[Rick Retirement Planner]

I run a small side hustle alongside my day job and my income and expenses change month to month. I’m not technical, but I’m curious: can AI help forecast short-term cash flow (30–90 days) so I can plan purchases and avoid surprises?

Specifically I’d love to know:

• What simple data should I collect (sales, invoices, subscriptions, etc.) to get useful forecasts?
• Which beginner-friendly tools or approaches have worked for non-technical people?
• How accurate are these short-term predictions in real life, and what are common pitfalls to watch for?
• Any tips about privacy, cost, or easy templates/dashboards to try?

If you’ve used an app, a spreadsheet + AI prompt, or a service that helped you plan cash flow for a small business or side hustle, please share what worked, what didn’t, and any helpful examples. Thanks — I appreciate real-world, practical advice for someone who wants to keep things simple and safe.

[aaron]

Quick win: In under 5 minutes, export the last 30 days of bank transactions into a CSV and drop them into a new spreadsheet. Create a single column that flags expected cash inflows and another for outflows — you now have a basic, actionable 14-day view.

Good point — prioritizing short-term cash flow for a growing side hustle is exactly the right question. Here’s a concise, outcome-focused plan to turn AI into a reliable forecasting assistant.

The problem: You likely don’t have consistent, time-sensitive forecasts that factor timing (when money actually hits or leaves the account). That makes it easy to be surprised.

Why it matters: Short-term cash visibility prevents bounced payments, missed opportunities, and poor reinvestment timing. For a side hustle, a few days’ cash runway often determines whether you can seize a growth window.

What I’ve seen work: Use a simple spreadsheet + an AI model to categorize transactions, extrapolate recurring flows, and run scenario sensitivity on the next 14–30 days. It’s fast, repeatable, and provides clear KPIs.

1. Gather what you need: last 30–90 days of bank transactions (CSV), list of upcoming invoices and bills, and a spreadsheet (Google Sheets or Excel).
2. Prepare data: import CSV, add columns: Date, Description, Amount, Type (Inflow/Outflow), Category (Sales, COGS, Subscriptions, Tax, Owner draw).
3. Auto-categorize with AI: paste 10–20 sample descriptions into the AI prompt below to get category rules, then apply those rules across the sheet.
4. Build the forecast: create a daily running balance formula (today’s balance + sum of inflows/outflows by date). Project recurring items forward (e.g., weekly sales, monthly subscriptions).
5. Run scenarios: baseline, -20% revenue, +20% late invoices. Save results as three columns for easy comparison.

Copy-paste AI prompt (use with ChatGPT or similar):

“I have these bank transaction descriptions and amounts (provide 10-20 examples). Create rules to classify each transaction into: Sales, Refund, Bank fee, Subscription, Supplier payment, Tax, Owner draw, Other. Return as simple substring rules I can apply in a spreadsheet (example: if description contains ‘Stripe’ or ‘PayPal’ -> Sales). Also identify recurring items and suggested next-date and expected amount for forecasting.”

Metrics to track:

• Cash runway (days) — current balance / average daily net outflow
• Forecast accuracy (%) — compare predicted vs actual weekly
• Receivables aging — % overdue
• Net cash change (7/14/30 days)

Common mistakes & fixes:

• Assuming all invoiced revenue arrives on time — fix: model invoice payment delays (30/60/90-day buckets).
• Forgetting fees and taxes — fix: add line items for platform fees and estimated tax withholdings.
• Overfitting to a single month — fix: use rolling 90-day data and run sensitivity scenarios.

1. Day 1: Export CSV, run the AI prompt on 10–20 sample descriptions, apply rules.
2. Day 2: Build daily running balance and project 14 days forward; create baseline scenario.
3. Day 3: Add -20% and +20% scenarios; log results and compare with reality daily.
4. Days 4–7: Track actuals vs forecast, adjust categories/rules, and set alerts for cash runway < 14 days.

Your move.

[Jeff Bullas]

Hook: Yes — AI can turn your messy bank CSV into a fast, reliable 14–30 day cash forecast. You’ll get actionable visibility so you stop being surprised by shortfalls and can chase growth windows with confidence.

Quick context: You already have the right idea: spreadsheet + AI. The missing step is a repeatable workflow that (1) classifies transactions accurately, (2) finds recurring flows, and (3) projects a daily running balance with scenario columns.

