# Build brief — a focused alternative to ScanToExcel > **Verdict:** Partly, if you narrow it · **Buildability:** 58/100 · **Category:** Documents > **Source:** https://www.canitbevibecoded.com/scantoexcel > Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from ScanToExcel. Verify current pricing and capabilities before acting. ## Context **ScanToExcel** — Photograph a table or form and get a real .xlsx back, handwriting included. It currently costs $39.99/mo. A vision model will read a clean printed table on the first try, and that demo is genuinely an afternoon of work. Consistency is the part that is not. Real documents arrive with merged cells, multi-line rows, columns that shift between pages and numbers that must survive as numbers, and a one-shot prompt handles each of those differently every time you run it. ScanToExcel puts a purpose-built extraction pipeline between the model and the spreadsheet precisely because the model alone is not reproducible. You can copy the easy half of this product in a sitting and spend months on the half that makes it trustworthy. This brief describes a focused, single-operator replacement for the part of ScanToExcel that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest. ## What you are building Upload a photo or PDF page, send it to a vision model asking for the table as structured rows, and write the result to an .xlsx file. - Create, edit, and organize structured documents with templates and clean exports. - A responsive interface with real empty, loading, success, and error states. ## Requirements ### Functional - Node.js 22. - A spreadsheet writer such as SheetJS or ExcelJS. ### Data and integrations - OpenAI or Anthropic API key with vision support, in .env. Each of these needs a real account, credential, or quota. Set them up before writing feature code. ### Non-functional - Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support. - Security: server-side secrets, validated input, and no credentials in the client bundle. - Reliability: retries with backoff on external calls, and a clear failure state when a provider is down. - Portability: the operator can export their data and leave without losing it. ## Implementation brief Build a document-to-spreadsheet converter inspired by ScanToExcel. Use exactly this stack: Next.js 15 + TypeScript. Primary job: the user uploads a photo or a PDF page containing a table, and gets back a downloadable .xlsx whose cells match the document. Start from an empty folder and create the complete working project. Send the image to one vision-capable model and ask for the table as structured rows and columns, not as prose. Write the result with a spreadsheet library so numbers arrive as numbers and dates as dates, not as text. Show the extracted table in the browser for review and let the user fix cells before downloading - never hand over a file the user has not seen. Handle multi-page PDFs by processing pages in order and dropping a repeated header row when the same header appears on a later page. Single-user and private by default; delete uploads once the download is produced, and say so in the UI. Put every secret in .env and provide .env.example. Include clear empty, loading, validation, success and failure states, and show the model's confidence when it reports one. Accessible keyboard navigation, labels, focus states and sensible contrast. Deliberately exclude these paid-product advantages: a native mobile capture app, tuned handwriting recognition, batch processing of very long documents. State plainly in the README that handwriting and low-quality photos are where this build degrades. Write unit tests for the row-parsing logic and one end-to-end smoke test that converts a sample image to a valid .xlsx. Create a README with setup, architecture, per-page cost estimate and limitations. Run the tests and build before finishing, then fix what fails. ## Delivery standard - Inspect the repository first, then write a short implementation plan before writing code. - Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data. - Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations. - Include responsive layouts plus genuine empty, loading, success, validation, and failure states. - Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data. - Add structured logs around every external call and return actionable errors without leaking sensitive details. - Write unit tests for the core logic and one automated test of the main user journey. - Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations. ## Acceptance criteria - [ ] A clean install starts the app using only the README and .env.example. - [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable. - [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state. - [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control. - [ ] Tests, type checking, linting, and a production build all pass with no ignored failures. - [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified. ## Non-goals Do not build these, and do not claim to have replaced them: - A purpose-built extraction pipeline rather than raw model output. - The same document producing the same spreadsheet twice. - Structure held across pages: merged cells, multi-line rows, shifting columns. - Reliable handwriting recognition. - A phone app that captures and converts without a laptop. ## What you still own after launch - Secure credentials, rotate secrets, and handle provider rate limits. - Run migrations, backups, restores, and dependency updates. - Test the critical journey after every model, API, or hosting change. - Monitor failures and fix the edge cases a first prompt will miss. ## Risk **Manageable.** A personal version is realistic if you test the critical journey and keep reliable backups. Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet. ## Prior art Working open-source software you can read, fork, or borrow from before starting: - [img2table](https://github.com/xavctn/img2table) — Table identification and extraction from images and PDFs, no model API required - [Camelot](https://github.com/camelot-dev/camelot) — Extracts tables from text-based PDFs into DataFrames - [docling](https://github.com/docling-project/docling) — Document parsing to structured formats, including table structure recovery --- Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/scantoexcel