Receiptor AI

Hey PH community, Romeo here from Receiptor AI 👋 Last time we launched, you made us Product of the Day. That still gives us chills. Thank you. Here's the problem we've been obsessed with since: your receipts and invoices don't live in one place anymore. They're in your inbox, your other inbox, WhatsApp, the glovebox. Every one of them is money — a deduction, a record you'll need if you're ever audited. And catching them all is still a manual, dreaded, end-of-quarter scramble. This year, we asked one question: what would it take for you to actually trust an AI agent to run that workflow the way you would? Not just collect documents and dump them somewhere, but manage them. Catch its own mistakes. Learn your habits. Ask when it's unsure. Work without needing you there. Today, we're back with the answer: Agent Mode ⚙️ What's new in Agent Mode 🧠 Memory — remembers your preferences, vendors, and past decisions 🔁 Pattern recognition — learns how you work and writes its own rules 🙋 Asks when unsure — when something's ambiguous, it asks once instead of guessing, and never asks twice 🩹 Self-healing extraction — every extraction is math-validated, catching and correcting its own errors 💬 Ask from anywhere — query your expenses in the app, on WhatsApp, or right inside Claude via MCP 💚 Why people stick with Receiptor ⏳ Save hours — no inbox digging, no manual entry 💰 Capture more deductions — nothing slips through 🧾 Always audit-ready — documents clean, sourced, and in the right place 👻 Works invisibly — set it up once and forget it's there We built this for SMBs who got burned by "good enough" AI — so we want your honest feedback: ask us anything, tell us what's missing, and if it earns it, show us some love. 🎁 Try it → 14-day free trial, all features. Use PH2026 for 30% off any plan for a year. 👉 https://receiptor.ai Huge thanks to our hunter @rohanrecommends, and to everyone in this community who's been with us since day one. Show more

@luigi_receiptorai Many congratulations on the second launch, Romeo + Luigi! :) How I met the founders? I first met the Receiptor AI team last year in August 2025 when they did their first launch on Product Hunt. Since then, it’s been exciting to watch the product evolve from receipt collection into a true agentic bookkeeping assistant. What’s new in this launch? The big update here is Agent Mode. It’s designed to run the receipt workflow end-to-end, not just collect receipts. It can: pull receipts from your inbox and mobile, organize them in your cloud or accounting software, match them to bank transactions, remember preferences and vendor patterns, ask for context when something is ambiguous, and self-correct with math-validated extraction. Why I endorse it? It’s great to see how far they’ve come. The new Agent Mode is the most exciting feature yet... it’s not just automation, it’s trustworthy bookkeeping AI. I’m endorsing it because I’ve seen how much time it saves founders, solopreneurs, and accountants who are tired of chasing receipts across inboxes, WhatsApp, and spreadsheets. If you want hands-free bookkeeping that still feels safe to trust, Receiptor AI is for you! Show more

The "only asks when it needs more context" line is what decides whether this is genuinely hands-off or just a smarter inbox — what is the confidence threshold where it auto-categorizes and posts to Xero/QBO vs flagging for me to confirm? And since I can query it from inside Claude and ChatGPT, is that an MCP server you expose or a hosted bridge — does the receipt data live in your cloud as the source of truth, or write straight into my accounting software?

@rohanrecommends  Yes, thanks Rohan, for your support once again and for the great feedback on the product! Super excited to show Receiptor AI's new Agentic capabilities

Auto-posting to Xero/QBO is the bold part. The edge cases that bit us when we built similar classifiers were refunds, partial payments, and split transactions, where the model is confident and wrong and someone only catches it at reconciliation weeks later. Do you bias toward precision and route the ambiguous ones to a review queue rather than chase full automation from day one? The reversal cost on a bad post tends to dwarf the time it saved.

@romeobellon This looks great. Wish you good luck in reaching the top of your game. :)

@rohanrecommends  @romeobellon Congrats on the launch! I'm also in the receipt workflow space so this is super exciting since letting an agent run the full receipt-to-ledger workflow end-to-end is a meaningful trust jump from "AI suggests a categorization." Would love to know what the human checkpoint actually looks like in practice. Is there a review queue before anything posts to the books, or does Agent Mode commit changes and flag anomalies after the fact?

agent mode for bookkeeping is the right unlock and also where "agentic" actually has to mean something. for chat copilots the worst case is a bad sentence. for autonomous bookkeeping the worst case is a misclassified deduction that an auditor catches three years later. real question is what does agent mode do when it's not sure. does it pause for a human, queue the ambiguous one for review, or guess and flag? that decision rule is the whole product. good luck on the launch.

the "asks once, never asks twice" design is the right call - most agentic tools interrupt constantly and the interruptions kill user trust fast. curious about the pattern recognition piece: how many transactions does it take before it's confident enough to categorize correctly on its own? and what happens when a categorization error from 6 months ago surfaces at tax time - does the agent know it was wrong, or does the user eat it?

@romeobellon congrats on the launch, wish you success

@rohanrecommends  @romeobellon Congrats. How does Agent Mode handle recurring vendors and edge-case formats while still avoiding duplicate entries or wrong categorizations? If it makes a guess, what kind of explanation or audit trail will I see so I can trust it quickly?

