Accounting teams are increasingly using AI to handle document-heavy tasks like data extraction, bank reconciliation, and workpaper prep. But there is a meaningful difference between using a general-purpose AI assistant like Claude, ChatGPT, or Gemini and using a purpose-built tool like Rima that is designed specifically for accounting workflows.
We break down where general AI tools hit their limits and where specialized accounting AI picks up, so you can make an informed decision about which approach fits your team.
Can You Use Claude or ChatGPT for Accounting Document Work?
Yes, but with significant limitations. General-purpose AI assistants like Claude and ChatGPT can read PDFs, extract some data, and perform basic spreadsheet tasks. However, they are designed to handle a wide range of questions across every domain — not to execute repeatable, auditable accounting workflows at scale. For a one-off question about a single invoice, a general tool may work fine. For processing 40 vendor invoices into a structured Excel worksheet every month with traceable accuracy, the gaps become clear fast.
What Are the File Upload Limits with Claude and ChatGPT?
General AI tools impose strict file constraints that create friction for accounting teams processing documents in volume.
| Limitation | Claude | ChatGPT | Rima |
|---|---|---|---|
| Max file size | 30 MB per file | 512 MB per file | Built for bulk document processing — handles large and multi-page files natively |
| Files per session | 20 files per conversation | Limited by context window | Designed for batch uploads across an entire workflow |
| PDF page limit | Full analysis under 100 pages; text-only above 100 | Varies by model | Processes documents of any length with extraction rules |
| Context window | ~200K tokens shared across files, chat history, and responses | ~128K tokens (GPT-4o) | No shared context limit — each document processed against your defined blueprint |
| Excel support | Requires special tool enablement; CSV recommended over .xlsx | Basic spreadsheet reading | Native Excel plugin — outputs go directly into your working spreadsheet or any format |
For accounting teams that regularly work with dozens or hundreds of invoices, statements, and ERP exports, these limitations are not edge cases. They are daily obstacles.
Does Rima Work Inside Excel the Way Claude Does?
No — and this is one of the most important differences. Both Rima and Claude offer Excel integration, but they serve fundamentally different purposes.
Claude's Excel integration is built for general-purpose spreadsheet tasks: writing formulas, analyzing data already in a sheet, generating charts, or answering questions about a dataset. It works well as a smart assistant sitting alongside your spreadsheet.
Rima's Excel plugin is built specifically for accounting document workflows. Once you build your workflow in the Rima app, if your output is an Excel file, you can leverage the Rima plugin when you download that Excel for provenance. You can trace the source of each data point and interact with your data to understand where it originated.
This provenance layer is critical for accounting teams. When a manager or auditor asks “where did this number come from?”, the answer is one click away — not buried in a chat history with an AI assistant. Tools like Claude do not provide this kind of document-to-cell traceability in Excel.
How Does Accuracy Compare Between General AI and Specialized Accounting AI?
Accuracy is the single biggest concern for any accounting team evaluating AI tools, and this is where the difference between general-purpose and specialized tools is most pronounced.
General-purpose AI assistants like Claude and ChatGPT are trained to be helpful across every domain — from writing poetry to explaining physics to reading financial documents. This breadth is impressive, but it means the model is not specifically optimized for the edge cases accounting teams encounter daily: inconsistent vendor invoice formats, ERP exports with merged cells and irregular headers, scanned documents with poor print quality, or multi-currency line items.
When general AI tools encounter these edge cases, accuracy can degrade without warning. The model may extract a subtotal instead of a total, misread a date format, or silently skip a line item. In a conversational context, these errors might go unnoticed until they cause a reconciliation discrepancy downstream.
Purpose-built accounting AI tools like Rima are designed to handle exactly these scenarios. The extraction and processing logic is tuned for accounting document types. The system is built around a review workflow where a human validates the output before it becomes final. And over time, the blueprints you create teach the system how your specific documents should be processed, creating consistency that a general AI chat session cannot replicate.
The difference is not that general AI is bad at reading documents. It is that accounting requires a level of consistent, verifiable accuracy that general tools are not architected to deliver at scale.
What Happens to Your Data When You Use Claude or ChatGPT?
Data privacy is a growing concern for accounting teams handling sensitive financial information, and the policies of general AI providers have shifted significantly.
Claude (Anthropic): As of September 2025, Anthropic updated its consumer terms to allow the use of chat data from Free, Pro, and Max accounts for model training — with the default setting turned on. Users who opt in have their data retained for up to five years. Users can opt out, which maintains a 30-day retention window. Business accounts (Claude for Work, API, and enterprise plans) are excluded from training. However, many small and mid-size accounting teams use Pro accounts, not enterprise plans, which means their data may be used for training unless they actively change their settings.
ChatGPT (OpenAI): OpenAI's consumer plans similarly allow data to be used for training unless users opt out. Enterprise and API accounts are excluded, but consumer and Plus plans are included by default.
Rima: As a purpose-built accounting tool, Rima is designed from the ground up to treat your documents as your intellectual property. Your data is not used to train AI models. This is a foundational architectural decision, not an opt-out toggle buried in settings.
