Automated data analysis

The analysis a data team would run, without hiring one.

Connect your database or upload a file. You get a written report and an interactive dashboard — churn, LTV, CAC, forecasting and risk scoring, already interpreted.

Sales by categoryrun_0629
64
59
50
Electronics
138.4k
Peripherals
126.2k
Furniture
114.7k
Utilities
75.9k
Storage
71.4k
Other (34)
84.6k

What it replaces

3roles: analyst, scientist, consultant
<5 minfrom data to report
SQL · NoSQL · CSVconnect anything

Two things land on your desk

Not a pile of charts to interpret yourself — a written report you can forward to the board, and a dashboard to dig into when you want the detail.

The report

A document with the findings written out: what changed, why it likely changed, and what to do about it. Every figure is checked against the source data before it's published.

Section 1

Executive summary

$568,300
22.4%
60

Revenue is concentrated: the top six customers account for 44% of the period. Three high-severity anomalies were flagged, all on profit, all in the last week of April — pointing to volume rather than pricing.

The dashboard

Every metric the analysis produced, grouped by theme. Risk shows up in colour, the long tail is grouped so the chart stays readable, and the underlying tables come with a written summary.

Risk score by category
Storage
352
Peripherals
338
Furniture
331
Electronics
320
Utilities
289
Office
266

What gets analysed

Each run goes from basic aggregation through to causal inference. You pick the depth; the pipeline does the rest.

Trends and forecasting

Multi-method forecasts with seasonality, confidence intervals and anomaly detection on every metric.

Risk and opportunity

A weighted score across four dimensions, per category and per customer, so you know where to look first.

Churn, LTV and CAC

Retention by cohort, lifetime value by plan, acquisition cost by channel, and the LTV/CAC ratio for each one.

Customer segmentation

Groups built from behaviour rather than guesswork, each with its own value, risk and engagement profile.

Causal inference

Which metric actually moves which, with lag correlation and mutual information — not just what happened together.

Scenario simulation

Conservative, moderate and aggressive projections so you can see the range before committing to a decision.

From data to report in four steps

Set it up once. Run it when you need it, or leave it on a weekly schedule.

01

Connect your data

Point it at a SQL or NoSQL database, or upload a CSV or Excel file.

02

Set the goal

Say what you want to find out. Relevant metrics are suggested from there.

03

It runs

The pipeline processes in the background and checks its own numbers.

04

Read the results

Report and dashboard, ready. Schedule it to arrive every Monday if you want.

See it run on your own data

A 30-minute call. Bring a dataset and we'll analyse it live.