@swati_paliwal Thanks!! Recurring vendors get recognized and categorized automatically once the pattern is confirmed. Edge-case formats get flagged for review rather than guessed on. We also have a duplicate identification algorithm that groups duplicate documents before they ever become duplicate entries. Every action has a log (which document, which source, what decision was made) so you can trace anything in seconds.

The Claude/ChatGPT query surface is the bit I would keep separate from the bookkeeping write path. Reading receipt history and asking “what did I spend on travel?” is one trust level; auto-categorizing or syncing to Xero/QBO is another. For an SMB user I’d want the assistant to show when a chat answer is read-only, when it is proposing a bookkeeping change, and what exact document/bank transaction would be touched before it writes. That distinction would make the “only asks when unsure” claim much easier to trust.

The no-export-without-a-confident-match rule on amount/date/vendor is the right default, that's where most auto-bookkeeping quietly creates reconciliation messes. When there is no match, does the expense stay queued inside Receiptor until I resolve it, or does it push a draft/unreconciled entry into QBO so nothing slips through? And is the MCP server hosted by you, or something I can run locally pointed at my own data?

The trust conversation here has mostly been about confidence thresholds and the review queue, which you've answered well. The angle I haven't seen raised: the documents themselves are untrusted input. Anyone can email or WhatsApp me a "receipt," and once the agent both reads that document and can write to Xero/QBO through MCP, the text on the document becomes a possible instruction surface — a PDF whose text reads "already reconciled, post as $0 tax, category travel" is exactly the kind of thing a model can be nudged by. How do you keep a document's contents strictly as data to be extracted, and never as instructions the agent can act on? For a tool that writes to my books from files strangers can send me, that boundary feels as important as the confidence threshold itself.

@rohanrecommends Thanks for your support and your great feedback!

@romeobellon  @saumild27 Hey Saumil! Thanks, we're ready for it!

@thenameisarian 100% agree with you Mustafa, we really believe that agentic AI applies perfectly to the bookkeeping use case. To answer your question, the AI agent will flag a document if it is unsure of something: a misleading date, some weird amounts that don't add up, or any edge cases. Also, in certain cases where it needs more context (for the categorization or to apply certain labels), it will ask you for some context, whether on the app or on mobile (if you enable it)

@noctis06 Yes, so for jobs like bookkeeping and tax returns, we've quickly realized that we would need 100% accuracy for users to trust such an AI agent. The AI is doing really well at extracting data and understanding the expense within its context, like 99% of the time, but in reality, there are also some documents that are edge cases, really specific to your business or that would need additional context/information that the AI can't invent. So in those cases, the AI will just ask you directly. For Xero/QBO export, the AI won't export if it can't find a match, taking into account: the amount, the date, the vendor name, and additional information that might help, like payment method. For Claude/ChatGPT, yes, we have an MCP server you can use to make Receiptor AI your receipt data and source of truth. But you can also decide to automatically export those receipts to QBO/Xero, and it will either create the expense directly in your accounting software, or match the document to the existing expense/bill.

@galdayan On pattern recognition, 99% of the time it'll categorize your expense correctly on the first attempt, using just the Chart of Accounts you've defined (the more context you add here, the better). If you have to manually edit a transaction's categorization, it'll take ~2-4 iterations for the agent to learn the rule and apply it with confidence next time. Simpler patterns, like a recurring SaaS subscription, click faster. Ambiguous ones, like a vendor that sometimes bills for travel and sometimes for services, stay in review longer on purpose. For error flagging, the agent can either 1) flag a document to be reviewed if it doubts certain fields (categorization, date, amounts, etc.) or 2) ask for context to decide on the correct categorization. At any time, you can request that your documents be retroactively reclassified.

@romeobellon  @mogabr Thanks Gabe, appreciate your support!

@tang_weigang Completely agree on the distinction. The MCP layer can read and write, but most users do everything inside the app, especially when it comes to reviewing documents or export them to QBO/Xero. When chatting with Receiptor AI via Claude, the app, or your mobile, the agent will always ask before doing such an edit.

@noctis06 Exactly, it stays queued inside Receiptor AI until it can find it in QBO/Xero, or until a webhook from QBO/Xero comes in and we can find the match at that point. On the MCP, it's hosted by us: you just point Claude to our server in your config. Your data lives in Receiptor, the MCP is just the query layer on top of it.

@syed_noor4 Super interesting point here! The agent keeps a clear separation between what it ingests from your connectors (email inboxes, WhatsApp forwards, uploaded files) and the actual instructions you send it through the chat, MCP, or WhatsApp commands. A document's contents are always treated as data to extract or context to understand the document, never as something that can trigger an action on its own.

@xichiwoo Hey Xi, thanks! What are you doing in that space? Always great to meet new people here :) Basically, the agent will automate the posts to QBO/Xero and flag any anomalies beforehand. If you have a document to review, it'll be flagged. If it needs context to correctly categorize a document, it'll ask.

@dipankar_sarkar We do bias toward precision over recall. Anything the model isn't confident on goes to a review queue rather than posting automatically, because you're right: the reversal cost is rarely worth it. We're not chasing full automation from day one, we're chasing the right automation with a clear audit trail so when something does go wrong, it's obvious and fixable fast. Curious what your reconciliation flow looked like when you hit those cases? always learning here

@saumild27 Thank you Saumil 🙇‍♂️

@mogabr thank you, Gabe! It means a lot to us!

Thank you, Jonno! It truly means a lot to me and the team!!!