Stop chasing
alerts.
Find the
root cause.
RLM finds what your monitoring tools miss: the root cause hidden inside thousands of alerts. It delivers clear answers in minutes while your sensitive data stays protected in your environment.
Your alerts are piling up.
Your team can't investigate them all.
Monitoring tools are designed to detect problems, but root cause investigation still depends on human effort. That process is slow, expensive, and doesn't scale.
Other AI Summarizes. RLM Investigates.
Most AI tools summarize your alerts. RLM investigates them the same way a senior engineer would. It forms a theory, tests it against your data, and writes up exactly what went wrong and what to do about it. And your actual log data never leaves your environment.
Three agents. One answer.
RLM does not ask one AI to guess the root cause. Three specialized agents investigate the incident together. The first identifies the signals that matter. The second tests the theory against the evidence. The third connects the findings and determines what actually went wrong.
Identify the signal. Test the theory. Find the root cause.
- Reviews thousands of events
- Identifies patterns, timing, and affected systems
- Separates meaningful signals from noise
- Forms an initial root cause hypothesis
- Creates a targeted plan for deeper investigation
- Reviews the initial hypothesis
- Identifies what evidence is missing
- Requests additional data for investigation
- Tests the theory against the evidence
- Confirms or challenges the root cause
- Connects the evidence across the incident
- Determines the root cause and confidence level
- Explains the sequence of events and impact
- Recommends immediate actions and next steps
- Delivers an escalation-ready RCA
The AI investigates your data.
It never reads your data.
Most AI tools send raw logs to an AI provider for analysis. That can expose internal IPs, usernames, error messages, and other sensitive operational data. RLM works differently. Your raw logs stay where they already are, inside your monitoring tools. RLM creates a compact abstract of the data inside a secure sandbox for analysis. The AI reasons from that abstract and requests additional evidence when needed. Your underlying log data never enters the AI's context.
Every tool sees the fire.
None of them put it out.
Today's monitoring tools are good at one thing: telling you something is wrong. They send an alert, maybe cluster a few related ones together, and then hand the problem back to a human. That human then spends the next hour digging through logs trying to figure out why. RLM skips that step entirely — it does the digging itself.
Seeing the problem is not the same as finding the cause.
| Traditional Monitoring | AI Summarization | ⬡ RLM | |
|---|---|---|---|
| Detect | Finds unusual activity and fires alerts | Finds patterns across alerts | Starts with the events that matter |
| Investigate | Engineer searches through logs and events | AI summarizes what it sees | AI forms a theory and tests it |
| Evidence | Human connects the dots | AI explains the available information | Evidence gathered through actual data queries |
| Root Cause | Hours of manual investigation | Best effort explanation | Evidence-backed RCA in minutes |
| Action | Engineer determines what to do next | Engineer validates the summary | Clear recommended action and escalation decision |
| Knowledge | Often stays with the engineer | Usually ends with the summary | RCA becomes a reusable operational artifact |
| Vendor | What they do | Where they fall short | How RLM is different |
|---|---|---|---|
|
⬡ Gekko RLM
Our Solution
|
Three AI agents work in sequence: one forms a theory about what went wrong, one tests that theory by running targeted checks against your data, and one writes a clear report with a confidence score — all before anyone is paged | Sets the standard | Your raw logs never touch an AI provider. Ever. |
|
Datadog
Observability Platform
|
Datadog's Bits AI agent wakes up when an alert fires, forms a hypothesis, and tests it by pulling logs, metrics, and traces — often completing an investigation in a few minutes with no human involved | Your raw data goes straight into the AI's context window — a compliance risk in regulated industries. Cost grows with every investigation | RLM performs the same autonomous investigation but keeps all raw data inside a secure sandbox. Works alongside Datadog rather than replacing it |
|
Dynatrace
Observability Platform
|
Builds a live map of every service and dependency in your infrastructure and traces failures through that map to find the root cause automatically | Only works if your entire infrastructure runs Dynatrace's own agent. If a system isn't on their platform, the investigation stops there. Enterprise-only pricing | RLM works alongside any existing monitoring stack — no rip-and-replace required |
|
LogicMonitor
AIOps Platform
|
Edwin AI connects to thousands of tools, runs multiple agents in parallel, and won't assert a cause until logs have actually confirmed it — a deliberately evidence-first approach | Enterprise pricing and complexity. Designed for large IT teams with dedicated staff to manage the platform | RLM delivers the same evidence-first philosophy at a price and deployment complexity that works for teams without a dedicated NOC |
|
SolarWinds
Traditional Monitoring
|
Widely deployed network and infrastructure monitoring. Tracks performance metrics and sends threshold-based alerts across your environment | Built on rules set by humans. When something unusual happens that wasn't anticipated, SolarWinds fires an alert and stops there — no investigation, no root cause | RLM handles everything SolarWinds can't: the moment an alert lands, it investigates autonomously and tells your team exactly what caused it and what to do next |
|
Zabbix
Traditional Monitoring
|
Open-source monitoring platform that tracks servers, networks, and applications. Highly customizable and free to use | Root cause analysis means an engineer combing through trigger logs by hand. No AI layer, no autonomous investigation | RLM adds the AI investigation layer Zabbix has never had — turning its alert stream into actionable root-cause reports without replacing the platform your team already knows |
|
Nagios
Traditional Monitoring
|
One of the most established open-source monitoring tools. Watches hosts and services, sends alerts when things go down, and has a massive library of community plugins | No machine learning, no AI, no correlation across alerts. When Nagios fires, someone has to manually investigate. Alert fatigue from noisy checks is a well-known problem | RLM is the upgrade Nagios never got — it sits above your existing setup and handles the investigation that used to fall on whoever was on call at 2 AM |
The alerts your team never reaches aren't just noise.
Some of them contain the exact signals that explain why your systems fail. RLM finds the signal, investigates the evidence, and delivers the root cause in minutes.