AI monitoring & autonomous AIOps
Effred SIM: AI monitoring that fixes what it finds.
Effred SIM — Effred’s Super Intelligence for Monitoring — puts autonomous AI agents on your production applications. They monitor, troubleshoot, find the root cause, patch and resolve issues on their own, within the policies you set, and respond in 100 ms on average and 0.5 s at most.
An example incident, INC-2041 “Checkout slow for some users”, handled by a SIM agent: detected at 14:02:07 with p95 latency on checkout at 4× its baseline; 38 alerts across 3 services grouped into one incident; root cause found at 14:02:09 — deploy v2.14.3 had cut the database pool from 50 to 5; rolled back to v2.14.2 at 14:02:12 with a canary from 10% to 100%, within policy; verified at 14:04:40 with latency back at baseline and the report filed to ServiceNow.
- 100 msaverage response time
- 0.5 smaximum response time
- 24×7agents on watch, every day
- 5 signalsmetrics, logs, traces, events and synthetic checks
- Every actionlogged with its evidence, rollback ready
An AI monitoring solution that does the work.
Effred SIM is an AI monitoring solution and AIOps platform for production applications. Instead of sending alerts for people to chase, its agents — Effred’s Super Intelligence agents — do the operational work themselves: they watch metrics, logs, traces, events and synthetic checks, group related alerts into one incident, find the root cause with the evidence behind it and fix it with your own runbooks.
Think of it as an AI SRE agent on every shift. It understands your system — services, dependencies and recent changes — so it can tell a bad deploy from a busy hour. Autonomy is policy-based: the agents act on their own within the rules and change windows you set, risky actions wait for a person’s approval, every action is logged with its evidence and a rollback is always ready.
That makes SIM more than a dashboard. It brings production application monitoring and AI observability together with AI root-cause analysis, auto-remediation for self-healing applications, AI incident management and automated patch management, all run by autonomous monitoring AI agents, 24×7.
From the first odd signal to the closing report.
Effred’s Super Intelligence agents handle the whole incident: the work an on-call engineer would do, done 24×7 within your rules.
- 01
Monitoring
Metrics, logs, traces, events and synthetic checks from every service, watched 24×7 against baselines learned for each signal, not fixed thresholds.
- 02
Troubleshooting
Agents investigate the way an experienced SRE does: follow the dependency map, check what changed and test each lead against live signals.
- 03
Root-cause analysis
Every incident gets a root cause with its evidence — which change, which component, which signal — written in plain words.
- 04
Patching
Patches planned into your maintenance windows, rolled out canary first with health checks at each stage, and rolled back automatically if anything degrades.
- 05
Issue resolution
Runbook-driven fixes, such as a restart, a scale-out or a rollback, run within your policies and are checked against the same signals before the incident closes.
- 06
Understanding your system
A live map of your services, dependencies and deployments, so every decision is made with the whole system in view, not from a single alert.
How Effred SIM works. Connect once; the agents take it from there.
Connect and learn once; after that every incident runs through the same three steps, with the same evidence, the same policies and the same report.
Send metrics, logs, traces and events through OpenTelemetry-compatible collectors or from Prometheus, and connect the clusters, clouds and databases SIM should watch. Your Grafana dashboards stay as they are.
SIM builds a live map of the application’s services and dependencies, and learns a baseline for every signal: what normal looks like for each service at each hour.
Anomalies are caught against those baselines, and related alerts across services are grouped into one incident, so the noise drops and the real problem stands out.
Agents troubleshoot along the dependency map, compare recent changes and pinpoint the root cause — the change, the component and the signal — with the evidence attached.
The fix runs from your runbooks, within policy, in stages with automatic rollback. SIM checks the signals are back to normal, updates ServiceNow, Slack or Microsoft Teams and writes the post-incident report.
Step 1, Connect: the Integrations screen: OpenTelemetry, Prometheus, Grafana, Kubernetes, AWS, PostgreSQL, ServiceNow and Slack connected.
Beyond traditional monitoring and APM.
SIM reads the signals your tools already collect, and takes on the work that starts after the alert.
| Traditional monitoring & APM | Effred SIM |
|---|---|
| Alerts for people to read and chase | Incidents with a root cause and the evidence behind it |
| An alert storm when one service fails | Related alerts grouped into one incident |
| Fixed thresholds that page too often, or too late | Baselines learned for each service and signal |
| Runbooks in a wiki, run by hand at 3 a.m. | Runbooks run by agents, within your policies |
| Patches planned and applied by hand | Patches in your maintenance windows, staged and reversible |
| Incident reports written afterwards | A timeline and report written as the incident is handled |
Built for production. The technical details.
