case study
VelAI is Finabeo's intelligent SRE Co-Pilot — an AI-powered automation layer that reduces mean time to resolution, eliminates toil, and enhances system reliability for high-performing engineering teams. It automates incident triage, root cause analysis, and guided recovery across the full observability stack.
Industry
Client Overview
Case Study Overview
VelAI is an agentic SRE Co-Pilot that sits across the observability stack, ticketing system, and communication layer. It ingests alerts in real time, classifies and triages incidents automatically, correlates signals across logs, metrics and traces to identify root cause, and suggests remediation actions grounded in historical patterns. Integrations with ServiceNow, Jira, Slack, Microsoft Teams and Google Chat keep the on-call experience native to how engineering teams already work.
the problem
Problem Statement
Modern IT Ops teams face a compounding problem: more systems, more alerts, more tools, and fewer engineers. Manual triage is slow and inconsistent, root cause analysis spans too many consoles, remediation knowledge lives in tribal memory, and every major incident costs disproportionate senior engineering time. The result is rising MTTR, engineer burnout, and reliability metrics that drift in the wrong direction.
outcome
Results We Delivered
An overview of the outcomes we delivered
Engineering teams reclaim time previously spent on triage and manual RCA, on-call rotations become less punishing, and reliability metrics improve as the system captures and reuses learning from every incident. For PE-backed portfolios, VelAI offers a standardised incident management capability that can be deployed across companies to drive a step-change in uptime economics.
approach
Our Approach
An overview of the approach we took to deliver the results
VelAI is built as a multi-agent system with an orchestrator agent managing specialist agents for triage, RCA, conversation, and remediation. It plugs into existing observability, ticketing and communication tools via a governed integration layer, with auth, RBAC and context-store controls designed in from the start. Deployment is typically scoped around a specific pain point — a high-noise service, a painful on-call rotation, or a flagship incident type — with measurable MTTR improvement as the success metric.
application
What We Did
The specifics over what we performed and how we delivered the results
Automated Triage
Instant incident classification and severity assessment using ML-trained models — engineers walk into an incident with context, not a blank page.
Intelligent Root Cause Analysis
Multi-signal correlation across the entire observability stack — logs, metrics, traces and alerts stitched into a coherent incident narrative.
Guided Recovery
Actionable remediation suggestions based on historical patterns from similar incidents — shortening the path from diagnosis to fix.
Learning System
Continuous improvement from every incident handled — the system gets sharper with use, and institutional knowledge stops walking out of the door with senior engineers.
War-Room Coordination
Automates the coordination overhead of major incidents — right people, right channel, right context — across Slack, Teams and Google Chat.
Ticketing Integration
Native integration with ServiceNow, Jira and adjacent tools keeps the record of truth where it already lives, with no parallel bookkeeping.