AI Cloud Ops
AI-Driven Cloud Operations for 24×365 Reliability
INTRO
Detection, diagnosis, and recovery — handled by AI first
Cloud services keep growing, but operations teams do not. Telemetry accumulates by the terabyte, alerts pile up, and finding the root cause of an incident still takes hours. For a global service, someone has to be awake at 3 a.m.
TecAce’s AI Cloud Ops replaces that model. Built on a unified Metric·Trace·Log pipeline, AI detects anomalies before they reach users, identifies root cause with a stated confidence level, and executes pre-approved runbooks. Your engineers step in only where judgment is genuinely required.
01 — Unified Observability Pipeline
Metric, trace, and log signals are collected through a single OpenTelemetry-based pipeline — vendor neutral by design. A Kafka backbone absorbs ingestion spikes, and a ClickHouse columnar store returns queries across petabyte-scale telemetry in seconds.
03 — Confidence-Based RCA & Agentic Remediation
LLM-driven log analysis and graph-based causal tracing identify the cause rather than the symptom, and report it with an explicit confidence score. Above the threshold, a pre-approved runbook executes automatically. Below it, the agent hands a structured brief to an on-call engineer.
02 — AI Anomaly Detection
Threshold alerts only tell you what has already crossed the line. Machine learning models learn the normal shape of your time series and surface the slow degradations that thresholds miss — starting the response before users are affected.
04 — Multi-Cloud Native Operations
AWS, GCP, Azure, and hybrid environments are operated consistently on Kubernetes. Infrastructure is defined as code with Terraform, Helm, and GitOps for repeatable, auditable change — and continuously optimized to keep total cost of ownership down.
HOW IT WORKS
Where the line between automation and human judgment is drawn
Detect — a single Metric · Trace · Log pipeline correlates signals the moment an alert fires
Diagnose — the agent gathers full context, infers root cause, and attaches a confidence score
Resolve — high confidence runs a pre-approved runbook; low confidence hands off to an engineer, and every outcome feeds the AI Knowledge Hub
WHY IT MATTERS
24×365 reliability cannot be reached by manual response alone
As services scale, telemetry grows by terabytes a day while alert fatigue and hours-long root cause investigations stay exactly where they were. Adding headcount does not close that gap — and for most teams, it is not an option.
AI Cloud Ops shifts detection and diagnosis to AI so that a lean team can hold enterprise-grade availability. Human expertise moves to where it actually creates value: verification and decision-making.
“The longest stretch of any incident isn’t fixing what broke — it’s finding what broke.”
IN ACTION
Enterprise-grade cloud operations, already running
99.99% availability across global brand AI services and B2B platforms, with worldwide synthetic testing
Confidence-gated — AI executes only pre-approved runbooks; low-confidence cases escalate to SRE
FDE-delivered — Forward Deployed Engineers embed from diagnosis and design through operation