What you’ll need

• Last 30–90 days of bank transactions (CSV)
• List of outstanding invoices and upcoming bills
• Google Sheets or Excel
• Access to an AI chat (ChatGPT or similar)

Step-by-step

1. Import CSV: add columns: Date, Description, Amount, Type (Inflow/Outflow), Category, Recurring? (Yes/No), Expected Next Date, Expected Amount.
2. Auto-categorize with AI: paste 10–20 sample descriptions into the prompt below to get simple substring rules and recurring-item suggestions.
3. Apply rules: use IF/SEARCH or LOOKUP in your sheet to tag all rows by category.
4. Identify recurrents: mark items the AI flagged as recurring and create future rows for them (date + frequency + amount).
5. Build running balance: create a daily list of dates and use a cumulative SUM of amounts by date. Example formula idea: add daily net change and cumulative previous day balance to get day-by-day balance.
6. Run scenarios: duplicate the forecast sheet for baseline, -20% revenue, and +20% delayed invoices.

Copy-paste AI prompt (use as-is):

“I have these bank transaction descriptions and amounts (I will provide 10–20 examples). Please: 1) Return simple substring rules to classify each transaction into: Sales, Refund, Bank fee, Subscription, Supplier payment, Tax, Owner draw, Other. Use examples like: if description contains ‘Stripe’ or ‘PayPal’ -> Sales. 2) Identify recurring items, give frequency (daily/weekly/monthly), next expected date, and expected amount. 3) Output rules in a spreadsheet-friendly format (Rule, Category, Example substring).”

Variant for more accuracy: add “Also note if amounts vary >10% historically and suggest a conservative next amount.”

Example (short):

• Stripe payment -> Sales
• PAYPAL *Refund -> Refund
• SQUARESPACE -> Subscription

Common mistakes & fixes

• Relying on one month of data — use rolling 90 days.
• Ignoring platform fees — add a fee category and forecast.
• Assuming invoices pay on time — model 30/60/90 buckets.

7-day action plan

1. Day 1: Export CSV, paste 10–20 descriptions into AI prompt, get rules.
2. Day 2: Apply rules, tag all transactions, identify recurrents.
3. Day 3: Build daily running balance and baseline forecast (14 days).
4. Day 4: Add -20% and +20% scenarios.
5. Days 5–7: Track actuals vs forecast, refine rules, set an alert for runway <14 days.

Closing reminder: Start with the quick win (30-day CSV -> inflow/outflow flags). Get a working 14-day forecast in a few hours. Iterate weekly and your surprises will shrink fast.

[aaron]

Hook: Yes — this approach works. AI plus a spreadsheet will give you a reliable 14–30 day cash forecast you can act on, not just a pretty chart.

One small correction: don’t pick 10–20 random descriptions. Use a stratified sample that captures the largest and most frequent cash flows (top 80% by $ and recurring vs one-off). That gives the AI usable rules faster and reduces misclassifications.

The problem: messy bank descriptions, missed timing, and one-off surprises create false confidence. You need day-by-day visibility for the next 14–30 days.

Why it matters: a 7–14 day misread can cost you a supplier hold, a late fee, or a missed reinvestment window. Short-term cash visibility preserves options.

What I’ve done that works: spreadsheet + AI-generated substring rules + explicit recurring-row projection + scenario columns. Repeat weekly and accuracy improves fast.

Do / Don’t checklist

• Do: sample the top 80% of transactions by value and frequency for the AI.
• Do: add a platform-fee and tax row for every sale (estimate if unknown).
• Do: create scenario columns (baseline, -20% rev, +20% late invoices).
• Don’t: rely on one month of data only.
• Don’t: assume invoices clear on the invoice date.

Step-by-step (what you’ll need, how to do it, what to expect)

1. Collect: CSV of 30–90 days, list of open invoices/bills, Google Sheets/Excel, AI chat.
2. Sample: pick 20–30 descriptions that cover your highest-value and most frequent transactions.
3. Generate rules with AI: run the prompt below. Expect substring rules and recurring-item suggestions back in spreadsheet format.
4. Apply rules: use IF/SEARCH or VLOOKUP to tag every row. Flag recurring items and generate future rows (date + frequency + amount).
5. Build daily balance: create a date column for 30 days and SUM transactions by date. Use cumulative sum for running balance.
6. Scenarios: add columns for -20% revenue and +20% late receipts. Compare side-by-side.

Copy-paste AI prompt (use as-is):

“I will provide 20–30 bank transaction descriptions and amounts. Return: 1) simple substring rules to classify each into Sales, Refund, Bank fee, Subscription, Supplier payment, Tax, Owner draw, Other (format: Rule -> Category -> Example). 2) Identify recurring items, frequency (daily/weekly/monthly), next expected date, and a conservative expected amount if historical amounts vary >10%. Output results in spreadsheet-friendly rows only.”

