AI Is Making Up Your Business Numbers. Here's How to Stop It

The $22,800 Mistake

A general contractor in Mississauga was putting together a quote for a kitchen renovation. He'd been using ChatGPT to help draft proposals -- feeding it project details, material costs, and labour estimates, then asking it to generate a professional-looking quote.

The AI returned a total of $22,400. The actual cost, based on the numbers he'd entered, was $14,100.

He didn't catch it. Sent the quote. The client said yes. Work started. Halfway through, the math didn't add up. Materials cost what they cost. Labour cost what it cost. The extra $8,300 the AI had baked in didn't correspond to anything real. It had inflated line items, added phantom charges, and rounded numbers in directions that made the total look plausible but wrong.

He ate the difference on some of it. Lost the client's trust on the rest.

This isn't a rare glitch. This is how AI works when you use it wrong.

Three Scenarios That Should Scare You

Scenario 1: Revenue hallucination. You paste your monthly sales into ChatGPT and ask for a summary. It tells you revenue was $84,000. Actual revenue was $61,200. The AI filled gaps in its understanding with plausible-sounding numbers. You use the wrong figure in a loan application.

Scenario 2: The invented customer. You ask AI to pull insights from your customer list. It references "Sarah M., a repeat client who has booked 12 sessions." Sarah doesn't exist. The AI created a composite that sounds real. You mention her in a team meeting. Everyone's confused.

Scenario 3: False growth projection. You ask AI to forecast next quarter based on current trends. It projects 22% growth. Your actual trend line shows 6%. The AI pattern-matched to optimistic business content in its training data, not your actual numbers. You hire based on the projection. Revenue doesn't materialize. Now you're overstaffed.

Every one of these has happened. Not hypothetically. To real business owners who trusted AI output without understanding what AI actually does.

Why AI Makes Things Up (In Plain English)

Here's the thing most people don't understand about tools like ChatGPT, Claude, and other large language models.

They don't know things. They predict words.

When you ask ChatGPT "What was my revenue last month?", it doesn't look up your revenue. It doesn't have access to your books. It generates the most statistically likely sequence of words that would follow your question.

If you pasted some numbers into the conversation, it'll work with those -- sort of. But it's not doing math the way a calculator does. It's predicting what the answer should look like based on patterns. Sometimes it gets it right. Sometimes it invents a number that feels right but isn't.

This is called hallucination. The AI generates confident, fluent, completely wrong information. Not because it's broken. Because that's literally what it's designed to do -- produce plausible text. Truth is a side effect, not a guarantee.

AI is a language engine, not a fact engine.

It's brilliant at writing emails, drafting content, answering general questions, and summarizing text. It's terrible at doing your bookkeeping.

The Danger of the Copy-Paste Workflow

Here's the workflow that gets people in trouble:

1. Copy data from QuickBooks or a spreadsheet
2. Paste it into ChatGPT
3. Ask a question about the data
4. Trust the answer
5. Make a business decision based on it

The problem is step 4. When you paste data into a general-purpose AI chat, you have no guarantee that:

• The AI parsed your numbers correctly
• It didn't fill in gaps with invented data
• Its calculations are mathematically accurate
• It's distinguishing between your data and its training data
• The answer will be the same if you ask again tomorrow

You wouldn't hand your accountant a stack of receipts and say "give me a number, any number, as long as it sounds about right." But that's essentially what you're doing when you use a general chat AI for financial data.

The Real Solution: AI + Database Architecture

Here's how AI should work in a business platform. And how we build it at Alpaca Launch.

Numbers come from your database. Period.

When you ask "What was my revenue last month?", the system runs an actual SQL query against your actual transaction records. It adds up real invoices with real amounts from real dates. The number is mathematically correct because a database did the math, not a language model.

AI handles the language layer.

The AI's job is to understand your question, translate it into the right query, and present the answer in plain English. It writes the emails, generates the reports, drafts the follow-ups. It does what it's good at -- language -- and leaves the math to systems designed for math.

This is called RAG: Retrieval-Augmented Generation.

Instead of asking AI to know things, you give it access to your actual records and tell it to look things up before answering. The AI retrieves real data from your database, then generates a response based on that data.

The difference is night and day:

• Without RAG: "Based on typical patterns, your revenue was approximately $84,000." (Wrong.)
• With RAG: "Your revenue for January 2026 was $61,243.87, based on 47 paid invoices." (Correct, verifiable, source-linked.)

Every output is traceable.

When our AI assistant tells you a number, it shows you where that number came from. Which invoices. Which date range. Which client records. You can click through and verify. No black box. No trust-me answers.

When to Use AI vs. When to Use Your Database

This is the framework every business owner needs:

Use AI For:

• Writing: Emails, proposals, social media posts, blog content
• Summarizing: "Give me a plain-English summary of this month's activity"
• Answering questions: "What's our cancellation policy?" (from your own docs)
• Drafting: Contracts, follow-ups, responses to reviews
• Brainstorming: Marketing ideas, service descriptions, ad copy

Use Your Database For:

• Revenue and expenses: Any dollar amount, ever
• Customer counts: How many clients, bookings, purchases
• Trends and projections: Based on actual historical data
• Inventory: What you have, what you've sold, what you need
• Scheduling: Who's booked when, availability, capacity

Use AI + Database Together For:

• Smart reports: Database pulls the numbers, AI writes the narrative
• Client communication: Database identifies who to contact, AI drafts the message
• Business insights: Database surfaces the pattern, AI explains what it means
• Automated follow-ups: Database triggers the event, AI personalizes the content

The magic happens when these two layers work together with clear boundaries. AI never invents a number. The database never writes a sentence. Each does what it's built for.

What This Looks Like in Practice

You open your Alpaca Launch dashboard on a Monday morning. You ask the AI assistant: "How did last week go?"

Here's what happens behind the scenes:

1. The AI understands your question (language processing)
2. It queries your database for last week's bookings, revenue, new clients, and cancellations (data retrieval)
3. The database returns exact numbers (math)
4. The AI formats the response in plain English (language generation)

What you see: "Last week you had 23 bookings (up from 18 the week before), $4,720 in revenue, 6 new clients, and 2 cancellations. Your busiest day was Thursday with 7 bookings. Want me to send a thank-you email to the new clients?"

Every number links back to the actual records. Click "$4,720" and you see the 23 invoices that add up to that total. Click "6 new clients" and you see their names and contact info.

No hallucination. No guessing. No plausible-sounding fiction.

The Stakes Are Higher Than You Think

When AI gets a trivia question wrong, nobody cares. When AI gets your business numbers wrong, the consequences are real:

• Tax implications: Wrong revenue figures in CRA filings
• Loan applications: Inflated or deflated numbers affect credit decisions
• Client trust: Sending a quote with wrong numbers kills credibility
• Business decisions: Hiring, inventory, and expansion based on fiction
• Legal exposure: Financial misrepresentation, even if accidental

In Canada, under PIPEDA and provincial privacy laws, you're also responsible for how customer data is handled. Pasting client information into a third-party AI chat tool raises data governance questions you don't want to answer in front of a regulator.

Stop Guessing. Start Knowing.

AI is the most powerful business tool to emerge in a decade. But only if you use it right. And "right" means keeping it away from your calculator and giving it access to your actual data through proper architecture.

At Alpaca Launch, every AI feature is built on this principle. Language from AI. Numbers from your database. Every answer traceable. Every figure verifiable.

Your business deserves AI that tells the truth.

See how we build AI that knows your business -- book a walkthrough and we'll show you the difference between guessing and knowing.