ERP
FreshBooks
Connect your AI to FreshBooks
Small business owners, bookkeepers and accountants, and freelancers and project managers run invoicing, expenses, bills, and time tracking through FreshBooks, but a client's full picture sits across invoices, payments, credits, and time entries that each take a separate lookup. AI can assemble that picture in seconds, but only if it queries just the FreshBooks company the user is authorized for and treats financial records as read-only.
Why it matters
Capabilities that matter most for FreshBooks
01
Ready-made FreshBooks tools
Pre-built Get tools cover invoices, clients, payments, credits, estimates, expenses, bills, vendors, items, taxes, projects, and time entries, so nobody builds them from scratch.
02
Prompts for finance jobs
Worked prompts chase overdue invoices, reconcile payments, find unbilled work, and review bills due, each naming the tools it calls and what it reports.
03
Scoped to your company
Queries run against the FreshBooks company the user is authorized for, so an AI client never pulls another company's records.
03
Read-only by default
Financial records are treated as read-only. The tools that create, update, and delete records stay switched off until someone reviews and activates them.
05
Cross-system answers
Pair FreshBooks with stores, email marketing, and spend tools such as Shopify, Mailchimp, and Ramp to reconcile clients, orders, and bills in one question.
Use cases
Example AI Use Cases for FreshBooks
scenario
-
Item catalogues and tax rates drift over time. Stock runs low without anyone noticing, items lose their tax setting, names are duplicated and a tax rate gets entered oddly, and spotting any of it means scrolling through two separate lists. Problems usually surface only when an invoice goes out wrong.
What popdock provides
--
Optionally name the FreshBooks company and a low-stock level, and it reads the item catalogue and the tax list. It checks for items at or below the stock level, items with no unit cost or SKU, duplicate item names, and items pointing at a tax that does not exist. It also flags taxes with duplicate names or numbers and unusual rates. The findings are grouped by check with item or tax ids, followed by a short summary of what to fix first. Nothing is edited in FreshBooks.
Business outcome
---
Catalogue and tax problems are found and ranked in one pass, so you know what to fix first without scanning each list.
scenario
-
When a customer asks what they ordered, what they were billed and what they still owe, an agent has to look in the store, then in the books, and line the two up by hand. Names and ids differ between systems, so it is easy to quote the wrong balance or miss a paid order with an unpaid invoice.
What popdock provides
--
Give a customer email and it finds that person in BigCommerce and in FreshBooks, then builds one view. You see their BigCommerce orders with dates, status and totals, next to their FreshBooks invoices, payments and the outstanding balance. If the email matches no one or several customers, it stops and lists them instead of guessing. If the two systems hold different names for the same email, it shows both and does not choose. Nothing is changed in BigCommerce or FreshBooks.
Business outcome
---
Support agents answer order and billing questions from one view, with mismatches called out and no records changed.
scenario
-
Answering a billing question means looking the customer up in Chargebee, then again in FreshBooks, and piecing the story together. Subscriptions, cards, invoices and payments sit in two places, and a cancelled plan with open invoices is easy to overlook.
What popdock provides
--
Give a customer email, and optionally a FreshBooks company and a look-back period, and it finds the customer in Chargebee and the client in FreshBooks. You get one profile with identity, Chargebee subscriptions, cards and unpaid invoices, and FreshBooks invoices and recent payments, finishing with a short list of mismatches or risks. If several customers match, it stops and lists them, and it never matches on name alone. Only the last four digits of a card are shown, currencies are compared only when they match, and nothing is changed.
Business outcome
---
Support and billing staff answer a customer question from one profile, with card details protected and nothing changed.
scenario
-
Payments taken in Chargebee are re-keyed into FreshBooks by hand, and some are never recorded. The matching invoice then looks unpaid in the books, and the gap is only found when someone chases a customer who already paid. That wastes goodwill and time.
What popdock provides
--
Give a look-back period and optionally a FreshBooks company, and it reads successful Chargebee payments from that period. It finds each customer's FreshBooks client by email, then looks for a FreshBooks payment with the same amount and currency within 3 days, or one that names the Chargebee transaction. You get the unmatched Chargebee payments with email, amount, date and transaction id, the clients not found in FreshBooks, and a total of unmatched money per currency. Each match is used only once, and nothing is changed in either system.
Business outcome
---
Finance finds Chargebee payments missing from FreshBooks before a customer is chased for money already paid.
scenario
-
Accounts payable staff compare the open bills report in FreshBooks against Ramp bill pay, one vendor at a time. A bill that was already paid in Ramp can still show as owing in the books, and it may get paid twice or sit on the aging report for weeks.
What popdock provides
--
Set a minimum balance and a due-by date, or leave them as defaults, and it lists open FreshBooks bills with their vendors. It looks each vendor's bills up in Ramp, matching on invoice number and falling back to amount. For bills Ramp shows as approved or paid, it checks the FreshBooks payment records. You get a table with an action hint for each bill: not in Ramp, pending in Ramp, paid in Ramp but unpaid in FreshBooks, or unpaid everywhere, with the paid in Ramp group first. Unclear matches are marked ambiguous, not guessed, and nothing is changed in either system.
Business outcome
---
You catch bills already paid in Ramp that still show owing in FreshBooks before they are paid twice, without changing any record.
scenario
-
Before a campaign goes out, nobody checks which recipients owe money or have unsubscribed. The invoices sit in FreshBooks and the mailing list sits in Constant Contact, so a client with an overdue balance can still get a promotional email, or someone who opted out can be mailed by mistake. Nobody wants to chase a payment and promote to the same client in one week.
What popdock provides
--
Name the FreshBooks company and, if you like, a minimum outstanding amount, and it finds the clients with overdue invoices and totals what each one owes. It then looks up each client by email in Constant Contact and shows their permission to send and their most recent campaign activity. Clients who have unsubscribed are flagged as not to be marketed to, and clients with no email or no Constant Contact record are reported as such. Nothing is changed in FreshBooks or Constant Contact.
