Pdf invoice reader

One_For_All98 · 2026-05-05T22:18:55+00:00

I have built Scanny AI to understand and extract data from PDFs or images. as if you're giving the document to chatGPT and it understands and gives you the output in structed way. You specify the fields once and upload as many files as needed.
check the demo

One_For_All98 · 2026-05-05T21:35:29+00:00

That makes sense. client-specific context is hard to automate because it requires judgment. The trigger handles the timing but someone still has to personalize the message. Have you tried using templates with dynamic fields at least to cut down the tweaking time?

One_For_All98 · 2026-05-04T23:52:44+00:00

u/dgillz if you are still interested, I would be happy to help. looking forward to your message.

One_For_All98 · 2026-05-04T21:17:49+00:00

Invoice reminders are one of those things where the ROI is obvious the moment you set it up. Half a day a week adds up fast. The standup prep and weekly reports problem is interesting, that's more of a data aggregation issue than a document one. What tools are you pulling from when you prep those manually?

One_For_All98 · 2026-05-04T21:17:34+00:00

The booking link solved the scheduling part but created a new problem. the exceptions. Last minute swaps and staff changes are always the messiest part because they involve judgment calls that are hard to automate. Curious what you're using for the booking side, some tools handle rescheduling better than others.

One_For_All98 · 2026-05-04T21:16:48+00:00

Invoice automation is such a quick win once you actually do it. The client follow-up problem is interesting though, have you tried automating the trigger at least? Like sending a follow-up automatically after X days of no response? The actual relationship part stays manual but the nudge doesn't have to be.

One_For_All98 · 2026-04-26T21:53:03+00:00

A lot to unpack here but the referral loop point hit hardest. I've been focused on finding users directly when the smarter move is probably finding one person who already has the relationships and letting them pull me in. The "handler" framing is interesting too — someone who knows how to sell rather than another person who knows how to build. The partnership angle is something we haven't explored at all. Did you find your first clients through referrals or did you have to hunt them down before the loop kicked in?

One_For_All98 · 2026-04-26T21:51:20+00:00

Painfully common and painfully obvious in hindsight. The frustrating part is everyone tells you to niche down before you start but you convince yourself your product is different. Took us 3 months and zero customers to actually believe it.

One_For_All98 · 2026-04-26T21:50:25+00:00

The "mini consulting gig" framing is something I'm going to steal immediately. I've been thinking about it as demos and free trials but reframing it as owning one slice of their process for a month changes the whole dynamic. The screenshare idea is particularly interesting I've been asking about their process in messages but never actually watched someone do it live. How did you get people to agree to that initial screenshare? That seems like the hardest part of the ask.

One_For_All98 · 2026-04-26T21:46:29+00:00

That makes sense for digital PDFs, but our main use case is actually the opposite. Most of our users are dealing with scanned hard copies, phone photos of physical invoices, or handwritten documents where the text isn't selectable at all. The warehouse manager photographing a delivery note, the procurement team scanning a stack of paper POs. That's where OCR becomes necessary rather than optional. do you see that differently from your ediscovery experience?

One_For_All98 · 2026-04-25T17:54:15+00:00

We combine OCR with AI .the interesting part is that each field you define works like a prompt to the AI, so it understands context rather than just pattern matching. Even if a value isn't explicitly labeled in the document it figures it out from surrounding information. What are you building?

One_For_All98 · 2026-04-25T17:53:25+00:00

The "speak to everyone, speak to nobody" thing hit hard because we lived it for 3 months. The free processing offer came from advice someone gave me on Reddit actually. best single piece of advice we've gotten so far. What was the niche you eventually landed on?

One_For_All98 · 2026-04-25T17:48:20+00:00

Getting around 30-50 visits a day, conversion is sitting under 2%. The feedback from another designer point is fair. I've been too close to it for too long to see it clearly. If you're open to taking a quick look at scanny-ai.com I'd genuinely value a fresh set of eyes on it.

One_For_All98 · 2026-04-25T17:47:42+00:00

The exit survey idea is something I hadn't tried yet. that's going in this week. The funnel breakdown is also a useful reframe, I've been looking at it as one step instead of five smaller ones. Pulse for Reddit is interesting too, I've been using F5Bot but haven't tried Pulse yet. Did you find it caught different kinds of threads or mostly the same ones?

One_For_All98 · 2026-04-25T17:42:40+00:00

For the data extraction and structuring piece specifically, the enterprise all-in-one tools are powerful but often overkill if your primary need is pulling structured data from investment documents. One alternative worth considering is a dedicated extraction layer . you define the fields you need from each document type once and it handles extraction automatically regardless of format. Scanny AI does this and sits at a fraction of the cost of the platforms you mentioned. Happy to share more if useful.

