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Artificial Intelligence/Machine Learning

AI/ML-Driven Cyber Defense


AI chatbots, auto-classification, predictive analytics, and click-to-call integrations that cut incident‑resolution time by 60%.

Predictive Service Desk & Virtual Agent

Machine-learning models mine years of incident tickets, sensor alerts, and change records to predict where and when future disruptions are likely to occur. When a user reaches out—by chat, voice, or web—the NLP virtual agent instantly classifies the request, surfaces recommended fixes drawn from the knowledge base, and, if confidence is high, executes the remediation itself (e.g., password reset, policy push). Only ambiguous or high-risk cases are passed to human technicians, who receive the agent’s context-rich transcript and suggested next steps. The result is shorter queues, faster mean-time-to-resolution, and a service desk that “gets smarter” with every interaction.

Autonomous Threat Correlation & Response

Endpoint, network-flow, and cloud-API telemetry stream into an analytics lake, where AI engines stitch thousands of low-signal events into a single, time-ordered attack narrative. Behavioral and signature-based detectors feed a common graph: when patterns match known tactics or deviate from learned baselines, automated playbooks spin up—isolating suspect hosts, forcing credential rotation, or deploying deceptive lures—often before analysts open a case. Humans remain “in the loop” for escalation and forensics, but Tier-1 triage and containment now occur at machine speed, shrinking dwell time and freeing analysts for higher-value threat hunting.

AIOps Self-Healing Cloud Fabric

Telemetry from infrastructure, applications, and user experience is continuously baselined by AI models that understand normal operating ranges. When the system detects anomalous latency, memory leaks, or config drift, closed-loop automation policy checks run: if safe, code rolls back, capacity scales out, or traffic re-routes—without waiting for a midnight maintenance window. Every corrective action is logged as evidence for compliance, producing an environment that not only reports health but actively preserves it, turning outages into transient blips invisible to end-users.

Digital-Twin CMDB & Smart Asset Governance

Continuous discovery tools scan data centers, cloud tenants, and edge nodes, feeding a real-time configuration-management database. AI engines build a “digital twin” of the enterprise—apps, dependencies, data paths—then model the blast radius of any planned change. The same telemetry scores asset health and utilization, forecasting license wastage, looming end-of-life components, and patch urgency. Automated workflows reclaim shelf-ware licenses, schedule just-in-time hardware refreshes, and tee up cyber-remediation tickets, turning the CMDB from static inventory into an active decision-support platform.

Case Studies


AI-Driven ITSM / Predictive Analytics


  • Challenge:  Asset lifecycle and license usage were opaque, causing audit findings.
  • Solution: G2IT deployed AI modules to predict asset failure and recommend patch windows; CMDB reconciled nightly with discovery scans.
  • Impact: 85 % CMDB accuracy (up from 46 %) with 60 % faster ticket closure

Chatbot Auto-Routing


  • Challenge: Tier-1 help-desk lines spiked during mobilizations, leading to long hold times.
  • Solution: G2IT stood up Chatbot triaged requests, mapped keywords to knowledge articles, and created/assigned tickets with correct priority and queue.
  • Impact:50% reduction in live-agent calls. First-contact resolution +18%

AI-Powered Omnichannel Service Desk


  • Challenge: The Navy needed to absorb >1 M annual, non-IT help-desk requests and turn raw ticket data into predictive insights.
  • Solution: G2IT designed an AI suite (chat-bot, incident auto-fill, automated routing, workload forecasting, solution prediction) tightly integrated with NAVY 311’s BMC infrastructure. ML models ingest live SDE data; RPA bots (Automation Anywhere) push actions to down-stream systems.
  • Impact: Self-service deflected 35 % of tickets; Mean Time-to-Resolution (MTTR) fell 42 %; predictive outage alerts now fire 2 hrs ahead of user impact.