Notice the unusual before it becomes expensive.
Most operations already produce the signals that something is off. Nobody has time to watch all of them, and fixed thresholds either stay silent or never stop firing.
The problem
Why it is hard today
Signals live in different places: order systems, sensors, logs, finance exports, support queues. Each has its own dashboard, and each dashboard assumes someone is looking at it.
Teams usually respond with static thresholds. Set them tight and people learn to ignore the alerts. Set them loose and the real problem shows up in a customer complaint or a month-end report.
When something is flagged, the person receiving it still has to rebuild the context: what normal looks like, what changed, who owns it, and whether it has happened before.
Sounds familiar?
- Alerts everyone has learned to mute
- Problems found in weekly reports, not when they started
- Every flag starts with a round of digging for context
- Nobody is sure who owns an issue that spans two systems
How the workflow runs
From signal to next step
- 01 / NOTICEDeviation detected
A metric, record, or event moves outside the pattern expected for that time, place, and segment.
- 02 / REASONContext assembled
The agent pulls related records, recent changes, and similar past cases, and estimates how unusual and how costly it looks.
- 03 / ACTRouted with evidence
The owner gets a short brief with the evidence. Low-stakes cases are logged; high-stakes ones wait for a decision.
Where people stay in control
Autonomous where it helps
- People decide what counts as an incident. The workflow proposes; it does not declare.
- Escalation thresholds are agreed with the team and visible to them.
- Every flag keeps the evidence it was based on, so a dismissal can improve the next one.
Typically connects to
- ERP and order systems
- Data warehouse or BI exports
- Monitoring and logs
- Ticketing tools
- Slack, Teams, or email
What you see
Evidence, not a black box
The workflow shows what it noticed, why it matters, and what it proposes, inside the tools your team already uses. Every step is recorded. We agree on the measures before launch and review them with your team.
- Time to awareness
- From the first deviation to the owner seeing it.
- Useful flag rate
- Share of flags the owner confirms rather than dismisses.
- Alert load
- Flags per person per week, kept low enough to be read.
- Misses
- Issues found outside the workflow that it should have caught.
What the owner sees: the signal against what normal looks like for this store and weekday, and a short brief with the evidence.
TodayRefunds per hour
Store 14 · Tuesday
- Expected range
- Actual
Flag brief
3.1× usualRefunds well above the usual Tuesday level
- Where
- Store 14 · Kitchenware
- Started
- 09:30, Tuesday
- Likely cause
- Price change went live on Monday; refund notes mention “charged more than the shelf price”.
- Similar cases
- Two in the last year, both pricing errors
- If it continues
- About €1,900 in refunds per day
- Signals watched
- 1,284
- Flags raised
- 3
- Confirmed
- 1
- Dismissed with reason
- 2
Is it a good fit?
Not every task needs an agent
Good starting point when
- The signal already exists in a system you can access
- Someone owns the response
- Missing it has a real cost
- There is some history of what normal looks like
Probably not yet when
- The data is not collected yet
- A simple fixed rule already catches it reliably
- Nobody can act on the flag once it arrives
Start with a conversation
Does this look like your operation?
Bring one process that costs your team time. We'll help you decide whether AI fits, what to build first, and how to connect it to what you already use.