Quick Start
A short go-live checklist for getting from installer download to first successful categorization run.
Open Quick StartDocumentation
Full public documentation for launch, onboarding, and the first working tutorial.
Use this as the main documentation entry point. Start with the tutorial if you are new, or jump directly to the section you need.
Windows available now. macOS coming soon.
Browse Documentation
These links cover the core paths users need before and after launch.
Tutorial
A first-time walkthrough covering license setup, dashboard access, managed AI credits, and your first processing run.
Open TutorialBuyer Questions
The dedicated drill-down page for pre-purchase workflow, safety, setup, output, and macOS timing questions.
Open Buyer QuestionsCustomer Portal
Manage licenses, check wallet balance, top up credits, and recover account access from the dashboard.
Open DashboardFeedback & Support
Send product feedback, report friction, and give the team the context they need to help you quickly.
Send FeedbackTutorial
First-time walkthrough
Follow this sequence if you are setting up UnboxedSpend for the first time and want the least confusing path.
Step 1
Choose your onboarding path
Choose either the Regular workflow or the full-featured Beta workflow, then keep the steps in sequence so each screen has one clear next action.
Step 2
Install and open UnboxedSpend
Download the Windows installer, complete the install, then launch the desktop app so you can add your license and confirm the app opens cleanly. macOS coming soon.
Step 3
Activate your account
Start with account access, verify your email from the setup link if the account is new, then continue when the website confirms `Your plan is ready`.
Step 4
Pick your AI mode
Use Managed AI if you want the simplest setup with tracked credits, or choose your own API key path if you prefer provider-managed billing. No AI training is required from you because UnboxedSpend uses AI to generate categorization rules.
Step 5
Process a first real file
Upload a sample CSV or start an Amazon reconciliation run, review the output, and make sure the resulting categories and clearing-account behavior match your expectations.
Step 6
Match your tier to the desktop tab you will use
Basic centers on Import Data, Instant Reconciler adds Amazon Tools for order-level clearing, and Line-Item Allocator unlocks Itemize for item-level clearing once you have Amazon order-history exports ready.
Step 7
Use support paths when needed
If you hit a login, licensing, or credit issue, go to the dashboard for account recovery or use the feedback page to send the team the details.
Clearing Walkthrough
Same Amazon order, two clearing modes
Compare order-level clearing and item-level clearing side by side using one shared order scenario so the difference is obvious before you choose a workflow.
Debit and credit here are bookkeeping directions, not `good/bad` transactions.
Base categorized aggregates stay reproducible and immutable; clearing writes accounting-adjusted outcomes into derived `_cleared` CSV artifacts.
Order-Level Clearing
Instant Reconciler
Use this when you want Amazon clearing quickly at the order level without waiting for item-level export files.
Requires categorized baseline plus Amazon Transaction History matching for the order.
What this is doing
At order level, the full order amount moves as one unit. Instead of splitting the order into products, the workflow posts one grouped clearing entry for the matched order.
Paired action: write `Transfer to the Amazon Clearing Account` to derived `all_categorized_normalized_cleared.CSV`, then append the exact matching amount (`49.98`) to `Amazon_Clearing_Account.CSV`.
How each transaction is processed
- Detect the order transaction: Match the Amazon order ID to the corresponding transaction-history row so one order amount is locked.
- Transform into one grouped clearing row: Keep the amount as a single order-level amount and assign one category outcome for the grouped order entry.
- Post debit/credit and write outputs: Create balancing posts, write `Transfer to the Amazon Clearing Account` to derived `all_categorized_normalized_cleared.CSV`, and append the same amount to `Amazon_Clearing_Account.CSV`.
1) Start with these CSVs
- ~/UnboxedSpend/output/Categorized/all_categorized_normalized.CSV (immutable categorized baseline)
- ~/UnboxedSpend/output/Categorized/all_categorized_normalized_cleared.CSV (derived order-level cleared output)
- Amazon Transaction History export CSV (order feed input)
2) Actions on each CSV (with examples)
| CSV | Action | Example |
|---|---|---|
| ~/UnboxedSpend/output/Categorized/all_categorized_normalized_cleared.CSV | Write matched Amazon-funded row category as `Transfer to the Amazon Clearing Account`. | Order 112-9843210-4455667 transfer relabel is written in the derived cleared file. |
| Amazon Transaction History export CSV | Match order IDs and lock grouped order amount used for clearing. | Order 112-9843210-4455667 contributes grouped amount 49.98. |
| ~/UnboxedSpend/output/ClearingAccount/Amazon_Clearing_Account.CSV | Append one grouped clearing row with the exact matched amount. | One grouped row posts 49.98 for the matched order. |
3) Atomic paired actions
Matched order ID 112-9843210-4455667
- Write transfer label to categorized baseline row.
