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AI Bookkeeping Assistant

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AI Bookkeeping Assistant

Hi HN, I've built a SaaS to solve a data entry task that I have often had as a business owner - to be able to extract key invoice data from batches of invoice files for financial record keeping. From my experience GPT-4o now outperforms traditional OCR for extracting text from documents, plus the LLM's reasoning ability means that specific data can be extracted and formats can be converted as desired. Traditional OCR services generally extract all text in the document and of course they extract it as-is without the ability to convert dates etc. Accounting software usually expects imported data to be in a specific format. The tool can extract data from batches of mixed invoice types (PDF, Word, JPG, PNG) which is particularly useful for 'bricks and mortar' businesses who often have mixed format invoices such as scanned documents or photos of receipts. The data is extracted to a spreadsheet with columns that the user defines ready for the user's workflow or for importing into their accounting software. The tool optimizes the document contents for best LLM understanding - which isn't necessarily what happens when you upload the documents to ChatGPT for example. I've tried to make the software as easy to use as possible, so hopefully there isn't a learning curve involved - it is just completing our template by using natural language to describe the data you need extracting. Again, with traditional (non LLM) OCR tools there is usually a learning curve involved in using the software, a long set-up process, and a rigid invoice format expected. If there are any business owners, bookkeepers or accountants who think the tool might be useful for them then I'm extremely open to working with you as an early user to onboard you with additional free credits so you can test out the software. I've tried to keep this brief - it would be great to hear feedback or hear from anyone who thinks this might be useful for them! Best regards, David.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, chatgpt, using · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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