How does ITRobo's AIOps platform actually reduce MTTR?

ITRobo's 14-billion-parameter IT-reasoning model ingests logs, metrics, traces, and changes in real time, clusters them into deduplicated incidents, and ranks probable root causes against a corpus of 2.1 billion real incident transcripts. Agents then execute remediation playbooks (runbook, rollback, scale, failover) gated by policy — delivering 12× faster MTTR vs. legacy ITSM in independent Enterprise Strategy Group validation.

Is ITRobo fully autonomous, or are humans still in the loop?

ITRobo uses supervised autonomy with explicit guardrails. Tier-1 incidents (known, low-blast-radius) are remediated automatically at a 99.97% success rate; higher-risk actions require human-in-the-loop approval via Slack, Teams, PagerDuty, or the ITRobo console. Every agent action is policy-bound, logged, and explainable.

Does ITRobo replace our existing ITSM and observability tools?

No. ITRobo is designed to layer on top of your existing ITSM (ServiceNow, Jira), observability (Datadog, Splunk, New Relic, Dynatrace), and cloud (AWS, Azure, GCP) estates via 180+ native integrations. Tickets, incidents, and runbooks continue to live in your systems of record — ITRobo acts as the autonomous layer above them.

What compliance certifications does ITRobo hold?

ITRobo is SOC 2 Type II, ISO 27001, HIPAA, and FedRAMP Moderate certified — the most comprehensive compliance footprint among pure-play AIOps vendors. All agent actions are recorded in an immutable audit trail with per-tenant data isolation.

How quickly can we get ITRobo into production?

ITRobo guarantees time-to-value of under 21 days in writing — measured from contract signature to first autonomous remediation in production — versus the 6–9 month industry average for AIOps rollouts.

What integrations does ITRobo support out of the box?

ITRobo ships with 180+ native connectors spanning ITSM (ServiceNow, Jira, Zendesk), alerting (PagerDuty, Opsgenie), observability (Datadog, Splunk, New Relic, Dynatrace, AppDynamics), cloud (AWS, Azure, GCP, OCI), data (Snowflake, BigQuery), CI/CD (GitHub, GitLab, Jenkins), and security (CrowdStrike, Wiz, Sentinel). Custom integrations are supported via webhook and a typed SDK.

PLATFORM // AGENT ARCHITECTURE

An autonomous agent stack for the four stages of IT operations.

ITRobo is not another monitoring tool. It is a four-pillar agent system — Detect, Diagnose, Resolve, Govern — built on a proprietary 14-billion-parameter IT-reasoning model trained on 2.1 billion real incident transcripts. Every layer runs on the same causal graph, so the system that sees an anomaly is the same system that fixes it.

01 Detect Signal ingestion & noise collapse
02 Diagnose Causal blast-radius mapping
03 Resolve Supervised autonomous remediation
04 Govern Policy, audit & guardrails

Book a Live Demo Read the Architecture Brief

Validated MTTR: 12× faster vs. legacy ITSM
Reasoning model: 14B-parameter, IT-domain
Time-to-value: <21 days, guaranteed

Severity SEV-1

Source payments-api / prod-eu-west-1

Confidence 0.984

Mode Supervised Autonomy

T+00:00.04 DETECT — 14M events/min collapsed to 1 ranked candidate
T+00:00.38 DIAGNOSE — root cause: stale connection pool in pgBouncer
T+00:01.12 RESOLVE — agent rolled pool & drained traffic
T+00:01.46 GOVERN — policy OK, audit trail sealed

MTTR 42s

Legacy baseline 8m 24s

Toil saved −71%

01

Multimodal signal ingestion

180+ native connectors pull from ServiceNow, Jira, PagerDuty, Datadog, Splunk, AWS, Azure, Snowflake, and your own collectors. Structured and unstructured feeds land in a unified event bus so the reasoning model never sees a partial picture.
- 14M+events / min
- 180+native integrations
02

Reasoning-driven noise collapse

Trained on 2.1 billion real incident transcripts, the model understands that 200 alerts about the same checkout failure are one incident. Duplicate firings, symptom noise, and downstream echoes are deduplicated at inference — not by brittle correlation rules.
- 2.1Btraining transcripts
- 14Bparameter model
03

Ranked, scored incident candidates

Each candidate carries a severity, blast-radius estimate, confidence score, and a natural-language summary an on-call engineer can read in one breath. PagerDuty, ServiceNow, and Datadog data sources are all verified sources of truth.
- 0.984typical confidence
- 99.97%Tier-1 success rate
04

Streaming & change-aware

Detect is change-aware: a deployment, a feature flag flip, or a config push is a first-class signal. The model weights recent-change evidence into its rankings so the queue already reflects what just shipped — not just what just broke.
- <21dtime-to-value
- 1,240+teams in production

Signal ingestion pipeline collapsing 14 million events per minute into one ranked incident candidate. — 14M events / min → 1 ranked candidate. Independently validated at 12× faster MTTR by Enterprise Strategy Group, March 2025.

