Alerts that mean something,
because the noise was filtered out first
Most alerting systems fail by crying wolf until everyone mutes the channel. We build monitoring that distinguishes a real anomaly from normal variance before it alerts anyone, so the alerts that do arrive actually get read.
What it is and who needs it
A monitoring and alerting product watches a set of signals, competitor prices, system metrics, business numbers, and tells your team the moment something genuinely unusual happens, instead of reporting raw numbers nobody has time to check. It fits anything where a delayed reaction costs money or trust: a competitor undercutting prices, a service degrading, a metric drifting in a way that matters. It is not worth building for something you already check daily by habit; the value is in the things that slip through because nobody is watching them constantly.
What is inside
Anomaly detection is tuned to the actual behavior of each signal, since what counts as unusual for one metric is completely normal variance for another, and a flat threshold applied everywhere produces either constant noise or missed real problems. Alerts are deduplicated and grouped, so one underlying issue produces one clear message rather than twenty near-identical ones that bury the signal in volume. Delivery goes wherever your team actually watches, a Telegram channel, a Slack workspace, email, since an alert nobody sees is worse than no alert at all. A dashboard tracks alert history against outcomes, so your team can see which alerts led to a real action and which turned out to be noise, feeding directly into tuning.
How we build it
We start by defining, with you, what actually counts as an anomaly for each signal, since this is a judgment call that depends on your specific business, not a generic statistics formula. Detection logic gets tested against your historical data first, checking it would have caught real past incidents without flagging normal fluctuations as emergencies. We launch watching one or two signals, deliberately narrow, and expand coverage once the alert quality on those is proven. The first weeks after launch are a tuning period: every alert gets reviewed with your team to adjust sensitivity before the system runs unsupervised.
What to watch
The core risk in any alerting system is the slow slide from useful to ignored: if early tuning is skipped and false positives pile up, your team mutes the channel within weeks and a real alert later goes unseen, which is worse than having no system at all. This is why the first weeks after launch are explicitly a tuning period, not an afterthought, with every alert reviewed against whether it represented a genuine issue. The other practical risk is signal drift, what counted as anomalous six months ago may be normal now as your business grows, so thresholds need periodic revisiting, not a one-time setup.
Timeline and price
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $1,500 | One signal watched, basic anomaly detection, Telegram or Slack alerts | 3 to 4 weeks |
| Production | from $4,000 | Multiple signals, tuned thresholds, alert dashboard, cross-signal checks | 4 to 6 weeks |
| Full control (handover-ready) | from $6,800 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 6 to 7 weeks |
Running cost on top of the build is usually $15 to $50 a month in hosting and data collection, depending on how many signals are watched.
What you own at the end
You own the monitoring logic, the alert history and the full source code, running on your own infrastructure. Thresholds and detection rules are documented clearly enough that your own team can adjust them directly as your business changes, without waiting on us.
Related
Pairs with data warehouse and BI product for the data foundation most monitoring draws from, and AI business data analyst for querying the same data on demand instead of only on alert. See the analytics service page for audits and the data layer underneath monitoring. Real builds: the analytics hub case study, where this kind of monitoring caught 184 of 218 real undercut events, and the ProBay marketplace case study. Found out about a problem a week after it started, from a customer instead of a system? Get in touch.
FAQ
How much does a monitoring and alerting system cost?
From $1,500 for watching one signal (competitor prices, a key metric, server health) with basic anomaly detection. A system watching multiple signals with tuned thresholds and a review dashboard runs $4,000 to $6,800.
How long does it take?
Three to four weeks for one signal once you define what counts as a real anomaly versus normal noise. Watching several signals with cross-checks between them takes longer, typically five to seven weeks.
What is the stack?
Python for the monitoring logic and anomaly detection, PostgreSQL for metric history, and delivery through Telegram, Slack or email depending on where your team actually watches for alerts.
Who owns the monitoring rules?
You. The thresholds, the anomaly logic and the code are yours, running on your own infrastructure, not locked inside a monitoring SaaS billed per metric.
How do you stop it from alerting on every tiny fluctuation?
Anomaly detection accounts for normal variance in the specific signal being watched, rather than a flat threshold applied to everything. We tune this against your real historical data before launch, and again after the first weeks of live alerts.