- Append the same amount to Amazon_Clearing_Account.CSV.
Example: 49.98 category rewrite and 49.98 clearing append happen together.
4) CSVs created or updated
- Created/updated: ~/UnboxedSpend/output/ClearingAccount/Amazon_Clearing_Account.CSV
- Created/updated: ~/UnboxedSpend/output/Categorized/all_categorized_normalized_cleared.CSV
- Unchanged source: ~/UnboxedSpend/output/Categorized/all_categorized_normalized.CSV
Flow diagram
Amazon Transaction History CSV + all_categorized_normalized.CSV
-> match order ID 112-9843210-4455667
-> atomic pair
-> write transfer label in all_categorized_normalized_cleared.CSV
-> append 49.98 to Amazon_Clearing_Account.CSV
-> outputs: all_categorized_normalized_cleared.CSV + Amazon_Clearing_Account.CSV
Mini input (same Amazon order scenario)
| Date | Description | Amount | Source |
|---|---|---|---|
| 2026-03-05 | AMAZON MKTPLACE PMTS 112-9843210-4455667 | $49.98 | Transaction History |
Mini output (grouped clearing row)
| ai_date | ai_description | ai_charge | ai_credit | ai_category | Output |
|---|---|---|---|---|---|
| 2026-03-05 | Amazon Reconciler Order 112-9843210-4455667 | 49.98 | 0.00 | Transfer to the Amazon Clearing Account | 1 grouped order row |
Journal entries (debit / credit)
Journal entries below explain the posting logic; they are explanatory and not literal CSV rows exported by the app.
| Step | Transaction | Account | Debit | Credit | Why |
|---|---|---|---|---|---|
| 1 | Order 112-9843210-4455667 | Shopping Expense | 49.98 | 0.00 | Recognize spending for the order as one grouped expense. |
| 2 | Order 112-9843210-4455667 | Amazon Clearing Account | 0.00 | 49.98 | Offset the expense and keep the clearing account balanced. |
Journal totals by account
| Account | Debit | Credit | Note |
|---|---|---|---|
| Shopping Expense | 49.98 | 0.00 | - |
| Amazon Clearing Account | 0.00 | 49.98 | - |
| Total | 49.98 | 49.98 | Total debits equal total credits. |
Total debits equal total credits.
Expected output files
~/UnboxedSpend/output/ClearingAccount/Amazon_Clearing_Account.CSV
~/UnboxedSpend/output/Categorized/all_categorized_normalized_cleared.CSV
Item-Level Clearing
Line-Item Allocator
Use this when you need line-item detail by product and refund allocation for the same order.
Requires Amazon order-history personal data export (plus any refund export you plan to allocate), using categorized Amazon itemized transactions as input (not credit-card transaction rows).
What this is doing
At item level, the order is split into product-level entries, and refunds are posted back against the affected item category instead of only adjusting one grouped order line.
Paired action: for each item/refund movement, write `Transfer to the Amazon Clearing Account` to derived `all_categorized_itemized_cleared.CSV` and append the same net movement into `Itemized_Amazon_Clearing_Account.CSV`.
How each transaction is processed
- Split the order into item transactions: Break one order into individual item rows so each product has its own amount and category target.
- Apply item and refund logic: Post item purchases to their matching expense accounts and post refunds back to the related item account.
- Post debit/credit per item and export: Record balancing posts for each item/refund movement, write `Transfer to the Amazon Clearing Account` to derived `all_categorized_itemized_cleared.CSV`, and append the matching movement to `Itemized_Amazon_Clearing_Account.CSV`.