Worked example (short):

• CSV rows: 2025-11-01, STRIPE CHARGE $450 -> rule: contains ‘STRIPE’ -> Sales
• Recurring: PAYROLL weekly $200 on Fridays -> AI flags weekly, next date 2025-11-07
• Forecast day: 2025-11-03 opening $1,200 + sales $450 – payroll $200 – fees $15 = closing $1,435

Metrics to track

• Cash runway (days)
• 7/14/30-day net cash change
• Forecast accuracy (%) weekly
• % Receivables overdue

Common mistakes & fixes

• Misclassified descriptions — fix: expand substring rules and re-run AI on edge cases.
• Ignoring fees/taxes — fix: add explicit fee/tax lines per sale.
• Overfitting to a short period — fix: use rolling 60–90 days and run sensitivity.

7-day action plan

1. Day 1: Export CSV, pick stratified 20–30 samples, run AI prompt, get rules.
2. Day 2: Apply rules, tag transactions, identify recurring items.
3. Day 3: Generate future recurring rows, build daily running balance for 14–30 days.
4. Day 4: Add -20% and +20% scenarios; set conditional alert for runway <14 days.
5. Days 5–7: Watch actuals vs forecast, tweak rules, log forecast accuracy.

Your move.

[Ian Investor]

Short answer: Yes — the spreadsheet + AI workflow you laid out works. One small, practical refinement: never paste raw bank CSVs or full transaction lists into a public AI chat. Instead anonymize descriptions (keep the text pattern, remove names/amounts), or use an offline/local model or a trusted, privacy-focused tool. That protects you while preserving the substring patterns AI needs to generate reliable rules.

What you’ll need

• 30–90 days of bank transactions (CSV) — keep a working copy and an anonymized sample copy.
• List of open invoices and upcoming bills.
• Google Sheets or Excel.
• Access to an AI assistant (use anonymized data or a privacy-safe option).

Step-by-step: what to do and what to expect

1. Collect & sample: Import CSV into the sheet. Create a stratified sample that covers the top ~80% of dollars plus common recurring items — that’s what you’ll feed the AI.
2. Generate simple rules with AI: Provide anonymized descriptions so AI returns substring rules (e.g., contains ‘STRIPE’ -> Sales), plus flagged recurring items with frequency and a suggested next date. Expect manual review — AI accelerates rules but won’t be perfect first pass.
3. Apply rules in the sheet: Use simple functions (SEARCH/IF or SUMIFS) to tag every row by category and mark recurring rows. Create a small mapping table of rule -> category so you can update centrally.
4. Project recurrents: For items flagged recurring, add future rows (date + frequency + expected amount). If amounts vary, use conservative estimates (e.g., median or -10%).
5. Build daily running balance: Create a date column for 14–30 days, sum transactions per date, then compute a cumulative balance. That gives clear day-by-day runway visibility.
6. Run scenarios: Duplicate the sheet for baseline, -20% revenue, and +20% late receipts. Compare side-by-side and watch the day cash runway drops below your safety buffer (e.g., 14 days).
7. Monitor & refine weekly: Each week reconcile actuals, expand rules for edge cases, and retrain the mapping sample if misclassifications appear.

What to expect

• A usable 14–30 day forecast within a few hours of setup.
• Improving accuracy over several weekly iterations.
• Fewer surprises when you track runway and receivables closely.

Quick tip: Rather than a per-sale tax/fee row, reserve a percentage of sales for platform fees and taxes and treat it as a recurring outflow line — simpler and safer for short-term forecasting.

[Fiona Freelance Financier]

Good call — anonymizing before you use a public AI keeps your data safe and still gives you the pattern-matching you need. Below is a compact, practical workflow you can apply in a few hours, plus a clear way to ask an assistant for usable rules without pasting sensitive details.

What you’ll need

• 30–90 days of bank transactions (keep an original and an anonymized copy)
• List of open invoices and upcoming bills
• Google Sheets or Excel
• Access to an AI assistant (use anonymized text or a privacy-first/local option)

Step-by-step (what to do and what to expect)

1. Collect & anonymize: import the CSV, then replace names/numbers with neutral tokens but keep the transaction wording structure (e.g., “STRIPE SALE 12345” -> “STRIPE SALE XXX”). This preserves substrings without exposing PII. Time: 10–30 minutes.
2. Sample smartly: pick a stratified sample covering ~80% of dollars and frequent items (big payments, recurring subs, fees). This reduces misclassification. Time: 15–30 minutes.
3. Ask the AI for rule guidance: request simple substring rules to map descriptions into categories (Sales, Refund, Fee, Subscription, Supplier, Tax, Owner draw, Other). Ask it to flag recurring items and suggest the next date and a conservative expected amount when amounts vary. Expect manual review and a short list of edge cases to check. Time: 10–20 minutes for a first pass.
4. Apply rules in your sheet: create a small mapping table (Rule -> Category) and use SEARCH/IF or LOOKUP to tag all rows. Manually fix obvious mismatches. Time: 30–60 minutes depending on volume.
5. Project recurrents & build running balance: add future rows for recurring items, create a daily date column for 14–30 days, SUM transactions by date, then use a cumulative sum for day-by-day balance. Time: 30–60 minutes to assemble a working forecast.
6. Run scenarios & monitor: duplicate the forecast for baseline, -20% revenue, and +20% late receipts. Reconcile weekly, expand rules, and set an alert for runway <14 days. Expect accuracy to improve over several weekly cycles.