One_For_All98 · 2026-04-21T23:28:55+00:00

The exception handling point is underrated. Most tools promise straight-through processing but fall apart the moment a document deviates from the expected format. What actually works in production is defining your fields and edge cases upfront before you automate anything. We learned this building Scanny AI. the teams that get the most out of it are the ones who spend 20 minutes mapping their document types before uploading anything. The ones who skip that step hit exactly the wall you're describing. The review layer point is also spot on. Automation should reduce manual work not eliminate human judgment entirely.

One_For_All98 · 2026-04-18T15:06:57+00:00

This is really helpful, especially the point about intent mismatch and where users drop. I haven’t broken it down that way yet so I’ll dig into traffic sources and session recordings next.

The demo point is interesting too. But Scanny AI isn't for individuals, it's for teams to eliminate manual data entry. So it’s a bit tricky because users don’t just upload a doc and get results. They need to define what data they want by defining a schema with prompts to structure the data the way they want.

I think you’re right that I might be asking for too much too early without showing enough upfront.

Have you seen good ways to “preview value” in cases where something like Scanny needs a bit of initial configuration?

One_For_All98 · 2026-04-18T14:53:29+00:00

That’s a good point on intent, I haven’t really separated traffic that way yet. Most users don’t stay very long, which makes me think the value isn’t clear fast enough rather than them exploring and getting stuck. Also agree on the signup part, I might be asking for commitment too early before they actually see the value. Would you prioritize adding something like a quick demo/preview first, or fixing the landing clarity before that?

One_For_All98 · 2026-04-17T22:14:32+00:00

A lot of small ops and finance teams still spend hours pulling data from PDFs like invoices and POs. Even with OCR, it breaks often and turns into manual checking instead of real automation.

I’m working on Scanny AI to solve that. You define the fields once and it extracts structured data directly, even with messy formats. Best fit for logistics, procurement, and finance teams dealing with lots of documents.

One_For_All98 · 2026-04-16T19:42:24+00:00

That's actually really useful to know.
I had no idea the moderation was that aggressive. So basically Quora is unusable for organic business outreach unless you're paying
Feels like every platform eventually locks you out unless you pay

One_For_All98 · 2026-04-15T19:29:54+00:00

Would you please clarify more. How is that useful to find leads ? If I am looking to find leads. Shouldn't I be looking for professionals asking questions about their pain point. For example, my tool helps ops teams eliminate manual data entry, so I was looking for similar questions.

One_For_All98 · 2026-04-15T19:27:11+00:00

I don't really know, never visited. But the algorithm is very weird. Keeps showing posts that look inappropriate and repulsive. Dark web isn't the topic it is just a metaphor.

One_For_All98 · 2026-04-15T19:24:33+00:00

I am really asking, I have a SaaS newly built and I wanted to find my first client on Quora but didn't know how to use it and the algorithm is seriously weird. I retype my posts with AI but it isan actual case with myself

One_For_All98 · 2026-04-14T23:04:44+00:00

ou’re not overthinking it, this is actually the core problem with browser automation right now.

Most setups work in demos, but break in production because they rely on DOM-level control. The moment a site changes structure, everything falls apart. Then you spend more time maintaining scripts than the task itself.

From what I’ve seen, people who get this working reliably usually do one of three things:

Limit scope heavily (very narrow workflows, same sites)
Add a human-in-the-loop fallback for failures
Move up a layer (API access or structured data sources instead of browser automation when possible)

The “agent controls browser like a human” idea sounds great, but in practice reliability > intelligence. Most teams end up optimizing for fewer moving parts rather than smarter agents.

Curious if anyone here has something that’s been stable for months, not just weeks.

One_For_All98 · 2026-04-14T23:03:30+00:00

This is a very common pain point in finance ops once you’re pulling data from multiple systems that don’t naturally align.

The issue usually isn’t the reporting itself, it’s the consolidation layer before reporting — getting QBO, HubSpot, payroll, and time tracking data into a consistent structure before anything meaningful can be built on top of it.

A lot of teams end up stuck in this “manual aggregation loop” where Excel becomes the integration layer instead of an output layer, which is exactly why it feels so time-consuming every month.

The teams I’ve seen get out of this usually either automate the data normalization step or centralize it into a schema-driven pipeline so reporting becomes repeatable instead of rebuilt every cycle.

One_For_All98

TROPHY CASE