Business outcome
---
You see who owes money and who may not be mailed before the campaign goes out, so no overdue or unsubscribed client gets a promotion.
scenario
-
Marketing plans a Mailchimp promotion without seeing FreshBooks balances, so a client who is seriously overdue can receive an upbeat offer. Finding those clients means pulling overdue invoices, looking up each client's email and then searching Mailchimp for each address.
What popdock provides
--
Give it a number of overdue days, or leave it at 30, and it finds FreshBooks clients with invoices overdue by more than that, totalling what each owes by currency. It gets each client's email from FreshBooks and looks it up in Mailchimp, matching on email only, and records each list, subscription status and tags. You get two tables: overdue clients who are subscribed, with list, tags and amount owed, who are the ones to hold back, and overdue clients who are not subscribed. Clients with no email are counted. It is read-only and changes no tags, segments or subscriptions.
Business outcome
---
Marketing gets a clear list of overdue clients to leave out of the next send, with no tags or subscriptions changed.
scenario
-
Collections and marketing run separately, so a client who is 60 days overdue keeps getting promotional emails. FreshBooks knows who owes money and Drip knows who is in a campaign, but nobody cross-checks the two lists. The client sees a collections reminder and a sales offer in the same week.
What popdock provides
--
Set an optional minimum overdue amount and how many invoices to check, and it finds overdue FreshBooks invoices and totals the balance per client. It then gets each client email and looks the address up in Drip, keeping only people who are active in campaigns. The report lists those clients with their overdue total, oldest due date and campaigns, and counts those with no email or not in Drip. Matching is on email only, truncated results are flagged, and nothing is changed in FreshBooks or Drip.
Business outcome
---
Marketing can stop promoting to clients who owe money, with a short list to review rather than a manual cross-check.
scenario
-
Purchase orders raised in ShipStation are easy to forget to bill. Without checking each supplier against FreshBooks vendors and then each vendor's bills, nobody notices an order with no vendor set up or no bill entered, and payables go unrecorded until the supplier chases.
What popdock provides
--
Give it a purchase order status, or leave it to cover all statuses, and it reads the ShipStation purchase orders with their supplier, order number, reference and dates, looking up supplier contact details where needed. For each supplier it looks for a matching FreshBooks vendor, then checks that vendor's bills for one whose number equals the order or reference number. You get a row per purchase order showing whether the vendor and bill were found, bill status and outstanding amount, grouped into no vendor in FreshBooks, vendor but no bill, and billed. Ambiguous vendor matches are marked rather than picked, and no vendors or bills are created.
Business outcome
---
You see which ShipStation purchase orders have no vendor or no bill in FreshBooks, so unrecorded payables are found before they are missed.
scenario
-
Chasing overdue customers means checking Chargebee dunning in one place and FreshBooks balances in another. Because the two are never put side by side, nobody knows a customer's total exposure, and the same person can be chased twice or not at all. Real exposure stays hidden.
What popdock provides
--
Give a days overdue threshold and optionally a FreshBooks company, and it reads overdue Chargebee invoices and groups them by customer. It finds each customer's FreshBooks client by email and reads their open invoices. You get one row per customer with the Chargebee amount due and oldest due date, dunning status, the FreshBooks outstanding total and oldest due date, and combined exposure per currency, sorted largest first. Amounts are never added across currencies, customers missing from FreshBooks are flagged, and nothing is changed.
Business outcome
---
Collections teams work from one list showing each customer's total exposure, with currencies kept apart and nothing changed.
scenario
-
A manager who wants one number for what a vendor costs has to add card spend from Ramp to expenses and bills in FreshBooks by hand. The same purchase can appear in both systems, so the total can be overstated without anyone noticing, and a vendor with several similar names makes it worse.
What popdock provides
--
Name a vendor and a period, or use the last 12 months, and it totals that vendor's Ramp card transactions and notes Ramp's own 365 day spend for reference. It then totals the FreshBooks expenses and reads the FreshBooks bills for matching vendors, with bill totals and amounts outstanding. You get the Ramp card total, FreshBooks expense total, bill total and outstanding, a grand total and the 10 largest items with their source. Amounts that look like the same purchase in both systems are pointed out as possible double counting but never removed. If several vendors match, each is totalled separately, currencies are never added together, and nothing is changed.
Business outcome
---
You get one cost figure for a vendor across Ramp and FreshBooks, with possible double counting flagged rather than hidden.
scenario
-
Product catalogues and item lists live in two places, so the same SKU can carry a different price or stock level in BigCommerce and FreshBooks. Nobody compares them line by line, and the difference only shows up when a sale or an invoice goes wrong. Nobody has time to compare them row by row.
What popdock provides
--
Optionally name a SKU, or let it read up to a set number of rows, and it compares BigCommerce products and variants with FreshBooks items by SKU. It reports SKUs whose price or stock differs, SKUs found only in FreshBooks and SKUs found only in BigCommerce. Items with no SKU are skipped and counted, and a SKU that appears twice on one side is listed as a duplicate rather than compared. Prices are compared only when they share a currency, and it notes that a FreshBooks unit cost is not necessarily a selling price. Nothing is changed in either system.
Business outcome
---
Price and stock gaps between BigCommerce and FreshBooks surface before a sale goes wrong, with no records edited.
scenario
-
Working out which paying clients are actually engaged in marketing means exporting payments from FreshBooks and lead scores from Drip, then joining them in a spreadsheet. The email addresses rarely line up cleanly, so the comparison is slow and often skipped.
What popdock provides
--
Give a period and how many clients to rank, and it totals FreshBooks payments by client to find the top payers. It gets each client email and looks the person up in Drip for status, lead score, lifetime value and tags. The table is ordered by revenue, with blanks where a client has no email or no Drip record. It then highlights high-revenue clients who are not in Drip, are unsubscribed or have a low lead score. Currencies are never added together, and nothing is changed in either system.