For accounting teams processing client invoices, payroll records, tax filings, and bank statements, this distinction matters. You should not have to wonder whether your client's financial data is being used to train a model that serves millions of other users.
Can Claude Build Reusable Workflows for Recurring Accounting Tasks?
Not in a meaningful way. This is where the fundamental design difference between a conversational AI and an accounting automation tool becomes most apparent.
With Claude or ChatGPT, every conversation starts from zero. You can upload a document, ask the model to extract data, and get a result. But next month, when you need to do the same thing with the same type of document, you are starting over. You need to re-explain the task. You need to re-upload your template. The model has no memory of your workflow unless you manually reconstruct the context each time.
Rima's blueprint system solves this by design. You define your task once, describe what data to extract, where it goes in your spreadsheet, and what processing rules to apply. That blueprint becomes a reusable template that anyone on your team can execute with one click. No repeated setup. No re-explaining. No variation in output quality depending on how well someone phrases their prompt that day.
For a team of 8 people processing the same document types every month, this is the difference between a tool that saves time once and a system that eliminates manual work permanently.
How Do General AI Tools and Rima Compare for Accounting Teams?
| Capability | General AI (Claude, ChatGPT) | Rima |
|---|---|---|
| Primary design | Answer any question across all domains | Automate document and spreadsheet work for accounting |
| Document extraction | Can read and summarize; limited batch processing | Purpose-built extraction with defined rules and blueprints |
| Excel integration | General spreadsheet assistance (formulas, charts, Q&A) | Native plugin with provenance — every cell links to its source document |
| Audit trail | No built-in provenance or traceability | Full audit-ready tracking from source document to spreadsheet cell |
| Accuracy approach | General-purpose model; accuracy varies by document type | Tuned for accounting documents with human-in-the-loop review |
| Reusable workflows | No workflow persistence; every session starts fresh | Blueprint system — define once, reuse across team |
| Data privacy | Consumer plans may train on your data (opt-out required) | Your data is your IP — not used for model training |
| File handling | 20 files per chat, 30 MB each, shared context window | Designed for bulk document processing at accounting scale |
| Best suited for | Ad hoc questions, one-off analysis, general writing | Recurring document workflows, reconciliation, data extraction at volume |
When Should You Use a General AI Tool vs. Rima?
Both types of tools have a role in a modern accounting team's workflow. The question is which tool fits which task.
Use Claude or ChatGPT when:
- You have a one-off question about a single document
- You need help drafting an email or writing a memo
- You want to explore a dataset interactively with questions
- You need a formula explained or written for your spreadsheet
- You are researching a technical accounting standard
Use Rima when:
- You process the same document types (invoices, statements, ERP reports) every week or month
- You need extracted data to go directly into your tools like Excel
- You need every number traceable to its source document for audit purposes
- Multiple team members need to execute the same workflow consistently
- You handle sensitive client financial data that should not be used for AI training
- You are working with high volumes of documents that exceed general AI upload limits
The most effective accounting teams will likely use both — a general AI tool for ad hoc thinking and a specialized tool like Rima for the structured, repeatable document work that consumes the bulk of their time.
Frequently Asked Questions
- Is Rima a replacement for Claude or ChatGPT?
- No. Rima replaces the manual document-to-spreadsheet workflow that consumes hours of your team's time each week. General AI assistants are useful for ad hoc questions and general analysis, but they are not built to handle recurring, auditable accounting workflows at scale. The two serve different purposes.
- Can Claude extract data from invoices into Excel?
- Claude can read a PDF and extract some information, but it does not natively place data into specific cells in your working Excel file with source provenance. You would need to copy and paste results manually, and there is no persistent audit trail linking each value to its source document.
- Does Rima work with QuickBooks, Sage, and other ERPs?
- Rima is designed to work alongside your existing ERP outputs. It handles the document extraction and data formatting step — the work that happens between receiving a document and getting data into your spreadsheet or accounting system — and also performs any analysis before generating outputs that can go back into your ERP.
- Is my data safe with Rima?
- Rima does not use your documents or data to train AI models. Your financial data is treated as your intellectual property. This is built into the product architecture, not a setting you need to find and toggle off.
- How is Rima different from other AI accounting tools like DataSnipper or Trullion?
- DataSnipper is primarily focused on audit firms and evidence management within Excel. Trullion targets enterprise lease accounting and compliance. Rima is built for in-house accounting teams at small and mid-size businesses who need to extract data from messy documents, process and transform the data, and get it into their spreadsheets accurately and repeatably.
- What does "blueprint" mean in Rima?
- A blueprint is a reusable set of instructions that tells Rima how to perform a task for the user. You define it once — what to extract, how to process it, how to output it (Excel, PDF, etc.) — and then anyone on your team can run it on new documents with one click. Think of it as a saved workflow that eliminates the need to re-explain the task every time.
- Why not just use AI to write Excel formulas instead?
- Writing formulas is not the bottleneck for most accounting teams. The bottleneck is getting data from source documents — PDFs, scanned invoices, bank statements, ERP exports — into the spreadsheet and performing the operations there after. Tools like Claude help you work faster inside Excel and are tailored to complex modeling use cases. Rima handles the end-to-end task: getting the data into Excel accurately, processing it, and generating an output that you can review.