.
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.
Problem
A mid-market NBFC with 350 employees was generating 600+ security alerts per day from their SIEM. Their three-person security team could investigate about 120 alerts daily. The rest were either bulk-dismissed or rolled into a weekly review that was always two weeks behind. A compliance audit flagged that most alerts were going uninvestigated. The CISO knew the team was exposed but could not justify hiring two more analysts at $15-$18K each.
What we did
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 their 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 NBFC passed its next compliance audit with zero findings on alert coverage.
Problem
A manufacturing company with 450 employees across four plants was running IT operations with an eight-person team based 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 calling a specific engineer who was the only person who understood the legacy SCADA integrations. When he went on leave, a two-hour outage at one plant took 11 hours to resolve.
What we did
Built a multi-channel ticket intake system with AI classification that routed plant-specific issues to the right resolver group instantly. Deployed an AI 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 fix steps 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.