1) Start with these CSVs
- ~/UnboxedSpend/output/Categorized/products/categorized_Retail.OrderHistory.1.CSV (categorized Amazon item rows)
- ~/UnboxedSpend/output/Categorized/all_categorized_itemized.CSV (immutable itemized aggregate handoff)
- ~/UnboxedSpend/output/Categorized/all_categorized_itemized_cleared.CSV (derived item-level cleared analysis output)
- ~/UnboxedSpend/output/ClearingAccount/Amazon_Clearing_Account.CSV (order-level clearing input)
- Amazon Refund export CSV (when refund rows exist)
2) Actions on each CSV (with examples)
| CSV | Action | Example |
|---|---|---|
| ~/UnboxedSpend/output/Categorized/products/categorized_Retail.OrderHistory.1.CSV | Produce categorized item/refund rows used for item-level clearing movements. | Coffee Beans 29.99 and Water Filter refund -5.00 are prepared as item-level rows. |
| ~/UnboxedSpend/output/Categorized/all_categorized_itemized_cleared.CSV | Write transfer rows for clearing movements into derived itemized cleared output. | Each item/refund movement is represented as a transfer-labeled row in the cleared analysis file. |
| Amazon Refund export CSV | Provide refund rows that are mapped to item-level categories and signs. | Water Filter 3-pack refund -5.00 is mapped to Household Refund/Returns. |
| ~/UnboxedSpend/output/ClearingAccount/Itemized_Amazon_Clearing_Account.CSV | Append per-item and refund clearing movements with matching amounts. | 29.99 item, 19.99 item, and -5.00 refund movements are appended. |
3) Atomic paired actions
Each item or refund movement
- Write transfer-labeled movement in all_categorized_itemized_cleared.CSV.
- Append the same signed amount to Itemized_Amazon_Clearing_Account.CSV.
Example: Refund movement -5.00 updates itemized transfer row and clearing row together.
4) CSVs created or updated
- Created/updated: ~/UnboxedSpend/output/ClearingAccount/Itemized_Amazon_Clearing_Account.CSV
- Created/updated: ~/UnboxedSpend/output/Categorized/all_categorized_itemized_cleared.CSV
- Unchanged source: ~/UnboxedSpend/output/Categorized/all_categorized_itemized.CSV
- Created/updated: ~/UnboxedSpend/output/Categorized/products/categorized_Retail.OrderHistory.1.CSV
Flow diagram
categorized_Retail.OrderHistory.1.CSV + all_categorized_itemized.CSV + Amazon_Clearing_Account.CSV + Amazon Refund export CSV
-> split order 112-9843210-4455667 into item/refund movements
-> atomic pair per movement
-> write transfer row in all_categorized_itemized_cleared.CSV
-> append same signed amount to Itemized_Amazon_Clearing_Account.CSV
-> outputs: all_categorized_itemized_cleared.CSV + Itemized_Amazon_Clearing_Account.CSV
Mini input (same Amazon order scenario)
| Order ID | Item | Item Total | Source |
|---|---|---|---|
| 112-9843210-4455667 | Coffee Beans 2 lb | $29.99 | Retail.OrderHistory.1.csv |
| 112-9843210-4455667 | Water Filter 3-pack | $19.99 | Retail.OrderHistory.1.csv |
| 112-9843210-4455667 | Water Filter 3-pack Refund | -$5.00 | Retail.OrderHistory.1.csv |
Mini output (split itemized rows)
| ai_date | ai_description | ai_charge | ai_credit | ai_category | Output |
|---|---|---|---|---|---|
| 2026-03-05 | Coffee Beans 2 lb | 29.99 | 0.00 | Transfer to the Amazon Clearing Account | Item row 1 |
| 2026-03-05 | Water Filter 3-pack | 19.99 | 0.00 | Transfer to the Amazon Clearing Account | Item row 2 |
| 2026-03-06 | Water Filter 3-pack Refund | 0.00 | -5.00 | Transfer to the Amazon Clearing Account | Refund row |
Journal entries (debit / credit)
Journal entries below explain the posting logic; they are explanatory and not literal CSV rows exported by the app.