Business outcome
---
You see which top-paying clients are under-engaged in Drip without building a spreadsheet join by hand.
scenario
-
Item prices, names and stock counts live in two places: the FreshBooks item list used for invoicing and the ShipStation catalog used for shipping. Someone has to line them up by SKU in a spreadsheet, and the drift only shows when an order goes out with the wrong price or stock count.
What popdock provides
--
Optionally name the items to check, or leave it blank for all of them. It reads the FreshBooks items, the ShipStation products and the ShipStation stock levels, then matches them by SKU, ignoring case and stray spaces. You get three lists: SKUs only in FreshBooks, SKUs only in ShipStation, and matched SKUs where the price, name, active status or stock count differs, with both values side by side. Items with no SKU and items without stock tracking are left out rather than reported as mismatches. Nothing is changed in either system.
Business outcome
---
Price, name and stock drift between FreshBooks and ShipStation is found by SKU before it reaches an order, with nothing edited.
scenario
-
Mailchimp store spenders and FreshBooks clients sit in separate systems. Nobody can easily see which big buyers in the store are not set up as clients, or whether what a customer spends in the store matches what you invoice them, so people are known in one place and missing in the other.
What popdock provides
--
Give it a minimum spend, or leave it at 100, and it reads the Mailchimp store customers who have spent at least that, with their email, spend and order count. For each one it looks for a FreshBooks client by email and, for matches, totals the invoiced amount by currency. You get three groups: customers who are also FreshBooks clients with store spend beside invoiced total, customers with no FreshBooks client who may need setting up, and a note on matches where the two spend figures differ widely. Matching is on email only, differences are flagged rather than called errors, and nothing is changed in either system.
Business outcome
---
You see which high-spending store customers are missing from FreshBooks and where the two systems disagree, with nothing changed.
scenario
-
Postage paid on ShipStation labels is rarely booked to FreshBooks expenses accurately. Carriers bill in batches, so label costs and expense entries never line up one to one, and shipping cost ends up under or over stated at month end, when it is hardest to untangle.
What popdock provides
--
Give a date range, or leave it blank for the last full calendar month. It totals the ShipStation label costs by carrier, leaving out voided labels, then totals the FreshBooks expenses that mention shipping, postage, freight or one of those carriers. You see a table of carrier, label cost, expense total and difference, which carriers have costs with no matching expense, and up to 10 labels not mentioned in any expense note. Currencies are never added together, and nothing is added or changed in FreshBooks.
Business outcome
---
Month end shipping cost is checked against what was booked, with the gap explained by carrier and no expenses created for you.
scenario
-
Shopify product variants and FreshBooks items are maintained separately, so SKUs go missing on one side and prices drift apart. Finding the differences usually means exporting both lists and comparing them in a spreadsheet, and the result is out of date by the next price change.
What popdock provides
--
Optionally give a SKU prefix to narrow the check, and the FreshBooks company if you have more than one. It reads the Shopify variants and the FreshBooks items, ignoring variants with no SKU and telling you how many, then matches on SKU without regard to case. You get three tables with counts: in Shopify only, in FreshBooks only, and in both with different prices. Prices in different currencies are flagged instead of compared, a possibly capped result is called out, and nothing is fixed or changed in either system.
Business outcome
---
Missing SKUs and price differences between Shopify and FreshBooks are listed in minutes, with no spreadsheet and no edits made.
scenario
-
The store catalog in WooCommerce and the item list in FreshBooks are kept separately, so prices and SKUs quietly disagree. Invoices then go out with the wrong price, and nobody notices until a customer queries one. Checking each product against its item by hand is slow work that rarely gets done.
What popdock provides
--
Optionally give a product status, which defaults to published, and the FreshBooks company. It reads the WooCommerce products and the FreshBooks items and matches them on SKU, ignoring case and spaces, with a second look in FreshBooks before calling an item missing. You get counts, then products with no FreshBooks item, items with no product, and matched pairs where the regular price or stock differs, with both values side by side. A sale price is not treated as a mismatch, duplicate and blank SKUs are listed instead of guessed at, and nothing is changed in either system.
Business outcome
---
Catalog and invoicing item mismatches are found by SKU before they reach an invoice, with no records edited.
scenario
-
New FreshBooks clients are rarely added to the Mailchimp audience, so they miss newsletters and announcements. Comparing the two lists by hand means exporting both, matching emails and watching for people who already unsubscribed, and it is easy to overlook someone.
What popdock provides
--
Give it a signup date and the name of a Mailchimp audience, and it reads the FreshBooks clients who signed up on or after that date, setting aside any with no email. It checks each email against the audience in Mailchimp and notes the status of those found, so unsubscribed or cleaned contacts are not re-added. You get the counts first, then three groups: clients missing from the audience, clients on it as unsubscribed or cleaned, and clients with no email. It is read-only, adds no one to Mailchimp, and stops if the audience is not found or no clients signed up in the period.
Business outcome
---
You get a clean list of new clients missing from your audience, with unsubscribed contacts kept out, and nothing added until you decide.
scenario
-
Marketers export the FreshBooks client list and compare it by hand against the Drip subscriber list. Paying clients get missed, or the match goes wrong because of how the email addresses are formatted. The result is a list that is out of date as soon as it is finished.
What popdock provides
--
Give a sign-up date and how many clients to check, and it reads recent FreshBooks clients and skips any with no email, counting them. Each address is trimmed and lower-cased, then looked up in Drip, and only an email match counts, so a person with the same name but a different email is not a match. It lists the clients who are not in Drip, flags duplicate Drip matches instead of guessing, and lists subscribers whose status is not active. If a call fails it says which side was not checked, and nothing is changed in either system.
Business outcome
---
Paying clients who never reached Drip are found in one request, with no mismatches caused by email formatting.
scenario
-
Orders get paid in the store, but nobody checks the books to see whether an invoice was ever raised. Paid orders sit unbilled for weeks because spotting them means matching order totals and customer emails against the invoice list by hand. Revenue is earned but never billed.
