Alerting layer

Data-Driven Alerts

An alerting layer that sits on top of your existing metrics and routes signals to owners before drift compounds or targets slip.

Outcome

Alerting system that fires on defined conditions and turns responses into structured changes — tagging records, updating states, and closing the loop.

Typical timeline

3–5 weeks to get first alerts into production, assuming the metrics already exist.

Best for

Teams with reliable metrics that still rely on dashboard watching instead of automated feedback loops.

What you actually get

You get an alerting layer that behaves like part of the system — not a noisy add-on glued to the side of your dashboards.

Alerts carry enough context, ownership, and recommended action that people execute instead of debating what happened.

Rules, routing, and payloads sit in one place so the logic is consistent instead of scattered across half-configured SaaS settings.

  • A signal catalog for the KPIs and events that actually warrant alerts.
  • A deterministic rule engine for thresholds, trends, and missing-data checks.
  • Slack, email, and webhook delivery wired into your existing tools with predictable output.
  • Structured responses: tagging records, updating states, or triggering follow-ups to close the loop.
  • A governance layer so noise stays out and alerts only fire when they should.

You're here when

  • Dashboards only reveal problems after the damage is already done.
  • People refresh reports manually because nobody trusts alerts to tell them first.
  • Built-in tool alerts are either silent or so noisy they’re treated as background radiation.
  • Incidents bounce through chat threads because no one owns the signal or the follow-up.

How it works under the hood

The flow is circular: metrics › evaluation › payload › delivery › feedback › metrics. The system closes its own loop — metrics land once, rules run deterministically, and every alert follows the same structure so behavior stays consistent.

  1. 01

    Pull metrics from your warehouse, metric layer, or source systems into an evaluation pipeline with no manual detours.

  2. 02

    Evaluate thresholds, trends, and absence-of-signal windows in a deterministic rule engine.

  3. 03

    Generate structured payloads: impacted metrics, segments, severity, and the action the owner should take.

  4. 04

    Deliver alerts to Slack, email, or webhooks with predictable latency and consistent formatting.

  5. 05

    Record each alert and outcome so the next evaluation cycle can adjust thresholds from actual behavior, not assumptions.

What the project looks like

First version in 3–5 weeks, depending on how many sources you connect and how noisy the signals are.

Phase 1 – Signal mapping

Strip the noise. Define which signals matter, who owns them, and what counts as a real action before any rules exist.

  • Identify the KPIs and events that merit an alert instead of another dashboard tile.
  • Define owners, routing paths, and the context required for someone to act immediately.

Phase 2 – Build & wire

Build the evaluation jobs and delivery paths so alerts run the same way every cycle.

  • Stand up jobs that read metric tables or APIs on a fixed cadence with no manual steps.
  • Configure Slack, email, or webhook channels with structured, consistent payloads.

Phase 3 – Tune & hand off

Run a burn-in window to eliminate noise, lock thresholds, and hand control to your team without surprises.

  • Use behavior from the burn-in period to tighten thresholds, windows, and routing logic.
  • Document rules, delivery channels, and on-call expectations so the system can be extended without breaking.

What becomes possible after this

Once alerts are stable, they act as a control layer — workflows, escalations, and automations run off the same signals instead of someone staring at charts.

  • Alert logs become a structured backlog for automation and operational fixes, not scattered anecdotes.
  • Dashboards stay quiet because major issues are intercepted and routed before they show up in a chart.
  • Internal tools and APIs can react to signals directly instead of waiting for manual review or after-the-fact cleanup.
Reporting that reflects the same signals Warehouses that keep the inputs stable Long-term support that keeps the loop running

This is overkill if

Your key metrics aren’t stable yet or still shift month to month.

Your team hasn’t agreed on what counts as an actionable signal.

You only need a simple notification, not an alerting layer that becomes part of your system.

Ready to wire the signals?

Show where your metrics live, how they drift, and who needs to know first — Anriku will tell you if structured alerts are actually worth the overhead.