02 // DIAGNOSE · 03 // RESOLVE

Diagnose builds the causal map. Resolve acts on it — supervised, never fire-and-forget.

Diagnose and Resolve are architecturally inseparable: an agent cannot safely act until it understands the blast radius. ITRobo first builds a live causal graph of the affected services, then executes a policy-gated remediation — runbook, rollback, scale, or failover — always within guardrails you define.

02 // DIAGNOSE

Causal blast-radius mapping, not a guessing tree.

The Diagnose agent reasons over the candidate from Detect, traverses the live service dependency graph, and returns a natural-language root-cause hypothesis with a confidence interval, the affected services, and the supporting evidence. Engineers can interrogate every step before Resolve is allowed to act.

Causal graph: live service & dependency topology, not static CMDB.
Evidence chain: every hypothesis links back to the exact log, metric, and change.
Human-in-the-loop: low-confidence cases route to a human before any action.

03 // RESOLVE

Supervised autonomy: the agent acts, the policy decides.

The Resolve agent executes the action — runbook, rollback, scale, failover — gated by your governance policy. Every action is dry-runnable, reversible, and sealed into an immutable audit trail. This is supervised autonomy with guardrails, not unchecked scripting.

Action types: runbook, rollback, scale, failover, throttle, drain.
Guardrails: policy DSL, blast-radius caps, kill-switch, dry-run mode.
Verified result: 99.97% automated-remediation success on Tier-1 incidents.

See Detect → Diagnose → Resolve run end-to-end on your own telemetry — with a solutions engineer, not a sales deck.

Book a Live Demo

// integration_graph.v2

180+ native connectors. Zero rip-and-replace.

ITRobo ingests signals and ships remediation actions across the systems you already run — grouped by domain, agent-readable, and policy-bound from the first packet.

ServiceNow
Jira Service Management
Cherwell
Freshservice
BMC Helix

Datadog
Splunk
New Relic
Dynatrace
Prometheus

PagerDuty
Opsgenie
VictorOps
FireHydrant
Slack

AWS
Azure
GCP
Snowflake
Kubernetes

Median first-event ingestion under 90 seconds · OAuth + service-account auth · SOC 2 Type II audited data path

> view full connector matrix

// govern_layer

Govern. The trust layer beneath every agent action.

Autonomy without audit is a liability. The Govern pillar is where ITRobo answers the only question that matters to your CISO: what did the agent just do, who allowed it, and where is the evidence?

Declarative YAML policies define what each agent may, must, and may not touch — by service, environment, blast radius, and time window. High-risk actions (data deletion, prod failover, IAM mutation) are routed to a human-in-the-loop approval queue with SLA timers. Every override is logged with reason codes.

Role-based & attribute-based access control (RBAC + ABAC)
Risk-tier auto-approval for defined action classes
Break-glass paths with mandatory post-hoc review

Every observation, hypothesis, action, and outcome is written to a tamper-evident log chain — hash-linked, signed, and exportable to your SIEM in near real-time. Reconstruct any incident as a first-class narrative: who paged, what the model reasoned, what the agent executed, and what changed downstream.

Cryptographic chaining (SHA-256, append-only)
Streaming export to Splunk, Datadog, Snowflake
7-year retention, customer-controlled key custody

The broadest compliance footprint in pure-play AIOps — underwritten by annual third-party attestation and continuous control monitoring. Vendor risk reviews collapse from quarters to days because the evidence is already on, signed, and queryable.

SOC 2 Type II · ISO 27001 · HIPAA · FedRAMP Moderate
Regional data residency: US, EU, UK, APAC
Customer-managed encryption keys (CMEK) on all tiers

12×

faster mean-time-to-resolution

Versus legacy ITSM platforms, measured across 1,200+ production deployments — validated by Enterprise Strategy Group, March 2025.

99.97%

automated-remediation success

On Tier-1 incidents, independently verified by PagerDuty, ServiceNow, and Datadog through shared customer telemetry.

<21d

time-to-value, in writing

Guaranteed in your MSA. Industry average for AIOps deployments sits at 6–9 months; we ship first autonomous action in three weeks.

1B+

automated remediation actions

Crossed the 1-billionth production remediation in October 2025 — every one policy-bounded, logged, and reversible.

An autonomous agent stack for the four stages of IT operations.

One ranked candidate, not 14 million alerts. That is the job of Detect.

Multimodal signal ingestion

Reasoning-driven noise collapse

Ranked, scored incident candidates

Streaming & change-aware

Diagnose builds the causal map. Resolve acts on it — supervised, never fire-and-forget.

Causal blast-radius mapping, not a guessing tree.

Supervised autonomy: the agent acts, the policy decides.

180+ native connectors. Zero rip-and-replace.

Govern. The trust layer beneath every agent action.

Policy Engine & Guardrails

Immutable Audit Trail

Compliance Posture

Four numbers that put legacy ITSM on notice.