What popdock provides
--
Give a start date, and optionally a payment status, and it reads the BigCommerce orders since then. For each billing email it checks FreshBooks for a client and for an invoice that matches the order, by purchase order number or by amount and currency. You get a table of unbilled orders showing order id, date, email and total, grouped by whether the customer already exists as a FreshBooks client, with a count and total value. Uncertain matches go to a separate needs review list, and nothing is created in either system.
Business outcome
---
Finance sees which paid orders still need an invoice, with doubtful matches kept apart and nothing created.
scenario
-
Billable hours and expenses are easy to forget. Finding them means checking time entries in one place and expenses in another, then working out which client and project each belongs to, so money sits unbilled for weeks. Work that was done is never invoiced, and nobody notices until later.
What popdock provides
--
Give a start date, or use the last 90 days, and it finds billable time entries that have not been billed and billable expenses that are not on an invoice. Durations are converted from seconds to hours, and the results are grouped by client with hours, expense total and project ids. Client names and project titles are filled in for the top results, and the oldest unbilled item per client is highlighted. Time with no client goes on a separate unassigned line, and nothing is changed in FreshBooks.
Business outcome
---
Forgotten billable hours and expenses are found by client, so they can be invoiced instead of lost.
scenario
-
Paid Shopify orders are billed in FreshBooks by hand, and some are never invoiced. Spotting them means scrolling through orders and searching FreshBooks for each one, so the misses surface only when the books are reviewed and the revenue is already late.
What popdock provides
--
Give a start date, or leave it blank for the last 30 days, and the FreshBooks company. It reads the paid, non-cancelled Shopify orders since then and the FreshBooks invoices for the same period, treating an order as invoiced when an invoice number or PO number equals the order name. For the orders left over it checks FreshBooks for a client with the order email. You get a table of uninvoiced orders with date, email, total, currency and whether the client exists, plus counts. It warns when record caps may hide results, and creates nothing.
Business outcome
---
Paid Shopify orders that never reached FreshBooks are listed with the client status, so none are missed at month end.
scenario
-
Clients who unsubscribed or bounced in Mailchimp are easy to miss when chasing overdue invoices, so reminders go to addresses that no longer work. Spotting them means listing unreachable contacts in Mailchimp and then checking each one against FreshBooks for what they still owe.
What popdock provides
--
Name a Mailchimp list, and optionally a minimum balance, and it finds the contacts who are unsubscribed or cleaned, with their reasons. It matches each to a FreshBooks client by email, leaving out contacts who are not clients, and totals each client's outstanding invoices with the oldest due date. You get a table sorted by amount owed, with email, Mailchimp status and reason, number of invoices and oldest due date, then a grand total and the count of contacts who were not clients. Totals stay per currency. It is read-only, never sends invoice emails, and suggests a call or letter instead.
Business outcome
---
You see which clients cannot be reached by email but still owe money, ranked by amount, so you can chase them another way.
scenario
-
Store sales that never reach the accounting system tend to show up only at month end. Someone then compares WooCommerce order lists with FreshBooks payments by hand, working out whether the client, the invoice or just the payment is missing. Each order needs several lookups, so the check is put off.
What popdock provides
--
Optionally give an order status, which defaults to completed, a look-back in days, which defaults to 30, and the FreshBooks company. It reads the WooCommerce orders, then for each customer email finds the FreshBooks client, invoices and payments. An order counts as covered when the client has a payment of the same amount within 7 days of the paid date. You get counts and the uncovered orders grouped as no client, client but no invoice, or invoice but no payment. Only same currency payments are compared, and nothing is changed in either system.
Business outcome
---
WooCommerce sales missing from FreshBooks are grouped by what is absent, so the right fix is clear before month end.
scenario
-
Before talking to a supporter who is also a customer, staff open two systems and piece together invoices, payments, gifts and contact history by hand. It is slow, and a conversation can start without knowing about an unpaid invoice or a recent gift. Details get missed.
What popdock provides
--
Give a client email and a look-back period, and it finds the client in FreshBooks and the matching constituent in Bloomerang. You get one profile with identity, billing (invoices raised, total, outstanding and payments received), giving (gift count, total and latest gift) and relationship (recent interactions and open tasks), plus two or three plain observations. Each currency is shown on its own and never added across systems. If there is no Bloomerang record it reports billing alone, and it never sends invoices or changes anything.
Business outcome
---
Staff walk into a supporter conversation knowing both the billing and giving history, with nothing sent or changed.
scenario
-
Answering a client query means opening FreshBooks for the balance and then Drip for their email history. The two views are never put side by side, so the answer misses that a client with an overdue balance is still in a promotional campaign. Switching between the two screens takes time while the client waits.
What popdock provides
--
Give a client email and how far back to look at payments, and it finds the client in FreshBooks and stops if the email matches several clients. It totals the open invoices and the payments received, then looks the same email up in Drip and lists the campaigns the person is in. The report shows balance, oldest due date, payments, Drip status, tags, lead score, lifetime value and each campaign. If the client is in only one system it says so, amounts are not converted between currencies, and nothing is changed.
Business outcome
---
You can answer a client query with their balance and email engagement in one view, without switching between two systems.
scenario
-
Checking one client means opening FreshBooks for balances and payments, then opening Mailchimp for their list status and email engagement, and piecing the two together by email. It takes several screens, and a mismatch such as a large balance on someone who has unsubscribed is easy to miss.
What popdock provides
--
Give a client's email address, and optionally a FreshBooks company, and it finds that client in FreshBooks, stopping if there are several matches or none. It totals invoiced, outstanding and paid amounts and notes the latest payment date. It then looks the same email up in Mailchimp for each list, status, tags and rating, and summarises recent opens, clicks and bounces. You get one profile with a note when the picture is unusual. Matching is on email only, differing name spellings are reported, and nothing is added, updated or deleted in either system.
Business outcome
---
You get one profile of a client with billing position and Mailchimp standing side by side, without opening two systems.
scenario
-
Finding which vendors also give money means exporting both systems and comparing email addresses in a spreadsheet. The work is tedious enough that overlaps, and any conflicts of interest they create, get missed. Nobody has time to do it often.