| Step | Transaction | Account | Debit | Credit | Why |
|---|---|---|---|---|---|
| 1 | Coffee Beans 2 lb | Groceries Expense | 29.99 | 0.00 | Record the item-level purchase in Groceries. |
| 2 | Coffee Beans 2 lb | Amazon Clearing Account | 0.00 | 29.99 | Offset Groceries purchase in the clearing account. |
| 3 | Water Filter 3-pack | Household Expense | 19.99 | 0.00 | Record item-level purchase in Household. |
| 4 | Water Filter 3-pack | Amazon Clearing Account | 0.00 | 19.99 | Offset Household purchase in the clearing account. |
| 5 | Water Filter 3-pack Refund | Amazon Clearing Account | 5.00 | 0.00 | Bring refund money back through clearing. |
| 6 | Water Filter 3-pack Refund | Household Expense (Refund/Returns) | 0.00 | 5.00 | Reverse part of Household expense due to refund. |
Journal totals by account
| Account | Debit | Credit | Note |
|---|---|---|---|
| Groceries Expense | 29.99 | 0.00 | - |
| Household Expense (net) | 19.99 | 5.00 | - |
| Amazon Clearing Account | 5.00 | 49.98 | - |
| Total | 54.98 | 54.98 | Total debits equal total credits. |
Total debits equal total credits.
Expected output files
~/UnboxedSpend/output/ClearingAccount/Itemized_Amazon_Clearing_Account.CSV
~/UnboxedSpend/output/Categorized/all_categorized_itemized_cleared.CSV
Before You Use Itemize
Itemize depends on Amazon order-history personal data.
The Itemize workflow requires an Amazon order-history personal data export. Amazon often takes a couple of days to provide that export after you request it. That wait comes from Amazon's export process, not from the app. As soon as you get that export from Amazon, you can finish the full item-level workflow.
Start Here
The fastest way to understand the workflow before you read deeper.
Import CSVs in
Today UnboxedSpend starts from the bank or card CSV files you already download yourself.
Keep Amazon login local
If Amazon asks you to log in, you sign in directly there in your own local browser window.
Export local CSVs out
The finished categorized and clearing outputs stay on your machine for the budgeting workflow you already use.
Mac timing
macOS coming soon. Targeting macOS availability within 2 weeks.
Tier Comparison
Compare the three actionable desktop paths, not just the license names.
Each tier becomes clearer when you look at the tab it unlocks. Use this section when you want a fuller side-by-side view of which workflow matches the job you need to finish.
Import Data
Basic
Start here if your immediate goal is to import card CSVs and get categorized output fast.
Basic is the foundation workflow. You import credit-card CSVs, review the staged files, run categorization, and produce the normalized categorized output that later Amazon-focused workflows build on. The final aggregate keeps the app's real normalized columns so downstream reconcile steps can use a stable schema. `ai_category` is the prized outcome because it captures the final category after AI and rule-based refinement work together, while `ai_category_by_credit_card_company` records provenance context such as the original card-company category or an `Amazon Reconciler` marker.
Expected Output Target
~/UnboxedSpend/output/Categorized/all_categorized_normalized.CSV
Actual final columns
ai_date
ai_transaction_date
ai_description
ai_charge
ai_credit
ai_category
ai_category_by_credit_card_company
SourceFile
Import and stage credit-card CSVs
Run categorization and aggregate output
Surface `ai_category` as the final category outcome customers care about most
Create the baseline data used by downstream tiers
Amazon Tools
Instant Reconciler
Choose this when you want order-level Amazon clearing without waiting for item-level export files.
Instant Reconciler builds directly on the categorized baseline from Import Data. It adds Amazon transaction-history reconciliation, preflight checks, and order-level clearing so you can match Amazon activity more cleanly before going deeper into item-level detail.
Expected Output Target
Amazon clearing-account CSV generated from the categorized baseline plus transaction-history matching
Typical result
Order-level clearing output
Matched Amazon activity against categorized inputs
Ready for clearing-account review
Includes everything in Basic
Adds Amazon transaction-history reconciliation
Runs order-level clearing with preflight checks
Itemize
Line-Item Allocator
Use this when you need the deepest Amazon detail, including item-level purchase and refund allocation.
Line-Item Allocator is the full workflow. After Import Data and Amazon Tools, it adds the Itemize tab so you can import Amazon purchase history and refund files, recategorize at item level, and build itemized clearing-account outputs.
