ProfitFalcon for a brand’s own P&L, ClientFalcon for a whole client book, BizFalcon Analyst free underneath both. Every number opens to the code that produced it.
Sample data, no install, no call — or start a free trial.
ProfitFalcon is for the brand; ClientFalcon is for the agency running someone else's. Two buyers, two products, priced on their own. Both run on BizFalcon Analyst, which writes Python, runs it in your browser, and shows the math behind every answer, and which you can also use directly, free.
Agency operations for account managers. A prospect's ad export becomes the audit that wins the pitch, and the day they sign, that audit becomes their live baseline. Reports, portfolio health, and a ledger you can show the client.
Unit economics for ecommerce brands. Contribution margin, LTV and per-SKU profit computed straight from your Shopify, WooCommerce or Amazon export, with the runnable SQL beside every number.
Your numbers are computed in the browser from the file you drop. Nothing leaves the tab until you save a report or run AI, and the trial does not ask for a card.
See ProfitFalconOpen ProfitFalcon Desktop app
The AI data analyst that shows its math. Drop in a spreadsheet, ask in plain English, and watch it write and execute real pandas. KPIs, forecasts, anomaly flags and decks, each with an audit trail. It is the engine ProfitFalcon and ClientFalcon are built on, and it is free to use on its own.
Real Python runs on your rows. Click any figure and the exact expression that produced it opens, with the row count behind it.
Every recommendation carries a projected dollar impact, waits for your approval, then gets scored against what your next export actually shows.
How scoring worksEach one watches a different way money leaks and queues the exact change. You approve every move.
See the agentsShopify, WooCommerce, BigCommerce, Amazon, Meta, Google Ads, GA4, or any CSV. No pixel, no app install, no connector to wait on.
Most AI analytics tools ask a language model to guess at your numbers and ask you to trust the dashboard. Ours makes the model write code, runs the code against your rows, and shows you both.
Drop a CSV or a platform export. It loads into a Python runtime running in your browser tab, not on a GPU somewhere. A 24,623-row sample store parses in seconds.
The analyst writes real pandas, executes it against your actual rows, and keeps the code. The model drafts; the interpreter decides. Nothing is estimated from a summary.
KPIs, forecasts and anomaly flags, each one linked to the exact computation behind it. Click any figure and the line that produced it opens underneath.
Three screens, one engine. Each is the product a different buyer opens first, and each figure on it was computed by Python that ran on the rows.
Revenue minus COGS, fees, shipping and ad cost for every product, ranked so the quiet losers surface first. On the sample store, two of 28 SKUs sit at or below a 20% margin floor.
Drop an ad export, get the KPIs, the written why, a prioritised test plan and a white-label PDF carrying your agency’s brand. Reusable per client, every month, at no extra cost per report.
Agents watch for the ways money leaks, price the fix, and queue it. Nothing executes on its own — you approve one at a time, and the ledger records what the change was worth.
The engine that computes your numbers also drafts your next move. Deterministic playbooks — not a free-running agent — watch the data and queue recommendations. You approve or dismiss. Execution stays off until the hit rate earns it, and every recommendation is logged against what actually happened.
Each recommendation arrives with the rows it was computed from and the figure it claims. “Rework Meta for Bloom Botanicals, 0.82× ROAS, $13.4K returning $11.0K” is a sentence you can check before you act on it.
Nothing touches your store, your prices or a client’s ad account without a click from you. Dismissing is a first-class answer, and the playbook learns which of its moves you never take.
Every approved move is recorded with what it predicted, then measured against what happened over the following 7 and 30 days. A tool that cannot be wrong in public is a tool you cannot trust.
Flat monthly plans, priced per product. We do not bill you more the better you do. Annual is ten months for twelve.
Drop an export and every figure recomputes on it. Every one opens to the code behind it.
BizFalcon AI
