We run a small restaurant group and we are looking at where to open our next location. Which counties should we look at, and can we afford to borrow for the build-out at current rates?

• Read a 504,108-row Census workbook, county income for every US county, the Fed survey, and current rates - then shortlisted counties and built the bank-ready workbook.
• Every figure in the workbook traces to the public row it came from.

We want to borrow to open a second location. Decide whether we can afford the monthly payments and build the affordability model with a clear verdict.

• No revenue or cost figures were on file, so it built the model from the rates it had - then STOPPED, held the verdict, and emailed one question asking for the real numbers instead of guessing them.
• With the numbers supplied, the verdict flipped from a placeholder AFFORD to a defended DO NOT AFFORD: the stated payroll meant the business was losing money before any loan.

The point of this one: it asks rather than invents.

Build me a PowerPoint for a meeting with my landlord. Slide 1: who we are... Slide 2: our sales, with a chart... Slide 3: what we are asking - a 3 year renewal at current rent with annual increases capped at 2 percent. Slide 4: why this is good for the landlord too. Exactly 4 slides, navy blue theme, keep the words short, I will do the talking.

• Four and a half minutes later: exactly those 4 slides, in that order, in navy - rendered to PDF and checked visually against the instructions.
• Then one follow-up sentence - 'the cap should say 2.5 percent, not 2, change nothing else' - and it edited that one number in the finished deck. Dictate the structure and it follows it; change your mind and it edits, not rebuilds.

The business is fictional; the branding came from the profile.

Here is our March operating account export. Tell me where the money went, flag anything unusual I should look at, and give me a clean one-page summary I can keep.

• It caught a duplicate $1,450 vendor payment (same day, same amount) and said what to do: call the vendor, the recovery is real.
• It refused to pretend: the export ends March 28, so every figure says 28 days, not a month.

The input is synthetic demo data: upload it to your own account and compare what comes back.

What is happening in the data. I have a small business so tell me anything I need

• One casual sentence, four public datasets in, a nine-section memo out: the ten numbers that matter, the landscape, the cost pressure - and what it could NOT know without the business's own records.
• This run ended by asking its client what they actually needed: the memo covers the environment; analysing THE business needs the business's own numbers.

I own two auto repair shops in Washington state, one in Tacoma and one in Spokane. Everyone keeps telling me EVs are going to kill the repair business. Here is the state's full EV registration data and current loan rates. How worried should I be, and on what timeline? Which of my two locations is more exposed? I have about 150k saved to reposition one shop - should I borrow more at current rates to do it sooner?

• 289,565 real EV registrations analysed county by county; the verdict opens the answer: a 3 to 5 year window, with the 2029-2031 warranty-expiry wave named as the inflection point.
• All five questions answered: Tacoma more exposed, Spokane the better home for defensive capital, deploy the 150k in cash, borrow only if rate and contracted revenue both hold.

The data is real and public; the shops are fictional.

Here are our customer balances from the end of Q1 and the end of Q2. What changed between them? Who is new, who left, where did balances move the most, and does anything look off?

• Caught every planted movement: wholesale as the +$4.63M driver, the Midwest as the only shrinking region, and the churn counted exactly - 3,000 departed, 4,000 new.
• Then it did something better than agreeing with its own numbers: it flagged that round churn counts like those can also mean the two extracts used different population filters, and said what to confirm.

I have monthly branch reports for the first half of 2026, half are PDFs and half are CSV exports. Put everything into one Excel workbook with a tab per month and a combined view, and tell me how the year is trending.

• Twelve files - six CSVs of 12,000 rows each, six PDF summaries - into one workbook: a tab per month, a combined view, the trend stated.
• And the honest catch: the PDFs' summary figures do not reconcile to the CSV detail, so instead of quietly picking one, the workbook says which number is which and what would settle it.

These are our customer survey responses for the year, all five stores. Categorize them into themes, rate the sentiment by store, and pull out the 20 responses I should personally read.

• 70,000 free-text responses categorized and scored by store - including the store that was quietly planted angrier than the rest.
• The checking pass earned its keep here: it caught a column error and 12 responses scored with the wrong sentiment in the first workbook, and the workbook was rebuilt before delivery.

This customer export is a mess - duplicates, weird dates, inconsistent names, amounts stored every which way. Clean it into a proper dataset I can trust, and tell me exactly what you fixed and what you could not.

• Exactly 3,000 duplicate rows were planted; exactly 3,000 exact duplicates were found and removed. All 100,000 dates normalised from four formats, names from three styles, amounts from four notations.
• Nothing silently changed: every fix is itemised in the cleaning report, and every row it could NOT settle is in its own issues file rather than quietly guessed.

Here is a year of our bank transactions. Categorize them into a sensible chart of accounts and build me a monthly profit and loss I can share with my accountant.

• 80,000 transactions categorised - payroll, rent, COGS, utilities, marketing, revenue - into a monthly P&L with live formulas.
• And it would not bless the totals: the scale of the numbers did not look like a small business, so the P&L says plainly to confirm the file is the real operating account before sending it anywhere. It was right - the data is synthetic.

Here is our full payments file for the year. Audit it - duplicates, anything suspicious, anything I should worry about before our review. Give me the evidence for each flag.

• 150,105 payments swept. It found the planted fraud: the exact duplicate pairs (with the cash impact computed), and one vendor with 60 payments totalling $583,050 - every one sized just under the $10,000 approval threshold.
• Then it did the thing that makes it worth trusting: four independent forensic signals - Benford rejection, uniform day-of-week, dates stopping at 28 - and it concluded the FILE ITSELF looked generated, and said so: if this is a test fixture, do not present these as real findings. It was right. It found the fraud, then proved the crime scene was staged.
• Every flag ships with its evidence rows in its own file.