Spotlight · AI for operations
Why automate
Most operations teams find out about problems from a customer, a late report or a month-end review, after the cost is already in.
We put the numbers in one place, have AI watch them all day and route each problem to a person with the next step ready. Your team keeps the decisions.
Services
See the business
Orders sit in the ERP, tickets in the helpdesk, pipeline in the CRM and the rest in spreadsheets. We bring them into one data layer and build live dashboards around the numbers you run the business on, with a plain-English summary of what changed since yesterday.
One view of the business, current to the hour
See the business
Managers wait days for someone to pull a number. With a question box on top of the same data layer, they ask in plain English, such as late orders by carrier this month, and get the figure with the query behind it.
Answers in seconds, with the source shown
Catch what's drifting
Stock falls below the reorder point, a ticket nears its SLA, a key account's orders drop, a supplier's lead time slips. You choose the signals and thresholds; the right person is notified in Slack, Teams or email with the context to act.
Problems flagged when they start
Catch what's drifting
Not every problem has a threshold. AI learns the normal pattern for each metric, by day of week and season, and flags the unusual: a spike in returns from one site, an expense line twice its usual size, a quiet day that shouldn't be.
The surprises you didn't set a rule for
Get it to the right person
An alert on its own is easy to ignore. Each one opens a task with an owner and a deadline, drafts the obvious next step (a purchase order, a customer update, a carrier chase) and escalates if nobody picks it up.
Every alert has an owner and a next step
Report without rebuilding
The morning ops summary and the weekly management report are rebuilt by hand every time. AI writes them from the data layer: what moved, what's at risk and what was resolved, sent before the first meeting.
The ops report writes itself
Connected data
Operations data layer. ERP (Orders, stock, purchasing), CRM (Accounts, pipeline), Helpdesk (Tickets, SLAs), Warehouse & shipping (Picks, dispatch, returns), Spreadsheets (Plans, targets) feed into Your operations data layer (Synced hourly, watched by AI), which updates Live dashboards (Per team), Alerts (Owner and next step), Escalations (If nobody picks it up), Digests and reports (Written for you).
A week of signals
Example week
Approval gates
| Criterion | Runs automatically | Waits for your approval |
|---|---|---|
| Refresh dashboards and summaries | Yes | No |
| Detect threshold breaches and anomalies | Yes | No |
| Notify and assign the owner | Yes | No |
| Raise a purchase order | No | Yes |
| Send an update to a customer | No | Yes |
| Send the daily digest | Yes | Optional |
How we roll it out
01: Map the numbers
We list the metrics you run the business on and connect read-only to the systems behind them.
Read-only access first02: Build the first dashboard
One live view for one team, checked against the numbers you report today until they match.
Numbers reconciled03: Add alerts and owners
Pick the signals that matter, set thresholds and decide who gets each alert and what happens next.
Human approval on04: Expand and tune
Add teams, anomaly detection and automated reports, and tune thresholds monthly so alerts stay worth reading.
Monthly tuningNeed the workflows behind the alerts automated too? See AI automation. Running logistics or manufacturing? See logistics and manufacturing. Money side of the business? See AI for finance.
FAQ
A live view of the numbers you run the business on, pulled from your own systems rather than exported by hand. The smart part is AI on top: a short summary of what changed and why, anomaly flags, and a question box so managers can ask for a number in plain English and see the query behind the answer.
Commonly ERPs such as NetSuite, Business Central, SAP Business One and Odoo, CRMs, helpdesks, warehouse and shipping tools, accounting software and Google Sheets or Excel. If a system has an API, a database we can read or a scheduled export, we can usually bring it in.
No. For most mid-sized businesses we set up a lightweight data layer as part of the project. If you already have a warehouse such as BigQuery, Snowflake or Postgres, we build on it.
We start with a handful of signals that have a clear owner and a clear action, route each alert to one person rather than a whole channel, and review the thresholds monthly. Alerts nobody acts on are retuned or removed.
It drafts the next step, such as a purchase order or a customer update, and a person approves it. Low-risk steps, like assigning a ticket or posting a digest, can run on their own once you trust them.
Yes. If your team already lives in Power BI, Looker Studio or Metabase, we can feed it from the same data layer and add the alerts, summaries and escalations around it.
With the report someone rebuilds by hand every week, or the problem you keep finding too late. Both give a clear before-and-after to measure.
Next step