Build Dashboards That
Tell You What To Do
Larger businesses already have plenty of reports. The gap is agreeing which change requires action, who owns it and how quickly they need to respond. We build dashboards around those decisions and alerts.
3 numbers
NOT THIRTY CHARTS
Pings you
WHEN A LINE IS CROSSED
Signed off
METRIC DEFINITIONS AND OWNERS
Dashboards should support a decision
Most dashboards are a museum of last week. Twelve charts, three colours, no targets. You open it, nod, close it.
A useful board names the metric, the target, the owner and the next move. When a number breaks a line, it pings the right person before they ask.
Each important measure should connect to an owner, threshold and agreed response.
YESTERDAY'S DASHBOARD
- Twelve charts, three colours, no targets
- Says down 12% without saying so what
- Open in a tab, read by nobody
- Refreshes when someone remembers
- No owner per number
PRESCRIPTIVE DASHBOARD
- Three numbers that change a decision
- Each number paired with a next move
- Pings the owner when it crosses a line
- Refreshes itself, on a schedule
- A name attached to every metric
What an action dashboard needs
Start with the decision, then give it the few parts it needs. Once you can see the parts, you can build them once.
The one number
The metric this board exists for. If it moves, the week is different. Everything else on the page is there to explain it.
Drivers
The handful of inputs that move the one number. So when it moves, you know where to look without scrolling.
Thresholds
Green, amber, red, with the numbers behind them written down. Off isn't a vibe, it's a band someone agreed to.
The next move
When the number is red, what someone is meant to do about it, and who. Written next to the chart, not lost in a Slack thread.
The loop
An AI agent watches the board, pings the owner when a line breaks, logs what they did, and learns which actions moved the number next week.
From reporting to recommended action
Gartner's Analytic Ascendancy Model frames it as four questions. Reporting answers the first. This service is about the fourth.
What happened?
Last month's revenue. Yesterday's tickets. The chart everyone already has. This is most dashboards.
Why did it happen?
Drill into the drop. Which segment, which channel, which week. The dashboard answers a follow-up question without a meeting.
What's about to happen?
Forecast next month from what's already in the pipeline. Spot the cliff before you walk off it, not during the post-mortem.
What should we do?
The board names the move and the owner. An agent watches it, pings on a breach, and learns from what worked. This is where we build.
Framing adapted from Gartner's Analytic Ascendancy Model: descriptive, diagnostic, predictive and prescriptive analytics.
How we take it into live use
We start with the decision the board is meant to serve, not the data you happen to have. Then we connect what's already in your stack and launch a first version before the scope sprawls.
We agree the decisions, metric definitions and system owners first. Each build phase covers a named set of data sources and leaves you with the dashboard, data model and operating notes.
START A DASHBOARD AUDITDecision audit
A working session with leadership, finance and the operational owners. We name the decisions the dashboard must support, the measure behind each and the response when a threshold is crossed.
Model the data
We connect Xero, HubSpot, Stripe, your CRM, your spreadsheets and the other authoritative sources. Each important number has an agreed definition and source.
Launch the first board
Three numbers, drivers, thresholds, owners and the move per metric. Your team starts using it in the next leadership meeting.
Add the agent on top
An AI watches the metrics on your schedule. When a line breaks, it pings the owner, drafts the diagnosis, and proposes the next move. It logs the outcome and learns which actions moved the number.
Examples of action-led dashboards
Three common ways a prescriptive board works once the agent is on top of it. The detail differs per business, the pattern doesn't.
Pipeline coverage drops below 3x.
Board flips amber on Monday. The agent pings the sales lead, lists the three stalled deals it thinks are the cause, drafts an outreach plan for each, and books a slot to review.
First-response time creeping up.
Median support response crosses the threshold mid-week. The agent identifies the queue, flags two tickets ageing fast, and proposes a rota tweak for next week.
Runway compresses ahead of plan.
Burn rate moves outside the band. The agent reconciles Xero, names the two cost lines that drove it, and drafts the email to chase the overdue invoices it thinks closed the gap last time.
Our dashboards sit inside operational systems
Digbeth Events uses connected booking, revenue and team reporting. Olton Bedrooms combines project stages, workshop time and stock alerts so managers can see which jobs need attention.
When this is worth discussing
We work best when there is a real operating problem, enough volume to measure and people from the affected teams who can make decisions.
Usually a good fit
- An established UK business, usually with annual revenue above £10m
- A repeated process with a known cost, delay, error rate or capacity problem
- A senior sponsor and a day-to-day owner who understand the work
- Access to the relevant staff, systems, sample records and security requirements
We may point you elsewhere
- A standard product already covers the process well
- The requirement is a one-off small build with no wider operating case
- There is no owner or access to the people and data needed to test the result
- The plan relies on AI making high-impact decisions with nobody responsible for review
Questions before connecting the systems
Can't we just use Power BI or Tableau?
Yes, and we sometimes use them. We first check whether the existing reports have agreed definitions, targets, owners and a clear decision attached. The interface can then use the product that best fits your licensing, data and support requirements.
Our data is a mess. Do we need to fix that first?
No. We start narrow: three numbers, the data for those, cleaned just enough. A big data clean-up before the first board is how most of these projects die. We get one decision working, then widen.
How long does it take?
Timing depends on source access and agreement over the measures. We establish one decision-led view first, then add alerts or AI recommendations after the business trusts the underlying figures.
Can the agent take action as well as propose it?
For some moves, yes. Sending the chase email, opening the ticket, kicking off the rota change, all routine and safe. For anything with a real cost, the agent drafts and a human approves. You decide what's automatic, and what goes for sign-off.
Who owns the code and the data?
Handover terms cover the source code, data model, dashboards, hosting and support. We can operate the system or work with your internal technology team.
How much does it cost?
Priced per phase, fixed before we start. First board is scoped against your source systems. The agent on top is a smaller follow-on phase. We tell you the number up front.
What if our team won't trust an agent's recommendation?
Good. They shouldn't, at first. The agent shows its working: which numbers moved, which rule fired, which past action it's copying. After a few weeks of being right about boring things, it earns its way to the bigger ones.
Talk to us about your reporting
Tell us which decisions still require somebody to collect figures or explain a variance. We will map the measure, threshold, owner and response before recommending a dashboard.