BizFalcon AI
Your data stays private
Saved work is encrypted and PII-masked, kept in your account until you delete it, and never used to train a model.
The BizFalcon engine
The engine behind ClientFalcon and ProfitFalcon.
It reads the exports you already have, computes every figure from the rows and writes up what it found. It is not sold on its own: it runs inside both products.
- Parsed in your browser
- A sample file opens with no sign-up
- Runs inside ClientFalcon and ProfitFalcon
Every figure from your rowsCSV · Excel · PDFAsk in plain English
- Computed KPIs
- Forecasts
- Anomaly flags
- Cohorts
- Reports
See it run
The engine on a sample file.
The workspace as it opens, and a recorded run on a sample file. Inside ClientFalcon and ProfitFalcon, the same engine runs on your own exports.
Analyst · the read
One file in. The full read out.
Rows read—ecom-orders.csv · 15 columns
Drop in an order export. Analyst reads every row: 24,623 orders across 15 columns in the sample file.
It checks the data before it trusts it: duplicates, blanks and outliers, column by column.
Then it writes the read: gross sales, contribution margin, the discount leak and what to do about it.
Start 14-day trial14 days free. No card. Cancel anytime.
ecom-orders.csvSample data
Reading rows0 rows
First rows
| CSC-100001 | Switchback Hiking Short — Storm / M | Organic | — | $77.47 |
| CSC-100002 | Prior-Year Trail Tee — Black / L | FALL25 | $30.06 | |
| CSC-100003 | Merino 250 Bottom — Ember / XS | Paid Search | — | $71.14 |
| CSC-100004 | Merino 150 Crew — Sand / L | Paid Social | COMEBACK | $84.21 |
15 columns found
- order_date
- order_id
- customer_id
- product_name
- sku
- product_category
- channel
- gross_amount
- cost
- shipping_cost
- discount
- discount_code
- units
- country
- order_status
Cascade Supply Co. orders over 180 days. A recorded run of the real pipeline.
Data checksSample data
- Clear
No duplicate rows
None of the 24,623 rows repeat.
- Clear
Every order has its own ID
order_id holds 24,623 unique values, none blank.
- Flagged
Discount codes are mostly blank
discount_code is empty on 61% of orders.
- Flagged
Discounts have extreme outliers
2,537 orders (10.3%) sit above the 55.24 threshold.
Run on every row, before any analysis is written.
The readSample data
24,623 orders, $5,199,611 in gross sales and an estimated $2,154,157 left after product cost, shipping and discounts.
Gross sales$0
Contribution profit$0
Contribution margin0.00%
Orders0
Units per order0.00
High
High discount outlier rate
2,537 discount outliers, 10.3% of orders, reaching 375.98 against a 55.24 threshold.
Suggested next step
Set automated approval or margin-review thresholds for discounts above 55.24, prioritizing the 10.3% of orders identified as discount outliers.
Ask a question. Get the answer and the chart.
It calculates on every row of your file, so totals are exact counts.
how much did discounts cost us?
discounted = orders[orders.discount > 0]
leak = discounted.groupby("code").discount.sum()
leak.sort_values(ascending=False)
9,713Discounted orders
−$45.0KMeasured leak / mo
24,623Rows computed
Discount leak found
Weekly revenue· $K · computed
Calculated on every row
Each answer lists the formula it used.
Charts, decks, forecasts
Ask for the chart, the slide or the projection; keep any of them.
Alerts and re-runs
Set a rule on a metric; it re-runs on the next upload and emails you when it trips.
How it works
From upload to first answer.
No setup. The file you already have is enough.
Drop a file
CSV, Excel or PDF, straight off your desktop.
Ask in plain English
Ask the way you'd ask a colleague. No formulas, no SQL.
Keep the analysis
Export the deck, or set an alert that checks your next upload.
What the engine does
Charts, forecasts and red flags from one file.
It reads messy headers, spots anomalies and builds the deck.
Charts, forecasts and decks, from the same rows
Auto-charts, forecasts and presentation-ready decks, ready to export or share.
Anomaly flags
It flags outliers, columns that never change and extreme spreads, so you know what to look at first.
Works with
The exports you already download.
Upload the file you already have. Nothing to connect.
CSV, Excel, JSON, PDF and images
Order exports, ad exports, payouts, ledgers, anything with a header row and rows under it. A sheet from Google Sheets works once you download it as CSV or Excel.
Columns it has never seen
It maps your headers itself and tells you what it mapped them to, so a file nobody cleaned still computes.
Built on the engine
Two products, one engine.
ClientFalcon
The AI account manager, for agencies. Ranks your client book each week: who is slipping, who has outgrown their fee, which ecommerce clients lose money below their margin breakeven. Drafts the message for each; you send it.
ProfitFalcon
The AI profit analyst, for online stores. Profit per SKU, the discount codes leaking margin and the fix worth the most, from your store export. Nothing changes in your store; you make every change yourself.
Questions
Can I buy the engine on its own?
No. It is what ClientFalcon and ProfitFalcon run on, and you get it inside either one, each with a 14-day trial and no card.
Do I have to connect anything?
No. Drop in a CSV, Excel or PDF export.
Is my data safe?
Your data stays private. When you use AI insights or save a report, the data needed is sent over an encrypted connection and stored in your account until you delete it. PII is detected and masked, and your data is never sold or used to train our models.
How is this different from ChatGPT with a file upload?
ChatGPT can guess from part of a file. The engine calculates on every row, so “$436,203 in discounts across 9,713 orders” is an exact count.
14-day trial on either product. No card.
See it run on your own exports.
Pick the product that fits: the account manager or the profit analyst.