Standard signals in, evidence and safe actions out — on the platforms your applications already run on.
Response time
100 ms
average response time
0.5 s
maximum response time
Response time is the time from detection to first action inside Effred SIM. The clock starts when SIM’s detection engine flags a signal as anomalous against the baseline it has learned, and stops when a SIM agent takes its first action on it: it opens the incident and starts the diagnosis. The average is 100 ms; the maximum is 0.5 s.
- What starts the clock
- SIM’s detection engine flags a signal (a metric, log, trace, event or synthetic check) as anomalous against the baseline it has learned for that service.
- What stops it
- A SIM agent takes its first action on that detection: it opens the incident and starts the diagnosis.
- What it does not include
- The time signals take to reach SIM from your systems, such as collector export or scrape intervals, and the time a fix takes to complete, such as a staged canary rollout.
- How it is reported
- As two figures, the average (100 ms) and the maximum (0.5 s), both measured from timestamped events in SIM’s own processing pipeline. In a demo we show the measured figures on your stack.
- Signals
- Metrics, logs, traces, events and synthetic checks
- Standards
- OpenTelemetry-compatible ingestion, Prometheus metrics and Grafana dashboards; signals from Datadog, New Relic, Dynatrace, Splunk, Elasticsearch and cloud monitoring
- Environments
- Kubernetes, Docker, VMware, Linux and Windows Server; AWS, Microsoft Azure, Google Cloud and Oracle Cloud; and databases
- Detection
- Baselines per service and signal, anomaly detection, event correlation and noise reduction
- Root-cause analysis
- Evidence-backed: the change, the component and the signal
- Remediation
- Runbook-driven actions, canary and staged rollouts, automatic rollback
- Patch management
- Maintenance windows, staged rollout, approval for risky patches
- Reporting
- An incident timeline and a post-incident report for every incident
- Hand-off
- ServiceNow, Jira Service Management, PagerDuty, Opsgenie, Slack, Microsoft Teams, email and webhooks
- Coverage
- 24×7, backed by Effred’s support team
- Deployment
- Talk to us: we fit SIM to your environment and policies
Autonomous, within your rules.
The agents act on their own the way enterprises need them to: inside the policies and change windows you set, with a person’s approval for anything risky.
- 01
Policies you set
You decide which actions the agents take on their own, for which services and environments.
- 02
Change windows
Patches and planned changes run only inside the maintenance windows you set.
- 03
Approval for risky actions
Anything you mark as risky, such as a database change, waits for a person’s approval.
- 04
An audit log with evidence
Every action is logged with the evidence behind it, what approved it and the result.
- 05
Rollback, always ready
Every change has a way back, and SIM rolls back on its own when a fix does not bring the signals back to normal.
# Example policy
service: checkout
environment: production
autonomy:
act_on_its_own:
- restart
- scale_out
- rollback
needs_approval:
- db_change
- patch_outside_window
change_window: "Sun 02:00–05:00 IST"
rollout: canary 10% → 50% → 100%
rollback: automatic, on a failed health check
audit: every action, with its evidence
Works with the tools you already run
Signals & observability
OpenTelemetrytraces, metrics & logs
Prometheusmetrics
Grafanadashboards & alerts- Grafana Lokilogs
Jaegertraces
Elasticsearchlogs & search
DatadogAPM & metrics
New RelicAPM
DynatraceAPM- Splunklogs & events
- Amazon CloudWatchAWS metrics & logs
- Azure MonitorAzure metrics & logs
- Google Cloud MonitoringGoogle Cloud metrics
Clouds & environments
Kubernetesclusters
Dockercontainers
Linuxservers- Windows Serverservers
- VMwarevirtual machines
- AWScloud
- Microsoft Azurecloud
Google Cloudcloud- Oracle Cloudcloud
Databases & data
PostgreSQLdatabase- MySQLdatabase
- Microsoft SQL Serverdatabase
MongoDBdatabase
Rediscache
Apache Kafkaevent streaming
CI/CD & change
GitHubcode & deploys
GitLabcode & pipelines
Jenkinspipelines
Argo CDGitOps deploys
Terraforminfrastructure as code
Ansibleconfiguration
HelmKubernetes releases
Hand-off, ITSM & on-call
- ServiceNowITSM
- Jira Service ManagementITSM
PagerDutyon-call
Opsgenieon-call- Slackchat
- Microsoft Teamschat
Emailnotifications
Webhooksany tool with an endpoint
Product names and logos are trademarks of their owners and are shown to indicate compatibility. SIM also connects to any tool with an API, webhooks or OpenTelemetry support. Logos via Simple Icons; OpenTelemetry logo CC BY 4.0, Jenkins logo CC BY-SA 3.0.
