Document-to-spreadsheet extraction and expense stewardship console

For

Finance and accounting teams extracting transaction and expense data from mixed documents

Solves

Structured financial data is trapped in PDFs, scans, screenshots and handwritten tables, forcing manual re-entry and reconciliation.

Delivers

Reviewer-approved structured rows linked to source evidence

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual data entry while preserving an auditable trail from source document to exported row.

1. Ingest PDFs, images, screenshots and scanned documents.
2. Run AI-powered OCR to convert text and tables into digital formats.
3. Recognize handwritten and sketched tables.
4. Extract receipt and invoice data automatically.
5. Extract transaction rows from bank statement PDFs.
6. Support any bank, format and language, including mixed layouts and foreign-currency rows.
7. Apply custom extraction hints for pages, tables and calculations.
8. Separate transaction and posting dates and preserve multi-line descriptions.
9. Sanitize currency formatting.
10. Track and categorize expenses in real time.
11. Generate customizable cash-flow and expense reports.
12. Export to Excel, CSV, JSON or clipboard.
13. Provide browser preview with quick-copy cells.
14. Offer a privacy mode toggle per import.
15. Store data securely in the cloud with access across devices.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned reviewer-approved structured rows linked to source evidence with source references and unresolved questions.

Everything these tools do, in one app

• Multi-format data extraction Extracts structured data from various document types such as PDFs, images, and screenshots.Found in Tablextract, LedgerBox, BankStatementLab
• AI-powered OCR Uses AI to recognize and convert text and tables from images or scanned documents into digital formats.Found in Tablextract, LedgerBox, BankStatementLab
• Export to multiple formats Allows exporting extracted data to Excel, CSV, JSON, or clipboard for use in other applications.Found in Tablextract, BankStatementLab
• Custom extraction hints Enables users to provide specific instructions to target particular pages, tables, or perform calculations.Found in Tablextract
• Handwritten table recognition Converts handwritten or sketched tables into digital formats accurately.Found in Tablextract
• Quick extraction workflow Extracts tables in under three clicks—upload, extract, and export.Found in Tablextract
• Automated receipt/invoice extraction Automatically extracts data from receipts and invoices using AI recognition.Found in LedgerBox
• Real-time expense tracking Tracks and categorizes expenses in real time for better financial oversight.Found in LedgerBox
• Accounting software integration Integrates with popular accounting software and bank accounts to streamline workflows.Found in LedgerBox
• Customizable reports Generates customizable reports to analyze cash flow and expenses.Found in LedgerBox
• Secure cloud storage Stores data securely in the cloud, ensuring safety and easy access across devices.Found in LedgerBox
• Bank statement transaction extraction Extracts transaction rows from bank statement PDFs into structured outputs.Found in BankStatementLab
• Multi-bank/language support Supports any bank, format, and language, including mixed layouts and foreign-currency rows.Found in BankStatementLab
• Browser preview with quick-copy Provides a preview table in the browser with quick-copy cell capability.Found in BankStatementLab
• Privacy mode toggle Offers a privacy mode toggle for per-import data handling.Found in BankStatementLab
• Advanced date parsing Separates transaction and posting dates, preserves multi-line descriptions, and sanitizes currency formatting.Found in BankStatementLab
• Free starter option Includes a free starter option with limited pages to test the service.Found in BankStatementLab

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed source documents, extraction hints and accounting rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed source documents

4. 3

   Extraction hints and accounting rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewer-approved structured rows linked to source evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed document set and licensed accounting rules; final reconciliation and posting checks remain accounting. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Document intake and extraction hints, Editable extraction preview, Export and stewardship log. Use a thumbnail gallery for documents, a large central table canvas, and a right-hand panel for source evidence, extraction hints and comments. Let users compare extracted rows against source images side by side. Display draft, changes requested and approved states. Provide a browser preview with quick-copy cells and a privacy mode toggle per import. Make the task-specific outcome reviewer-approved structured rows linked to source evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, document versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Accounting software and bank accounts for streamlined workflows. Cloud document storage, spreadsheet import/export and accounting destinations. Start with file exchange and validate destination specifications before promising direct posting. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: ingest PDFs, images, screenshots and scanned documents; run AI-powered OCR to convert text and tables into digital formats. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will finance and accounting teams extracting transaction and expense data from mixed documents use it to solve "structured financial data is trapped in PDFs, scans, screenshots and handwritten tables, forcing manual re-entry and reconciliation"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted rows per reviewer hour and corrections after export.
4. Measure, then decide. Track accepted rows per reviewer hour and corrections after export; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed document set and licensed accounting rules; final reconciliation and posting checks remain accounting. Implement one approved input format, a bounded representative case set and the first two task modules: ingest PDFs, images, screenshots and scanned documents; run AI-powered OCR to convert text and tables into digital formats. Support the third module with operator review: recognize handwritten and sketched tables. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved structured rows linked to source evidence. Retain the explicit scope boundary: One fixed document set and licensed accounting rules; final reconciliation and posting checks remain accounting.

What the build depends on. Document upload and preview, asynchronous extraction jobs, editable version history, reviewer access and tested export formats. High-fidelity extraction requires specialist accounting QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed document set and licensed accounting rules; final reconciliation and posting checks remain accounting.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined document package. Offer a monthly extraction allowance after repeat demand. Quote complex multi-bank, multi-language or handwritten-table work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved structured rows linked to source evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual data entry while preserving an auditable trail from source document to exported row. Demonstrate a concrete reviewer-approved structured rows linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Finance and accounting teams extracting transaction and expense data from mixed documents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved structured rows linked to source evidence from a small authorized input set, with a transparent calculation of accepted rows per reviewer hour and corrections after export and no promised savings.

The first 30 days

1. Week 1: interview five finance and accounting teams extracting transaction and expense data from mixed documents and inspect a recent example of structured financial data trapped in PDFs, scans, screenshots and handwritten tables, forcing manual re-entry and reconciliation.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted rows per reviewer hour and corrections after export, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted rows per reviewer hour and corrections after export. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted rows per reviewer hour and corrections after export; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved structured rows linked to source evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved extraction hints, accounting rules and review examples, together with reliable delivery for a narrow finance niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for finance and accounting teams extracting transaction and expense data from mixed documents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Tablextract, LedgerBox, BankStatementLab, manual spreadsheet entry and generic OCR tools. Compare this product with the buyer's present method on accepted rows per reviewer hour and corrections after export. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

OCR and model attempts, storage, reviewer hours, client revision rounds and licensed source documents. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved structured rows linked to source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, transaction accuracy and usage permissions. Accountants approve substantive changes and posting scope. One fixed document set and licensed accounting rules; final reconciliation and posting checks remain accounting. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.