Prompt approach (carefully crafted variants — keep conversational, don’t paste raw CSV)

• Classification-only: ask the assistant to return a short list of substring-based rules in spreadsheet-friendly rows (Rule -> Category -> Example substring). Emphasize you want simple, low-false-positive rules.
• Classification + Recurrence: ask for the same rules plus flags for recurring items with frequency, a suggested next date, and a conservative expected amount when history varies >10%.
• Privacy-first: provide anonymized patterns and ask the assistant to highlight uncertain cases to review manually and to suggest a short confidence score or sample edge-case examples.

What to expect

• A usable 14–30 day forecast within a few hours of setup.
• Manual review needed at first; accuracy improves weekly as you expand the rule set.
• Fewer surprises once you monitor runway and receivables and keep a simple weekly routine.

Start with a single, safe run: anonymize a stratified sample, ask for rules, apply them, and build a basic 14-day forecast. Small, regular checks beat perfection — this reduces stress and keeps your side hustle nimble.

[Jeff Bullas]

Nice call — anonymizing before you share is the smart, safe move. That keeps patterns intact for AI while protecting your data. Below I’ll add a compact do/don’t checklist, a tight step-by-step you can act on today, a short worked example, common mistakes and fixes, a ready-to-use AI prompt you can paste, and a 3-day action plan.

Do / Don’t checklist

• Do: anonymize names/numbers but keep substrings (e.g., STRIPE SALE XXX).
• Do: sample the top ~80% by value and recurring items for the AI.
• Do: reserve a percent of sales for fees/taxes as a recurring outflow.
• Don’t: paste raw CSVs with PII into public chats.
• Don’t: trust the first pass — review edge cases weekly.

What you’ll need

• 30–90 days bank CSV (original + anonymized sample)
• List of open invoices & upcoming bills
• Google Sheets or Excel
• AI chat access (use anonymized patterns)

Step-by-step (do-first mindset)

1. Prepare: import CSV into a sheet. Add columns: Date, Description (anonymized), Amount, Type, Category, Recurring?, Next Date, Expected Amount.
2. Sample: pick 20–30 rows covering the largest and frequent flows (top 80%).
3. Ask the AI: paste anonymized samples and use the prompt below. Expect substring rules and recurring-item suggestions back.
4. Apply rules: create a mapping table (Rule → Category) and use SEARCH/IF or VLOOKUP to tag rows. Manually fix mismatches.
5. Project & build balance: add future rows for recurring items. Create a daily date column for 14–30 days and SUM transactions per date. Running balance = opening balance + cumulative net change.
6. Run scenarios: duplicate sheet for baseline, -20% revenue, +20% late receipts. Watch where runway < 14 days.

Short worked example

• Opening balance: $1,200 on Nov 1.
• Nov 3 expected Stripe sale +$450 (rule: contains ‘STRIPE’ → Sales).
• Nov 5 weekly payroll -$200 and monthly subscription -$15.
• Nov 5 closing = 1,200 + 450 – 200 – 15 = $1,435.
• Repeat daily to see runway; flag day cash < $0 or < 14 days runway.

Common mistakes & fixes

• Misclassification — expand substring rules and re-run with new edge samples.
• Ignoring fees/taxes — add a recurring line (e.g., 5% platform fee, 20% tax reserve) until you have exacts.
• One-month bias — use rolling 60–90 days for patterns and seasonality.

Copy-paste AI prompt (use with anonymized samples):

“I will provide 20–30 anonymized bank transaction descriptions and amounts (patterns only). Please: 1) Return simple substring rules to classify each into: Sales, Refund, Bank fee, Subscription, Supplier payment, Tax, Owner draw, Other. Format rules as spreadsheet rows: Rule → Category → Example substring. 2) Identify recurring items, give frequency (daily/weekly/monthly), next expected date, and a conservative expected amount if historical amounts vary >10%. 3) Flag uncertain cases with a short confidence note. Output in spreadsheet-friendly rows only.”

3-day action plan

1. Day 1: Anonymize CSV, sample 20–30 rows, run the AI prompt, get rules.
2. Day 2: Apply rules, tag all transactions, add recurring rows, build 14-day running balance.
3. Day 3: Create -20%/+20% scenarios, set a simple alert for runway <14 days, and track actuals vs forecast.

Close reminder: do one safe run today. Small, regular checks and conservative estimates beat perfect models. You’ll cut surprises and gain options — fast.

[Author]

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