Downtime caught before
a customer has to report it
Most teams hear about an outage from a customer complaint, not from their own monitoring. Either the monitoring stayed quiet, or it fired so often that nobody was watching when it mattered. We build an agent that watches uptime, error rate and latency around the clock, and attaches a likely cause to every alert.
Why outages catch teams off guard
A service rarely fails all at once. Latency creeps up, the error rate climbs slowly, and none of it looks like an outage until it already is one. Thresholds set once at launch stop matching what normal looks like for that service six months later.
Industry incident reports usually point at detection time, not fix time, as the biggest chunk of total outage duration. Teams that fix an issue in minutes often took far longer just to notice it, especially outside business hours.
The opposite failure is just as common. Set thresholds tight enough to catch everything, and the team gets paged for every traffic spike, every slow third-party call, every deploy’s momentary blip. Eventually the alerts get muted or ignored. Either way, the first real signal is often a support ticket, by which point the business has already absorbed the damage.
What the agent watches
Uptime, error rate and latency, continuously, across every site and API you connect. Not on a fixed polling interval that misses what happens between checks.
A real baseline per service. It learns what normal traffic, error rate and latency look like for that service at that hour. One generic threshold does not have to cover everything anymore.
A likely cause on every alert. It checks the spike against recent deploys, infrastructure changes and known third-party outages. The first message a human sees already points at where to look.
Your deploy history, automatically. It flags when an error spike started within minutes of a release going out. That is the single most common root cause, and the easiest one to miss under pressure.
Status page drafts for customer-facing outages, written in plain language. These are held for a human to approve and publish, never posted on their own.
A weekly reliability summary with trend lines, so slow degradation that never crossed an alert threshold still shows up before it becomes an incident.
What you still decide
The agent detects and explains. It does not decide an incident is over, and it does not talk to customers unsupervised. Any public status update is drafted by the agent and published only after a human approves it. Fixing the root cause, scaling, rollback, failover, stays with your engineers. The agent’s job ends at a clear alert with a likely cause attached.
Safety rails
A tuning period comes first, where the agent watches and logs what it would have alerted on, without actually paging anyone. Your team compares its judgment against real traffic before thresholds go live. Every alert logs the exact metric, baseline and deploy correlation behind it, so nothing is a guess dressed up as certainty. Rate limits stop a genuine widespread outage from flooding the paging system with duplicate alerts, and a kill switch reverts to your previous monitoring setup instantly.
Price and timeline
| Package | Price | Best for |
|---|---|---|
| Single automation | from $700 | One stack, watching uptime, error rate and latency with alerts tuned to real baselines |
| Department package | from $2,500 | Uptime monitoring plus log triage, code review and release notes for the same infrastructure |
4 to 8 days, most of it spent building a real traffic baseline per service, so alerts are tuned before they ever reach a human.
Related
Shares its alerting pipeline with log triage and alerting, and feeds database reports when an incident needs a quick data check to confirm customer impact. Teams running API integration glue often add uptime monitoring to watch the integrations themselves. Part of automation of everything digital and built the way we build AI agents for our own products. We run this on our own systems, including ProBay, the marketplace we are launching, with an AI agent team and the secure Telegram mini-app infrastructure.
Tell us which services and regions you want watched and we will send back a fixed price and a plan for the first week: get in touch.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does an AI uptime monitoring agent cost?
A single-stack agent watching your existing services starts from $700. A department package covering uptime monitoring alongside log triage, code review and release notes starts from $2,500. The exact price depends on how many services and regions are in scope.
How long does it take to go live?
4 to 8 days. We spend most of it connecting to your services and existing monitoring tools, then building a normal-traffic baseline for each one. A short tuning period follows, before alert thresholds are trusted enough to page someone directly.
Which tools does it connect to?
UptimeRobot, Pingdom, Datadog, Grafana and similar monitoring platforms. Your CI/CD pipeline, so incidents can be correlated with recent deploys. PagerDuty, Opsgenie or Telegram for alerting. Claude turns a metric spike into a plain-language likely cause.
What if the agent pages for a false alarm?
Thresholds are tuned against your own service's real traffic, not a generic default. Every alert names the metric and baseline it tripped, so a human can dismiss a false positive in seconds. That feedback goes straight back into tuning.
Is our infrastructure data safe?
The agent only reads the metrics and logs from the services you connect it to, under your own monitoring platform's permissions. It does not retain data outside the monitoring session, and it has no write access to your infrastructure beyond sending alerts.