What popdock provides
--
Give a vendor limit and optionally a FreshBooks business, and it reads that many vendors from FreshBooks. Each vendor email is looked up in Bloomerang, and for every match it adds up the gifts. You get a table of vendor name, email, Bloomerang name and account number, number of gifts, total given and latest gift date. Vendors with no email are skipped and counted, matching is on email only with no name guessing, and when a vendor matches several constituents it lists them without totalling. Nothing is changed in either system.
Business outcome
---
Finance and fundraising see which vendors are also donors, with ambiguous matches listed and no records changed.
scenario
-
Working out whether a billing client is the same person as a donor means searching FreshBooks and Bloomerang by hand. Different name spellings make it unreliable, so people either miss real matches or link the wrong records. A wrong link then pollutes both records.
What popdock provides
--
Give a client email, and optionally a FreshBooks business, and it finds the client in FreshBooks, then looks for the Bloomerang constituent by the same email. If that fails it tries the name and marks any hit as probable only. It shows the two records side by side, covering name, email, phone, location and Bloomerang status, type and account number, with every difference flagged and the match called exact or probable. It stops when several records match, reports billing only when there is no donor, and never writes to either system.
Business outcome
---
You know whether a client and a donor are the same person, with differences flagged and nothing written to either system.
scenario
-
A bookkeeper checking a Shopify order has to find the client in FreshBooks, search for the invoice, and compare totals and payment status by eye. Missing invoices and wrong amounts slip through, especially when tax and shipping make the totals differ.
What popdock provides
--
Give a Shopify order name such as #1001 and the FreshBooks company. It reads the order, finds the FreshBooks client by the order email, and looks for the invoice whose PO number or invoice number equals the order. You get a table comparing total, currency, payment status and outstanding, each marked Match or Differs with the difference. If several invoices could match it lists them and asks, cancelled orders are flagged instead of compared, and nothing is changed in either system.
Business outcome
---
Each Shopify order is checked against its FreshBooks invoice in one step, with wrong or missing invoices called out.
scenario
-
Working out whether the people who opened a Mailchimp campaign are paying customers means exporting the openers, finding each one in FreshBooks and adding up their payments by hand. Because it is slow, the question often goes unasked, and when it is asked the matching is patchy.
What popdock provides
--
Give it a campaign and a lookback period, or leave it at 90 days, and it confirms the Mailchimp campaign and reads its openers with their open counts. It then looks each opener up in FreshBooks by email, keeps only the matches, and totals each matched client's payments in the window by currency. You get the campaign title and send date, how many people opened and how many are clients, a table of matched clients with opens, payments and last payment date, and the total paid with the share of openers who are clients. It says plainly that this shows association, not causation, and changes nothing in either system.
Business outcome
---
You see how many campaign openers are paying clients and what they paid, with the caveat that it shows association only.
scenario
-
Employee reimbursements paid through Ramp often never get booked as FreshBooks expenses. Finding the gaps means scrolling the Ramp reimbursement list beside the FreshBooks expense list and checking amounts and dates by eye, and the books end up understated.
What popdock provides
--
Give it a date range, or use the last 30 days, and optionally a minimum amount, and it reads the Ramp reimbursements and the FreshBooks expenses for the same dates. It matches a reimbursement to an expense when the amount is equal and the dates are within 3 days, using vendor or merchant text as a tie-breaker, and one expense can match only one reimbursement. You get a table marking each reimbursement as recorded or missing in FreshBooks, the FreshBooks expenses with no Ramp reimbursement, and the total of missing items. Amounts in different currencies are never matched, and it adds no expense unless you ask separately.
Business outcome
---
You see which Ramp reimbursements are missing from FreshBooks and how much they total, so the books can be corrected on your say.
scenario
-
Refunds issued in the WooCommerce store are easy to forget to mirror in FreshBooks. The credit note never gets created, the customer balance is wrong, and the gap is found only when someone compares the two records by hand, one refund at a time, which rarely gets done on a busy week.
What popdock provides
--
Optionally give a look-back in days, which defaults to 30, and the FreshBooks company. It reads the recent WooCommerce refunds, finds each customer in FreshBooks by email, and reads their credits for the same period. A refund counts as covered when a credit of the same amount was created within 7 days or its number mentions the order. You get counts, then refunds with no credit, credits with no refund and refunds whose client is missing, plus the uncovered total. Equal amount matches that are ambiguous are labeled, not paired, and nothing is changed.
Business outcome
---
Refunds missing a FreshBooks credit note are found with their total value, so customer balances can be corrected.
scenario
-
Marketing cannot tell whether a campaign led to paid invoices. Opens and clicks live in Constant Contact and payments live in FreshBooks, so someone has to export both, match the people by email and add up what they paid after the send date. The result is often a guess based on a hunch rather than a number.
What popdock provides
--
Name a Constant Contact campaign and a FreshBooks company, plus how many days to look at after the send, and it finds the campaign and the people who opened or clicked. Each address is matched to a FreshBooks client by email only, and the payments that client made in the window are totalled. Amounts in different currencies are kept apart, and the report says plainly that it shows timing, not proof the campaign caused a payment. Nothing is changed in either system and the campaign is never resent.
Business outcome
---
You can see which engaged contacts paid after a campaign, in the right currencies and without overclaiming what the campaign caused.
scenario
-
Chasing overdue invoices without knowing the client is also a donor can damage a relationship the fundraising team has built. Finance rarely sees gift history or recent contact, so a firm reminder can land on someone who gave last month. Nobody wants to explain that afterwards.
What popdock provides
--
Give a cutoff date, and optionally a FreshBooks business, and it lists unpaid FreshBooks invoices due before then. It looks up each client's email in Bloomerang and, for donors, checks recent gifts and contact from the last 90 days. You get a table sorted by amount outstanding, with days overdue, whether the client is a donor, last gift and last interaction, and totals per currency. Clients with no email are listed as unmatched instead of guessed at. It sends no reminders and changes nothing in either system.
