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Accounts Payable

Scale Multi-Client AP with Vic.ai Autopilot Invoice Coding

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What the source describes

Vic.ai's case study describes Countsy, an outsourced accounting firm for startups, which Vic.ai says processes up to 3,500 invoices a month across hundreds of clients on NetSuite, Bill.com and Expensify. Countsy adopted Vic.ai to automate invoice ingestion, coding and approval workflows, shifting accountants from manual entry to AI-assisted review, with the AI predicting GL dimensions such as cost account, department and location.

See also our roundup of AI for invoice processing for other AP tools by invoice volume.

Result or outcome

Vic.ai's case study claims that 78% of Countsy's invoices are processed on Autopilot with no human input, that coding accuracy reached 95%, and that average processing time dropped 84%, from 4 to 5 minutes per invoice to under 2 minutes. These are the vendor's own figures, not independently verified.

How to run it

  1. Standardize each client's accounting, payment and reimbursement tools so invoices flow into Vic.ai consistently, as the firm in the case study did.
  2. Let Vic.ai ingest invoices and predict GL dimensions such as cost account, department and location for each client.
  3. Shift staff from entering invoices to reviewing the AI's coding and approval routing, correcting fields where the prediction is wrong.
  4. Each month, export invoice-level data with edits and time spent, then paste it into the prompt on this page to measure results per client.
  5. Use the summary to target clients or vendors that need better coding rules, and track per-employee processing time to plan training.

Try it yourself

Copyable prompt

I run AP for [number] bookkeeping clients and want to measure whether an AI invoice coding tool is paying off. Using the data below for [month], calculate for each client: invoices processed, percentage approved with no edits, percentage with GL or department corrections, average minutes per invoice, and the vendors most often corrected. Then summarize firm-wide trends in three bullet points and suggest which clients or vendors need better coding rules or training data. Use only the numbers I provide and show your formulas. Data: [paste export with client, vendor, invoice ID, edited yes/no, fields changed, minutes spent].

Replace the [bracketed] placeholders. Remove client names, account numbers, and personal data before pasting into any AI tool.

What to review

Vic.ai's case study reports Countsy's own accuracy and Autopilot share; treat those as the vendor's claims, not a benchmark for your clients. Check that the AI's monthly summary uses only your exported numbers and that its formulas recompute correctly. Before letting invoices run without review, sample Autopilot invoices against source documents and confirm department and location coding matches each client's chart of accounts.

Related free download: 50 AI Prompts for Bookkeepers (Prompt pack (PDF))

Source: Vic.ai Case Study

Not professional advice. This is general information, not accounting, tax, legal or financial advice. Check AI output yourself and consult a licensed professional for your situation. See our Terms of Use.

This entry summarizes a public source. Features, plans, and availability change, so confirm details with the vendor. Tool names are trademarks of their respective owners.

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