How to choose an AI monitoring solution.
Comparing AI monitoring tools or AIOps platforms? Six questions separate the ones that act from the ones that only alert.
- 01
Does it act, or only alert?
An AI monitoring solution should troubleshoot and fix within your rules, not just send smarter alerts.
- 02
Can you see the evidence?
Every root cause should name the change, the component and the signal, so your engineers can check it.
- 03
Does autonomy follow your policies?
You should decide what runs on its own, when changes are allowed and what needs a person’s approval.
- 04
Can every action be undone and audited?
Look for staged rollouts, automatic rollback and a log of every action with its evidence.
- 05
Does it use the signals you already collect?
OpenTelemetry-compatible ingestion and Prometheus and Grafana support save you from instrumenting everything again.
- 06
How fast does it respond, and how is that measured?
Ask for the response time, from which moment to which, and for both the average and the maximum. Effred SIM responds in 100 ms on average and 0.5 s at most, from detection to first action inside SIM.
Questions about Effred SIM
An AI monitoring solution watches your applications and infrastructure the way an experienced engineer would: it learns what normal looks like from metrics, logs, traces and events, spots what is out of line and explains why. Effred SIM goes further: its agents also troubleshoot, find the root cause, and patch or fix the issue within the policies you set.
Traditional monitoring and APM tools collect data, draw dashboards and send alerts, and people do the rest. SIM’s agents read the same signals, group related alerts into one incident, find the root cause with the evidence behind it and act on it — a restart, a scale-out, a rollback or a patch — then check that the fix worked and write the incident report. It works alongside the monitoring you already run, such as Prometheus and Grafana.
Yes, within the policies you set. The agents run the runbook action, check the result and roll back if it does not help. You decide which actions run on their own and which wait for a person’s approval, such as a database change, and every action is logged with its evidence.
They measure how fast Effred SIM goes from detecting a problem to acting on it. The clock starts when SIM’s detection engine flags a signal as anomalous against its learned baseline, and stops when an agent takes its first action on it: it opens the incident and starts the diagnosis. On average that takes 100 ms, and the maximum is 0.5 s. Both figures are measured from timestamped events in SIM’s own processing pipeline. They do not include the time signals take to reach SIM from your systems, such as collector export or scrape intervals, or the time a fix takes to complete, such as a staged canary rollout. In a demo we show the measured figures on your stack.
Patches go out only in the maintenance windows you set, in stages: a canary first, then the rest, with health checks at each step and an automatic rollback if anything degrades. Patches you mark as risky wait for a person’s approval, and each one is recorded with what changed and why.
Production applications on Kubernetes, Docker, VMware, Linux and Windows Server, on AWS, Microsoft Azure, Google Cloud or Oracle Cloud, together with their databases. Signals come in through OpenTelemetry-compatible collectors, Prometheus and the monitoring tools you already run, and incidents are handed off to ServiceNow, Jira Service Management, PagerDuty, Opsgenie, Slack, Microsoft Teams, email or webhooks. For anything else, talk to us.
Look at what it does after it detects something: does it only alert, or can it find the root cause and fix it? Check that every root cause comes with evidence, that autonomy follows your own policies and change windows, that every action can be audited and rolled back, and that it works with the signals you already collect. Then see it on your own stack before you decide.
Yes. SIM’s agents watch your applications around the clock, and Effred’s support team works 24×7. We work with companies in 14 countries, shift our hours for teams in the US and invoice in INR, USD, AED, GBP or EUR. Working with us from the US.
Guides on AI monitoring and operations
Related services
Put Effred SIM on watch.
Book a call and bring your engineers’ questions: how the agents decide, what your policies control and how the 100 ms average and 0.5 s maximum response time are measured on your stack. A person replies within one working day.
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