Business outcome
---
Finance decides how to chase each overdue invoice knowing who is a donor, with no reminders sent and no records changed.
scenario
-
Comparing a Shopify customer with the FreshBooks client for the same person means opening two screens and reading fields side by side. Mismatched emails, names, phone numbers or countries go unnoticed until an invoice or a message goes to the wrong place.
What popdock provides
--
Give an email and the FreshBooks company if there is more than one. It finds the Shopify customer and the FreshBooks client with that email, stopping with a list if either side has several matches. You get a side by side table of name, email, phone and country, each row marked Match or Differs, plus a line on the customer's Shopify order count and total spent. Emails and names are compared ignoring case, and country codes are treated as matching a full name. If the client is missing it says so, and nothing is changed.
Business outcome
---
Customer detail mismatches between Shopify and FreshBooks are spotted in one view, with no records edited.
scenario
-
Someone exports BigCommerce customers and FreshBooks clients and compares email addresses in a spreadsheet. The two lists drift apart a little each week, and customers missing from the books or clients missing from the store go unnoticed. Missing records mean missed invoices.
What popdock provides
--
Give a start date and a row limit, and it reads recent BigCommerce customers and looks each email up in FreshBooks, then does the same in reverse for FreshBooks clients. You get matched pairs, customers missing in FreshBooks, clients missing in BigCommerce and customers skipped for having no email, with counts. Several clients on one email are reported as ambiguous with their ids instead of picking one, and if either system returns nothing it stops. Nothing is added or changed in BigCommerce or FreshBooks.
Business outcome
---
Teams see who is missing on each side in minutes, with ambiguous matches flagged and no records changed.
scenario
-
Orders are checked against invoices by eye, one customer at a time. Unbilled orders and invoices with the wrong amount or payment state only surface at month end, when they are harder to untangle. Fixing them then means digging back through weeks of orders.
What popdock provides
--
Give a date range and it reads the BigCommerce orders in that period, ignoring cancelled and incomplete ones and saying how many. It finds each customer in FreshBooks by email and matches an invoice by purchase order number, or by the same amount and currency. If two invoices fit equally well, it lists both and picks neither. Each order gets a verdict of matched, amount differs, payment state differs, no invoice or no client, with counts per verdict. Amounts are compared only in the same currency, and nothing is changed in either system.
Business outcome
---
Finance catches unbilled orders and invoice differences within the period, with ties listed and no records changed.
scenario
-
Billing customers and accounting clients drift apart, and finding who is missing or misspelled means comparing two exports by eye. Records without an email or with duplicates make the comparison harder still. Small mismatches later cause failed matches in reports.
What popdock provides
--
Give a FreshBooks company and optionally a start date, and it reads Chargebee customers and FreshBooks clients, matching them on email. You get counts first, then customers in Chargebee only, clients in FreshBooks only, and those in both with differences in name, company or organization, and currency. Customers with no email go in a separate cannot match list, and duplicates on one side are listed instead of picking one. Nothing is added or changed in Chargebee or FreshBooks.
Business outcome
---
Teams see who exists in only one billing system and where records differ, with duplicates listed and nothing changed.
scenario
-
A bookkeeper opens both billing systems to check one customer's invoices line by line. Differences in amount or paid status slip through, and an invoice that exists in only one system can go unnoticed for months. Month end then brings surprises.
What popdock provides
--
Give a customer email, and optionally a FreshBooks company and look-back period, and it finds the customer in Chargebee and the client in FreshBooks. If either side returns several records it stops and lists them. It pairs the invoices by reference number or by the same total and a date within 3 days, then reports each pair with totals, amount due or outstanding, status on each side and a verdict. Uncertain pairings go to an unsure list with the reason, and nothing is changed in either system.
Business outcome
---
Bookkeepers see which invoices match and which differ for a customer, with doubtful pairings kept apart and nothing changed.
scenario
-
Someone exports the client list from FreshBooks and the contact list from Constant Contact and compares the emails by hand. Clients get missed from the mailing list, and clients who unsubscribed can still end up mailed because the two lists never line up.
What popdock provides
--
Name the FreshBooks company and how many clients to check, and it reads the clients and looks each email up in Constant Contact. Matching is on email address only, ignoring case and spaces, never on name, and it lists the candidates rather than guessing when several contacts share an email. It reports clients missing from Constant Contact, clients who have unsubscribed, clients with no email and contacts with no matching client. It warns when a list may be truncated, and nothing is changed in either system.
Business outcome
---
You get a clear list of clients missing from the mailing list or unsubscribed, without comparing two exports by hand.
scenario
-
Matching invoices to shipments by hand means switching between FreshBooks and ShipStation and comparing order numbers line by line. Orders end up billed but never shipped, or shipped and never billed, and the gap is found only when a customer complains.
What popdock provides
--
Give a start date, or leave it blank for the last 30 days. It reads the non-draft FreshBooks invoices and the ShipStation shipments since that date, and matches them on the shipment order reference against the invoice number or PO number, ignoring case and leading zeros. If nothing matches it falls back to the customer name and labels that match as weak. You get tables of billed and paid invoices with no shipment, shipments with no invoice, and matched pairs that were cancelled or voided, with counts. Nothing is changed in either system.
Business outcome
---
Orders billed but not shipped, or shipped but not billed, are listed with counts, so the gap is closed before a customer notices.
scenario
-
Matching money received in FreshBooks to gifts recorded in Bloomerang is done line by line at month end. With payments, other income and gifts across two systems, unmatched items are easy to miss and refunds muddy the totals. Finance ends up with a gap and no clear cause.
What popdock provides
--
Give a date range, and optionally a FreshBooks business, and it reads FreshBooks payments and other income, where donations are often booked, along with Bloomerang transactions. It pairs records with the same amount within 3 days, confirming the donor by email and preferring the record whose email matches when amounts tie. You get matched pairs, FreshBooks receipts with no Bloomerang gift and Bloomerang gifts with no FreshBooks receipt, with the net difference per currency. Refunds are listed separately, a customer payment is never assumed to be a gift, and nothing is changed.
