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Get your data
business-ready
in 10 minutes.

Drop a messy spreadsheet. Get it cleaned, checked and explained. The work of a data analyst, delivered in minutes.

Data ingestion Data cleaning Data validation KPI calculation Profitability analysis Risk detection Visualisation Recommendations Export

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Sales 2026
sales_2026.xlsx
0% · a full run takes a few minutes on large workbooks.
# reading workbook
sheets = read_workbook("sales_2026.xlsx")
# ✓ 4 sheets · 360,718 rows
# detecting column types
types = audit_columns(sheets)
# ✓ 38 of 41 columns typed
# cleaning: orders
orders = orders.drop_duplicates(subset="order_id")
# ✓ 11 issues found · 8 auto-repaired
# mapping relationships
link(customers.id → orders.customer_id)
# ✓ customers → orders → payments
# computing KPIs
kpis = compute(revenue, margin, delivery)
# ✓ 18 of 24 KPIs computed
Results
What the data says about the business.
Revenue
£0.00M
▲ +32.2% vs last period
Orders
0
▲ +24.1%
Gross margin
0.0%
▼ −3.1pts
Avg delivery
0.0 days
▼ -13.7%
Top products
Gross profit per product
Showing 8 of 11,564 products · computed from the cleaned dataset
Risks · ranked by impact
!Gross margin slipped from 41.7% to 38.6%.Review against the 38.6% baseline.
!Your top 10% of customers carry 38.3% of revenue.Nearly 4× an even distribution.
Recommendations
  • Review cost drivers behind the recent slip.
  • Reduce reliance on your top accounts. The next tier is where growth is cheapest.
Cleaned & certified · 10 minutes
Export →
Drop your file
sales_2026.xlsx24.6 MB
sales_2026_cleaned.xlsx✓ certified
Data stored in the EU· Encrypted at rest· Never used to train AI· ICO registered ZC217327
The solution

The full data pipeline, automated

Extract

Raw files, read and mapped automatically

The engine accepts large Excel and CSV spreadsheets in their original state. Every sheet is scanned, column types are detected, and tables are mapped together through shared keys: customers to orders, orders to payments. The result is one connected dataset, ready for processing.

sales_2026.xlsx · 4 sheets detected
↓
customers
orders
payments
Transform

Cleaning, validation, reconciliation

Duplicates are removed, formats standardised, dates normalised, currency symbols detected, invalid entries flagged. Cross-table checks reconcile the data: totals, references, missing links. Every change is recorded: what was changed, and where.

# cleaning: orders
orders = orders.drop_duplicates(subset="order_id")
orders["date"] = to_datetime(orders["date"])
orders["amount"] = clean_amount(orders["amount"])
validate(orders)
# ✓ 360,718 rows · 11 issues found · 8 repaired
Load

KPIs, margins, risks · computed and delivered

From the verified data, the engine computes the numbers that matter: revenue, margins, top products, risks, anomalies, then reads them together. Revenue up and margin down is a different story from both going up, and the engine says so. The output is a finished analysis: clear figures, ranked findings, concrete recommendations, delivered on screen and exportable in one click.

Revenue£2.54M
Margin38.6%
Risks6
Growing, but keeping less of it: revenue up 32.2%, margin down 3.1 points.
#1 Investigate margin decline in order_items
#2 Add delivery-SLA alert for slowest carriers
#3 Diversify the top-10% accounts
Why CleanMyData

Not another AI tool

A rules-based engine that does the work of a data analyst.

One input,
one answer

The engine runs on explicit rules, written, tested and versioned like any serious software. Nothing is generated, nothing is estimated: every number in the output is computed, and can be recomputed identically. Run it a hundred times, the answer stays the same.

The analyst's playbook,
executed by software

An analyst opens the file, cleans it, reconciles the tables, computes the KPIs, writes the report. The engine executes those same five steps, in that same order, as software. The method stays; the timeline collapses: from days of manual work to minutes of runtime.

Full audit trail,
built in

Every modification made to the data is recorded: what was changed, and where. The complete log is visible on the Cleaning Review screen: each duplicate removed, each format fixed, each flagged entry, table by table. The result can be trusted because the process can be checked.

Industries

Validated on real data, industry by industry

Validated means the engine has processed real datasets from that industry, end to end. In validation means the work is under way.

SaaS

Customers, subscriptions, recurring billing, computed month by month.

✓ Validated on real data

Hospitality

Bookings, stays, rates, cancellations, guest records · made readable.

✓ Validated on real data

Logistics

Loads, routes, trips, drivers, fuel, maintenance · nine data types.

✓ Validated on real data

E-commerce

Orders, order items, payments, customers, products, sellers, reconciled end to end.

In validation

Retail

Sales orders and product–supplier catalogues: pricing, quantities, supplier terms.

In validation

Finance

Invoices, expenses, cash flow.

In validation

Manufacturing

Production, materials, output.

In validation

Inventory

Stock levels, movements, valuation.

In validation

Coming soon.

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Contact

Talk to us

A question about the engine, the pilot, or your data? Write to us. A real person answers, usually within a day.

support@cleanmydata.co
London, United Kingdom