VelAI: Transforming Incident Management for IT Ops Teams
VelAI is Finabeo's intelligent SRE Co-Pilot, an AI-powered automation layer that reduces mean time to resolution, eliminates toil and improves system reliability by automating incident triage, root-cause analysis and guided recovery across the full observability stack.
Company Overview
VelAI is designed for IT Operations and Site Reliability Engineering teams under pressure to keep complex systems reliable while incident volumes, tooling fragmentation and cost pressures all rise.
It is built for enterprises and PE-portfolio companies where mean time to resolution, the average time taken to fix an incident, directly affects customer experience, commercial SLAs and exit valuation, and where engineers are spending too much time on toil, the repetitive manual work of operations, rather than on engineering.
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, so mean time to resolution rises, engineers burn out, and reliability metrics drift in the wrong direction.
Outcome Delivered
VelAI turns incident management from a reactive, senior-engineer-intensive process into an automated, learning, multi-agent workflow, reducing mean time to resolution, removing toil from on-call rotations, and offering PE-backed portfolios a standardised reliability capability that improves with every incident handled.
Executive Overview
VelAI reduces mean time to resolution, removes repetitive toil from on-call rotations, and improves reliability by learning from every incident it handles, with integrations across ServiceNow, Jira, Slack, Microsoft Teams and Google Chat keeping the on-call experience native to how teams already work.
Engineering teams reclaim time once spent on triage and manual root-cause analysis, on-call rotations become less punishing, and reliability improves as the system captures and reuses learning, while 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, turning incident management from a reactive, senior-engineer-intensive process into an automated, learning, multi-agent workflow.
Our Approach
VelAI is built as a multi-agent system, with an orchestrator agent managing specialist agents for triage, root-cause analysis, conversation and remediation. It plugs into existing observability, ticketing and communication tools through a governed integration layer, with authentication, role-based access control and context-store controls designed in from the start.
It sits across the observability stack, the ticketing system and the communication layer, ingesting alerts in real time, classifying and triaging incidents automatically, correlating signals across logs, metrics and traces to identify root cause, and suggesting remediation grounded in historical patterns; 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 improvement in mean time to resolution as the success metric.
Challenges
Challenges Faced
Every engagement starts with a clear-eyed view of what was actually getting in the way, because you cannot fix what you cannot see. Here we set out the real constraints the client was working within, the costs and risks they were carrying, and the gaps that needed closing before any meaningful progress could be made.
Let's Transform Your Enterprise Today
Send an EnquiryMore systems, fewer engineers
Teams face more systems, more alerts and more tools, but fewer engineers to handle them.
Slow, inconsistent triage
Manual triage is slow and inconsistent, so similar incidents get handled in different ways.
Root-cause spans many consoles
Root-cause analysis spans too many consoles, scattering the picture across tools.
Knowledge in tribal memory
Remediation knowledge lives in tribal memory and leaves when senior engineers do.
Deployed solutions
What We Did
A look at how we approached the work, from the first discovery sessions through to the changes we put in place, and the thinking behind each decision along the way. The aim is always the same, to deliver something measurable that the client owns and can build on, rather than a one-off fix that fades once we leave.
Automated triage
It classifies incidents and assesses severity instantly using ML-trained models, so engineers start with context rather than a blank page.
Intelligent root-cause analysis
It correlates signals across the entire observability stack, logs, metrics, traces and alerts, stitched into a coherent incident narrative.
Guided recovery
It suggests actionable remediation based on historical patterns from similar incidents, shortening the path from diagnosis to fix.
Learning system
It improves continuously from every incident handled, so institutional knowledge stops walking out of the door with senior engineers.
War-room coordination
It automates the coordination overhead of major incidents, the right people, channel and context, across Slack, Teams and Google Chat.
Ticketing integration
It integrates natively with ServiceNow, Jira and adjacent tools, keeping the record of truth where it already lives with no parallel bookkeeping.