FLEET UPTIME
99.7%
IoTGo deploys autonomous AI agents that monitor telemetry, detect anomalies, execute remediation playbooks, and optimize device configurations. The detect→act loop, closed. Stop getting paged at 3am for problems the system could fix itself.
Free for founding operators · No credit card required
THE PROBLEM
Every platform surfaces anomalies. None of them do anything about it. You get an alert. You investigate. You remediate manually. At 3am. Again.
70% of IoT pilots stall after 18 months. The complexity of wiring telemetry to ML to alerting to human to action kills momentum. Most teams never get past the dashboard.
Current platforms sell "AI-powered anomaly detection" but ship threshold alerts with an ML veneer. The AI detects. The human acts. There is no closed loop.
HOW IT WORKS
Write playbooks in YAML. Deploy agents to your fleet. Start at observer mode. Promote to autonomous as trust builds.
# temp-spike-remediation.yaml
scope: fleet:warehouse-east/temp-sensors
trigger:
condition: anomaly:spike
threshold: 3σ
actions:
- throttle_device:
duration: 30s
reason: "temp spike exceeding 3σ"
- if: still_anomalous
then: restart_device
- if: restart_failed
then: escalate
level: operator
channel: pagerduty$ iotgo deploy --fleet warehouse-east \
--playbook temp-spike-remediation.yaml \
--autonomy level-2
Deploying agent AGENT-07 to warehouse-east...
[08:42:11] AGENT-07 > MONITORING 2,847 devices
[08:42:11] AGENT-07 > BASELINE loaded (7-day)
[08:43:22] AGENT-07 > ANOMALY dev:WH-E-T0447 temp=42.3°C (3.2σ)
[08:43:22] AGENT-07 > EXEC throttle dev:WH-E-T0447 30s
[08:43:53] AGENT-07 > CHECK dev:WH-E-T0447 temp=38.1°C (1.4σ)
[08:43:53] AGENT-07 > RESOLVED anomaly cleared post-throttle
[08:43:53] AGENT-07 > LOG outcome=success action=throttle| Level | Agent Can | Requires Approval |
|---|---|---|
| 0 — OBSERVER | Monitor + alert | Everything |
| 1 — ADVISOR | Monitor + recommend actions | Operator executes |
| 2 — SUPERVISED | Execute playbooks | Destructive actions |
| 3 — AUTONOMOUS | Execute all playbooks | Escalation only |
| 4 — ADAPTIVE | Suggest playbook changes | New playbook adoption |
FLEET VIEW
Every device, every status, every metric. Color is signal — green means operational, amber means attention, red means act now. No decoration. No wasted pixels.
WH-E-T001
[ONLINE]
23.4°C
14d 3h
WH-E-T002
[ONLINE]
22.8°C
14d 3h
WH-E-T003
[WARN]
38.7°C
14d 3h
WH-E-T004
[ONLINE]
23.1°C
7d 12h
WH-E-T005
[CRITICAL]
54.2°C
14d 3h
WH-E-T006
[ONLINE]
22.9°C
14d 3h
WH-E-T007
[UPDATING]
23.0°C
14d 3h
WH-E-T008
[ONLINE]
24.1°C
3d 8h
WH-E-T009
[OFFLINE]
—
—
WH-E-T010
[ONLINE]
22.6°C
14d 3h
WH-E-T011
[ONLINE]
23.3°C
14d 3h
WH-E-T012
[WARN]
36.1°C
14d 3h
WH-E-H001
[ONLINE]
45.2%
14d 3h
WH-E-H002
[ONLINE]
44.8%
14d 3h
WH-E-H003
[ONLINE]
46.1%
7d 12h
WH-E-V001
[ONLINE]
3.31V
14d 3h
WH-E-V002
[WARN]
2.84V
14d 3h
WH-E-V003
[ONLINE]
3.29V
14d 3h
CAPABILITIES
YAML-defined, version-controlled remediation sequences. Agents can only take actions the playbook authorizes. Auditable, constrained, learnable.
Unsupervised baseline learning per device type. Multivariate scoring, seasonal awareness, drift detection. Not just threshold breaches.
Start at observer. Promote to autonomous as trust builds. Level 0 through Level 4. You're always in control of the control.
Firmware updates deploy to 5% canary first. Agent monitors health for 2 hours. Healthy? Roll to 100%. Not? Automatic rollback.
Ingest from MQTT, HTTP, AWS IoT Core, Azure IoT Hub, ThingsBoard. IoTGo is an intelligence layer, not a replacement for your infrastructure.
Not "what happened" — "what the agent did and whether it worked." Fleet health score. Incidents prevented. MTTR reduction. Cost impact.
IoTGo is building the autonomous fleet management layer IoT engineers actually need. Get early access and shape the product.
Founding operators get lifetime access to Pro tier