Expected Output Target
~/UnboxedSpend/output/ClearingAccount/Itemized_Amazon_Clearing_Account.CSV
Intermediate and final outputs
~/UnboxedSpend/output/Categorized/products/categorized_Retail.OrderHistory.1.CSV
~/UnboxedSpend/output/Categorized/all_categorized_itemized.CSV
~/UnboxedSpend/output/Categorized/all_categorized_itemized_cleared.CSV
~/UnboxedSpend/output/ClearingAccount/Itemized_Amazon_Clearing_Account.CSV
Includes Basic and Instant Reconciler
Adds Amazon purchase-history and refund imports
Produces item-level clearing outputs for the deepest workflow
Normalized Output
What the final `all_categorized_normalized.CSV` columns actually mean.
The `Basic` workflow finishes by writing a normalized aggregate CSV with a stable schema. These are the real final columns customers should expect to see in that file.
ai_date
The normalized main posting date in YYYY-MM-DD form.
ai_transaction_date
A separate normalized transaction date when the source file provides one.
ai_description
The cleaned transaction description used for categorization and review.
ai_charge
Money spent, normalized as a positive amount.
ai_credit
Money returned or credited back, normalized as a negative amount.
Primary Outcome
ai_category
The target categorization outcome classified by AI.
This is the major time saver in the normalized output because it solves the core pain point: getting transactions into the target category faster and consistent without manual decision and editing.
ai_category_by_credit_card_company
Category provenance context from the source system (for example the card-company category), or `Amazon Reconciler` when reconciliation updates the source marker.
SourceFile
The original categorized input filename, kept so later reconciliation can trace each row back to its source.
Frequently asked questions
Do I connect UnboxedSpend to my bank?
Not today. UnboxedSpend currently starts from manual CSV imports that you download from your bank or credit-card account yourself. Direct bank integration may be added later if demand is strong enough.
What exactly do I import each month?
Start with the bank or credit-card CSV files you already download. If you are using the Amazon workflows, you then add the Amazon-side history or order-history files that match the tier you are using.
What exactly can I test with the Free License?
The Free License is meant for evaluation. You can test up to 5 CSVs per run, up to 3 Amazon Transaction History pages, and up to 1 Gift Card Activity page before you move into the paid unlimited tiers.
Do I need a dashboard account before I download the app?
No. You can download first, but dashboard access becomes useful as soon as you want to manage licenses, set your password, or top up managed AI credits.
Which desktop operating systems are available right now?
The public website currently supports the Windows desktop installer first. macOS coming soon. Targeting macOS availability within 2 weeks.
Do I give UnboxedSpend my Amazon username and password?
No. When you use Amazon extraction, UnboxedSpend opens a local browser window on your machine. If Amazon shows a login page, you sign in directly there with Amazon, and the automation continues after that local session is ready.
Is Amazon extraction safe to use?
The normal flow keeps the login inside your own local browser session instead of an UnboxedSpend credential form. If Amazon changes the page structure and normal extraction breaks, UnboxedSpend first falls back to AI parsing. If that also fails, we develop an update fix and charge a small update fee for that repair.
What files do I actually get out at the end?
UnboxedSpend writes local CSV outputs on your machine, including the categorized baseline file, the Amazon clearing-account file, and the itemized clearing-account file when you use the deeper Itemize workflow.
How much AI usage usually costs in practice?
The public site now frames this as example household budgeting ranges in both credits and approximate dollars. Those examples are meant to help you plan, not to act as fixed quotes, because actual spend varies with provider, model, file size, and how many categorization requests you run.
When should I use Managed AI versus my own API key?
Use Managed AI for the fastest setup and tracked credits. Use your own API key if you want billing to stay directly with your provider.
How much setup or AI training should I expect?
No AI training is required from you. UnboxedSpend uses AI to generate categorization rules, so your job is to import files and review the results instead of teaching a model from scratch.
What is the difference between Instant Reconciler and Line-Item Allocator?
Instant Reconciler is for order-level Amazon clearing built from your categorized baseline. Line-Item Allocator goes deeper to the item level once you have Amazon's order-history personal-data export ready.
Where do I go if I lose track of setup emails?
Open the password setup page or dashboard recovery path. If you still need help, use the feedback page and include the email tied to your license.
Why is Itemize sometimes not available right away?
Itemize depends on Amazon order-history personal data exports. Most users need to request that file from Amazon first, and it commonly arrives a couple of days later.
Need the next action?
These are the most common places people need right after reading the docs.