A dashboard that tells you what to look at first and from which source.
KPIs that update via Supabase realtime, live sparklines and receivables aging kept current. Claude reads the dashboard, detects the anomaly and tells you which data to highlight and why, citing the table it comes from; the decision stays yours.
<1s
latency
from trigger to widget
0
F5
data arrives on its own
1
source of truth
behind every number
8
industries
same engine running
UNDER THE HOOD
It tells you which metric matters today and why it flagged it.
A dashboard with 40 widgets is noise. Claude reads the full dashboard every time a data point changes, decides which metric is out of pattern, and explains why it highlighted it: threshold crossed, trend broken, or statistical anomaly.
Sample signal
The signal comes in
Projected 30-day cash flow dropped to $48K. Configured operating minimum is $60K. AP due this week: $71K. Confirmed collections today: $12K.
Hard threshold crossed
projected cash $48K < minimum $60K → immediate red flag
Outflow pressure
AP $71K due in 5 days → the gap lands this week
Insufficient inflow
confirmed collections $12K don't close the $23K gap
Actionable move
there are 3 invoices +90 days overdue for $19K that cover almost all of it
Score
18/100
Verdict from Claude
Alert to Finance. Highlight cash widget + open +90-day AR aging. Suggest prioritized collections.
The signal comes in
Average ticket at the Downtown location had been stable at $84 for the last 6 weeks. Today it's been at $61 for 4 hours. Ticket volume is normal. No catalog changes recorded.
Pattern break
ticket $61 vs moving average $84 → 27% sustained drop over 4h, not an isolated spike
Volume rules out seasonality
# of tickets normal → the same customers spend less per ticket
Bounded hypothesis
no catalog change → likely a bad promo load or default combo in POS
Cross-location comparison
other 2 locations hold $83-$86 → it's local to Downtown, not market-wide
Score
44/100
Verdict from Claude
Notify Downtown location manager. Highlight avg ticket + cross-location comparison. Check POS config.
The signal comes in
Returns in the last hour: 14 units of SKU-2207. Normal band for that hour: 0-3. The supplier for that SKU changed last week. Product margin: 31%.
Statistical outlier
14 returns vs 0-3 band → 4.6σ above expected: a real signal
Concentrated in 1 SKU
100% of spike is SKU-2207 → the spike comes from a single product
Temporal correlation
supplier change 7 days ago → probable root cause: new batch quality
Quantified impact
at this rate, ~$2.1K/day in returns + 31% margin erosion
Score
9/100
Verdict from Claude
Critical alert to Operations + Purchasing. Highlight returns widget + drill-down to SKU-2207. Suggest lot hold.
WHAT AI DOES ON YOUR DASHBOARD
Five jobs your analyst shouldn't be doing manually anymore
The dashboard reads the business for you, decides what to look at and tells you when a data point changes.
realtime.stream(event)
Every change reaches the widget without reloading
Supabase realtime subscriptions on Postgres triggers. A sale, a payment, an order: the event travels from the core to the dashboard in under a second. Zero polling, zero F5.
› input
Postgres trigger: INSERT into sales · location=Downtown · amount=$240
› claude →
event received · t+0.4s
KPI sales_today: $18,420 → $18,660
sparkline: +1 point, recalculates moving average
Downtown location widget repaints on its own
→ no reload, no open tab needed
realtime.aggregate(role)
Each role sees their dashboard, not everyone's
Same data stream, different aggregation depending on who's looking. The location manager sees their location; the CFO sees consolidated and aging; the rep sees their quota. Role-configurable widgets.
› input
User: location_manager · location=North · role=operations
› claude →
dashboard filtered to location=North (Postgres RLS)
widgets: location_sales, occupancy, returns
hidden: consolidated, payroll, global margins
KPIs recalculated only within their scope
→ one stream, N role-based dashboards
claude.alert(dashboard)
Decides when it's worth interrupting you
Claude reads the dashboard every time a data point changes and decides: is this noise or a signal? It only alerts you when a threshold is crossed, a trend breaks, or there's a real anomaly. With the reason attached.
› input
Dashboard: projected cash $48K · configured minimum $60K
› claude →
⚠︎ threshold crossed: cash < operating minimum
severity: high · window: this week
reason: AP $71K due in 5 days
suggestion: manage +90-day AR ($19K)
→ alert sent · cash widget highlighted
realtime.drilldown(kpi)
From total to detail, without leaving the dashboard
An aggregated KPI is useless if you can't ask it "why?" Click on the number and drill into the detail that makes it up — customer, SKU, location, invoice — in real time, without separate reports.
› input
Drill-down: total AR $214K → what's it made of?
› claude →
0-30 days: $128K (60%) · healthy
31-60: $44K · 12 customers
61-90: $23K · 5 customers · watch
+90: $19K · 3 customers · manage NOW
→ click on +90 → invoice list
claude.forecast(series)
Where the period will close
On the live series, Claude projects the period close and contrasts it against budget and Order Entry. It tells you whether you'll make it — and by how much — before the month runs out.
› input
Series: month-to-date sales $312K · day 18 of 30
› claude →
projected run-rate: $498K at close
month budget: $540K
projected gap: -$42K (-7.8%)
backup Order Entry: $61K in pipeline
→ achievable if 2 of 4 key deals close
LIVE SYSTEM · Grupo Latitud
This is what it looks like inside an AI-operated system — and how it is governed.
A dashboard is useful when it is fed by the same information that moves the operation and respects who may see what.
Modules in this tour
- 01The dashboard reads the operation live
- 02A summary that cites its sources
- 03The same question, two answers
- 04One dashboard per role, one single datum
- 05Who sees what, written down
Boosty Standard for Operating with AISimulated AI · demo data
THE SAME ENGINE
One live dashboard. The metrics change, the judgment doesn't.