Business outcome
---
Month end matching of receipts to gifts is done in one pass, with gaps and refunds listed and no records changed.
scenario
-
Matching payments to invoices by hand for a period hides problems. Payments can have no invoice, some are funded from credit, and some invoices look paid but still show a balance, and none of it stands out in a long list. Month-end then turns into a line-by-line hunt for the cause.
What popdock provides
--
Give a start and end date, and it pulls the FreshBooks payments for that period, then looks up each invoice once and compares the payments in the period to the invoice paid and outstanding amounts. The report totals what was received by currency, payment type and gateway, lists payments with no invoice, and lists payments funded from credit. It flags invoices whose payment status disagrees with their payments, and ends with a short list needing human attention. Nothing is edited or deleted in FreshBooks.
Business outcome
---
Orphan payments and mismatched invoices are surfaced for a period, so month-end checks take minutes instead of a manual match.
scenario
-
A bookkeeper opens the same vendor's bills in FreshBooks and in Ramp and ticks them off line by line. Amount mismatches and bills entered in only one system are easy to miss, and the two sets of books slowly drift apart until month end, when the difference is hard to trace.
What popdock provides
--
Name a vendor and a due date range, or use the last 90 days, and it finds the vendor in FreshBooks, listing the options and waiting if more than one matches. It reads that vendor's bills in FreshBooks and in Ramp, and if the vendor is not in Ramp it reports that and shows the FreshBooks bills only. It matches on bill number, or on amount and due date within 3 days when numbers are missing. You get one row per bill with amounts, statuses and a verdict of match, amount differs, only in FreshBooks or only in Ramp, then totals per side and the net difference. Currencies are never converted and neither system is changed.
Business outcome
---
You see every bill that matches, differs or exists in only one system, with the net difference, without changing either record.
scenario
-
Someone exports the vendor lists from FreshBooks and Ramp and compares them by eye. Vendors that exist in only one system slip through, and name variants such as Contoso Inc and Contoso, LLC look different even when they are the same company, so duplicates and gaps build up.
What popdock provides
--
Optionally give part of a vendor name, or leave it to compare all vendors up to 200 per side, and it reads the vendors from FreshBooks and from Ramp, including contact emails where they matter. It matches on email first and then on name, ignoring case, punctuation and suffixes such as Inc, LLC and Ltd. Close but not exact names are listed as possible matches for a person to decide and are never merged. You get three tables: in both with both IDs, only in FreshBooks, and only in Ramp with last year's spend, plus counts and the five highest-spending Ramp-only vendors. It reports when a list is capped and changes nothing.
Business outcome
---
You see which vendors exist in only one system and which names need a human decision, with nothing merged or changed.
scenario
-
Store customers in WooCommerce and clients in FreshBooks drift apart over time. Finding who is missing from one side or spelled differently on the other means exporting both and comparing them by eye. Duplicates and missing emails make it harder still.
What popdock provides
--
Optionally give a since date and the FreshBooks company. It reads the WooCommerce customers and the FreshBooks clients and matches them by email, ignoring case and spaces, with a second look in FreshBooks before calling someone missing. You get counts, then three lists: in WooCommerce only, in FreshBooks only, and in both with differences in names or company. Customers with no email, and anyone sharing an email with another record, are listed separately instead of guessed at. Names are never used to match, and nothing is changed in either system.
Business outcome
---
Customers missing or different between WooCommerce and FreshBooks are listed by email, with no guessing and no edits.
scenario
-
Understanding where a client stands means opening invoices, payments, credits and estimates separately and adding them up by hand. With several currencies and a year of activity, it is easy to miscount or miss an overdue invoice. A single missed credit changes the balance you quote the client.
What popdock provides
--
Give a client name and, if you like, a start date, and it finds the client in FreshBooks and stops if the name matches nothing or several clients. It reads their invoices, payments, credits and estimates since the start date and totals billed, paid, outstanding and unused credit, with each currency kept separate. It lists overdue invoices with days late, estimates not yet invoiced, and the average days from invoice to payment where it can be worked out. If there was no activity it says so, and nothing is changed.
Business outcome
---
You get a client account summary in one request, with every figure drawn from FreshBooks and no manual adding up.
scenario
-
Preparing for a client conversation means opening FreshBooks for invoices and payments, then Constant Contact for consent and campaign clicks, and stitching the two together. It is easy to walk into the call without knowing the client owes money or has opted out of email.
What popdock provides
--
Give a client email and the FreshBooks company, and it finds the client in FreshBooks and stops if the match is missing or ambiguous. It totals what was invoiced, paid and is outstanding, and notes the last payment. It then checks Constant Contact for the same email and reports permission to send, lists, tags and opens and clicks over your chosen look-back period. It closes with a line of observations and reports name conflicts instead of picking one. Nothing is changed in either system.
Business outcome
---
You walk into the client conversation with billing and marketing history side by side, ready in one request.
scenario
-
Year-end 1099 prep means working out which vendors were paid how much, using bills and payments held in separate places. Vendors over the reporting threshold with no tax ID are easy to miss until the filing deadline is close. Each vendor needs the same check, and it is tedious to repeat.
What popdock provides
--
Give a start and end date, and it lists the FreshBooks vendors marked as 1099 vendors. It reads the bill payments and bills in the period, then uses each bill to link payments to the right vendor. For each vendor it reports the total paid per currency, the number of payments, and whether tax ID, country and email are on file. Vendors above 600 in total with no tax ID are flagged. The totals rest on recorded bill payments only, and nothing is edited in FreshBooks.
Business outcome
---
1099 prep starts with a clear per-vendor total and a list of vendors missing tax details, found before filing time.
scenario
-
Paying vendors on time needs bills, balances and past payments checked across several screens. Due dates slip because nobody has a single ordered list of what is overdue and what is coming up. A late payment can cost a vendor relationship or a late fee.