We don't build a different BI solution per industry. The same realtime + judgment engine reads the specific metrics of each business and decides what to highlight. This is already running in production.
Signals specific to the industry
Signals specific to the industry
Signals specific to the industry
Signals specific to the industry
Signals specific to the industry
Signals specific to the industry
LIVE BOARD
The numbers change while you watch them
No reload, no tab left open. Each KPI listens to its Postgres trigger via Supabase realtime and repaints on its own — with its sparkline recalculating on the fly.
Sales today
$19,382
▲ 0.09%
Projected cash
$65,028
▲ 0.09%
Average ticket
$88.60
▲ 0.09%
Occupancy
77.3%
▲ 0.09%
IN THE SYSTEM · CONTROL TOWER
The same data as operations, on one screen.
Indicators by country with their change, prioritized exceptions, pending approvals and what the AI is doing now. The tower has no numbers of its own: it reads them from the modules.
Real screenshot of the system · demonstration data
EVENT STREAM
Everything that happens in your core, arriving live
Postgres triggers emit every change to a Supabase channel. This is the raw feed: sales, collections, inventory, returns — the heartbeat of the business, with no reload.
0
events today
0/min
throughput
ANOMALY DETECTION
It sees the outlier before you do
Returns per hour for SKU-2207. The normal band is 0-5. Claude watches the series live and, when something falls 4σ outside the pattern, it highlights it and fires the alert — with the root cause next to it.
Critical alert · returns SKU-2207
14 returns this hour vs normal band 0-5. 100% of the spike is SKU-2207. Supplier change 7 days ago → probable root cause: quality of the new batch. Estimated impact: ~$2.1K/day + margin erosion 31%.
IN THE SYSTEM · DAILY BRIEFING
A summary for each person, with the tables it read.
The management briefing and the sales briefing do not say the same thing, because they do not see the same thing. Each block cites its source: if a figure is questioned, you know where it came from.
Real screenshot of the system · demonstration data
CONNECTED STACK
Lives on top of your data. Doesn't migrate it.
The live dashboard connects to where your information already lives. If your core has a database or REST API, we listen to it in real time.
Supabase
Realtime subscriptions + Postgres triggers: the heart of the live stream and low latency
Claude · Anthropic
Claude PartnerThe judgment: reads the dashboard, decides what to highlight, detects anomalies, and forecasts
Make / n8n
Ingest events from any legacy core or ERP without a modern API into the stream
Kommo CRM
Live pipeline and forecast KPIs on top of the commercial data you already have
Monday.com
Project progress and deliveries feeding executive widgets in real time
WhatsApp Business
When Claude detects a critical anomaly, the alert goes out through the channel where it finds you
Frequently asked questions about real-time dashboards
No. It's a visualization and judgment layer that connects to the data you already generate. If your core runs on Postgres/Supabase we listen natively via realtime; if it has a REST API, same; if it's legacy with no API, we ingest events via Make/n8n. We don't migrate your database — we observe it live.
Sub-second in the typical case. We use Supabase realtime subscriptions on Postgres triggers: when data changes in your core, the event travels to the widget in under a second. The data arrives on its own, with no page reload and no five-minute polling.
A dashboard with 40 widgets is noise: nobody looks at all 40. Claude reads the full dashboard every time something changes and decides which of those metrics is out of pattern — threshold crossed, broken trend, or statistical anomaly — and highlights it with the reason why. The difference between having data and having data that speaks to you.
Yes. The same data stream is aggregated differently by role using Postgres RLS: the location manager sees their location, the CFO sees consolidated and aging, the rep sees their quota. Widgets are role-configurable — nobody sees what doesn't belong to them and nobody drowns in someone else's data.
Yes, without leaving the dashboard or generating a separate report. Click on total AR and you drill into aging buckets; click on +90 days and you see the list of invoices and customers. The drill-down is also real-time: if a payment comes in while you're looking, the number adjusts on its own.
You define the operational thresholds (minimum cash, SLA, normal returns band). On top of that, Claude distinguishes noise from signal: an isolated spike won't interrupt you; a sustained broken trend or a 4σ outlier with a probable root cause will. Every alert comes with its reasoning, not as a bare number.
Yes, it's one of the most common patterns. Location/company filtering via RLS, consolidated or per-location KPIs depending on the role, and multi-currency support with dual rates (official + parallel) when the business requires it. Receivables aging and cash flow respect the currency and scope of who's looking.
It is defined in the assessment. The scope — what gets built first and what waits — comes from what we see in your operation, not from a catalog. Book 30 minutes and we give you the range in writing.
A WORD FROM THE FOUNDER
“A dashboard earns its place when the important data shows up in time to decide.”
An aircraft’s instrument panel is useful because it warns you while the course can still be corrected. A monthly report is closer to the black box: it explains what already happened. A company can have its full BI stack and still find out about the cash problem on Friday afternoon, when the supplier is already calling.
Real-time dashboards connect the board to the single source of truth and update it live. The AI reads it continuously, spots what breaks pattern and flags it with its reason: what changed, where to look and which datum shows it. The system prepares the alert; what to do with it — pay, hold a purchase, call a customer — is decided by a person.
For leadership, that means seeing the deviation while there is still room to act. Book 30 minutes with me: I’ll show you a live dashboard catching an anomaly, with the reasoning next to it. Which figure would you want to see before it turns urgent?
Gabriel Montiel
CEO · Boosty Digital
LET'S TALK
Ready for a dashboard that alerts you first?
Schedule a 30-minute assessment. We bring a live dashboard and show you Claude detecting an anomaly in real time on data like yours. No corporate deck.