What popdock provides
--
Give a due-by date, or use the next 14 days, and it reads the unpaid FreshBooks bills due on or before that date. It looks up the vendor names in one pass rather than bill by bill. The report is a table of vendor, bill number, due date, days overdue or until due, outstanding amount and currency, with overdue bills first and subtotals per currency. Partially paid bills are mentioned, and payment history is fetched only if you ask. Nothing is recorded or changed in FreshBooks.
Business outcome
---
You get an ordered list of bills to pay, overdue first, so due dates stop slipping.
scenario
-
Expense review means scanning a long list by eye for missing receipts, duplicates and odd vendors. Duplicate entries, uncategorised items and a one-off large charge are easy to miss, and billable expenses can sit without an invoice. Reviewers tire quickly, so the later rows get less attention.
What popdock provides
--
Give a start and end date, and it reads the FreshBooks expenses in that period. It flags probable duplicates with the same vendor, amount and date, expenses with no receipt (largest first), items with no category and amounts well above the usual for that vendor. It also lists billable expenses that are not on an invoice. The report gives totals by currency, top vendors by spend and the flagged items with their ids, and duplicates are treated as possible, never certain. Nothing is edited or deleted.
Business outcome
---
Problem expenses are flagged with ids in one pass, so review time goes to the items that need it.
scenario
-
Judging whether a FreshBooks project is making money means opening the project for its budget and rate, the time entries for logged hours, the expenses for costs and the invoices for what has been billed. Each screen tells part of the story, and an overrun or unbilled work is easy to miss until the project is over.
What popdock provides
--
Name the project and it finds the matching FreshBooks project, and if no project or several projects match it lists them and stops rather than guessing. It then compares the budget with hours logged, splits time into billed and unbilled and billable and non-billable, and totals expenses and how much of them is billable. You get unbilled hours valued at the project rate, expenses not yet recovered, a check on whether the due date has passed while the project is open, and a one-paragraph verdict of on track, at risk or over. Nothing in FreshBooks is changed, and if the budget or rate is missing it says the comparison cannot be made.
Business outcome
---
You see whether a project is on track, at risk or over budget in one read, with unbilled work and unrecovered expenses called out.
scenario
-
Chasing late payers means scanning the FreshBooks invoice list, working out how many days late each one is and guessing who to call first. Large or very late invoices get buried among small ones, and a customer with several overdue invoices is easy to underestimate.
What popdock provides
--
Give it an as-of date, or leave it as today, and it pulls the FreshBooks invoices that are past due with money still outstanding, ignoring drafts and paid invoices. It works out days late, groups invoices by customer, and ranks customers by total outstanding and then by oldest invoice. You get a table of invoices late, total owed and oldest days late, plus the top three customers to chase and why, with a separate total for each currency. It is read-only, never sends email or changes an invoice, and says so and stops when nothing is overdue.
Business outcome
---
You know which late payers to call first, ranked by amount and age, without touching a single invoice.
scenario
-
Estimates sent from FreshBooks go quiet. Nobody has a single view of which ones were accepted but never invoiced, which were sent and never answered, and which are still drafts, so open quotes go stale, follow-ups depend on memory, and accepted work is left unbilled.
What popdock provides
--
Give it a start date, or leave it as the last six months, and it reads the FreshBooks estimates created since then. It sorts them into accepted and invoiced, accepted but not invoiced, sent but not answered, and draft, and works out each one's age in days. You get totals and counts per class for each currency, then a ranked follow-up list by value and age with customer and estimate number, with accepted but not invoiced called out as money left on the table. It is read-only and never converts or edits an estimate.
Business outcome
---
You see which estimates need a follow-up and which accepted work was never invoiced, ranked by value and age, with nothing changed.
scenario
-
An account manager preparing for a customer call has to work out what the customer bought from Shopify and what they still owe from FreshBooks. Pulling both together by hand takes several screens, and amounts in different currencies are easy to add up wrongly.
What popdock provides
--
Give a customer email, the FreshBooks company and how many days to look back, which is 365 by default. It reads the customer's Shopify orders and their FreshBooks invoices and payments, then summarizes Shopify orders and total beside the FreshBooks invoiced, paid and outstanding totals. It lists any overdue invoice and any paid Shopify order with no invoice. Currencies are reported separately and never added together, one side is still reported if the other is missing, and nothing is changed.
Business outcome
---
Account managers see what a customer bought and what they owe in one summary, ready before the call.
scenario
-
Answering a customer who asks where their order is means opening FreshBooks to check the invoice and payment, then ShipStation for the shipment, label and tracking. You piece the story together from separate screens, and it is easy to miss that an invoice was paid but never shipped.
What popdock provides
--
Give an invoice number. It finds the invoice in FreshBooks and stops with a list if more than one invoice matches. It then reads each payment, finds the ShipStation shipment under the invoice number or PO number, and reads the label, tracking and delivery details, ignoring voided labels. You get one dated timeline of invoice created, payments, shipped and delivered, with a one line status such as paid and delivered or paid but not shipped. If no shipment exists it says so rather than guessing, and nothing is changed.
Business outcome
---
One timeline answers where an order is, from invoice to payment to delivery, without searching two systems.
scenario
-
Finding where an online order ended up in the books means opening the WooCommerce order, then hunting through FreshBooks for its client, its invoice and any payment. It is slow, and a missing client or an unpaid invoice is easy to overlook, so the answer to the customer takes longer than it should.
What popdock provides
--
Give a WooCommerce order number and the FreshBooks company. It reads the order and its lines, stopping with a list if more than one order matches. It then finds the FreshBooks client by the order email, the invoice for that client and the payments against it, using a candidate match on amount and date only when the invoice number is not found. You see the order summary, then the client, invoice and payments, with plain statements such as order paid but invoice unpaid or amounts that differ. Loose matches are labeled as candidates, and nothing is changed.
Business outcome
---
One check shows whether an online order has a client, invoice and payment in FreshBooks, and where it falls short.
No items found.
Ready to unlock your data?
Start building tools that turn your business systems into AI superpowers
Join forward-thinking companies that are transforming their operations with intelligent, secure AI automation.