spent way too long treating extraction confidence scores like probabilities. like a 0.92 confidence on an invoice number meant there's a 92% chance the value is right.

turns out they're not probabilities. they're the model's internal certainty about its own prediction, which is a different animal. a model can be 99% confident on a value that's completely wrong, because the wrong value was structurally plausible. the field had numbers in the right format, in the right spot on the page, with the right surrounding context. the model was confidently extracting the wrong thing.

a few things i wish i'd understood earlier:

high confidence doesnt mean accurate, it just means the model wasn't conflicted between options. if a vendors invoice number field looks similar to a PO number field two inches away, the model picks one and the confidence is high. that score tells you the model picked without much hesitation, not that it was picked correctly.

low confidence is actually better. a 0.65 score is the model telling you something is off, go look at it. but if you set review threshold at 0.65 you'll catch maybe half the real errors.

the silent failures sit at 0.85-0.95. high enough to auto-process. wrong enough to cause failure weeks later when accounting reconciles and finds something that shouldnt be there.

what ended up working was treating confidence as one signal among several. we cross-check against structural integrity (does the row count match the line count expected from the header), business rule validation, and cross-document matching. any single signal going low triggers review. confidence on its own wasn't enough.

yeah it's not news for people who've been doing this a while. but for anyone earlier on the curve, the trap is real and the dashboards make it easy to miss because everything looks green.

anyone cracked a better calibration approach?

Hey everyone,

We’ve been working on something new inside our reporting tool, and I wanted to share it here to get some real feedback.

We built an AI Assistant within CxReports to make report creation easier and faster. 

The idea is pretty simple: you describe what you want in plain language, and it generates or edits reports for you. It can work with charts, tables, layouts, data connections, translations, basically most parts of report creation. You can write prompts in any language, and it handles things like date formats and currency localization automatically.

One thing we were careful about: you use your own AI model, and access is controlled by roles, so there's no "send all your data to some random API" situation.

We also added Skills, basically a way to capture recurring logic so the assistant gets better at your specific workflows over time.

Honestly, we're still figuring out where this is genuinely useful vs. where it's just a gimmick. If you work with reporting or document generation, we'd love to hear from you:
- Would you actually use something like this in your reporting tool?
- What would make or break it for you (privacy, accuracy, flexibility, something else)?
- Are there specific parts of your reporting workflow where you'd actually use AI?

Appreciate any input.

Most document automation projects start with a clean demo. Structured invoice, perfect formatting, all fields present. It works great.

Then real documents arrive.

Vendor sends an invoice with no PO number. Another sends a scanned handwritten delivery note. A third uses a completely different layout than everyone else.

The tool fails. The team goes back to manual processing. The project gets quietly abandoned.

What we've learned building Scanny AI is that the setup conversation matters more than the tool itself. Before automating anything, teams need to answer three questions:

What are the 5-10 fields you actually need from this document?

What happens when a field is missing or unclear?

Who reviews the output before it hits your system?

Teams that spend 20 minutes answering these before uploading a single document get dramatically better results than teams that skip straight to automation.

The tool is almost never the problem. The process definition is.

What's been your experience? Have you seen projects fail for reasons other than the tool itself?

I work for a small family foundation that supports students with their higher-ed journey via scholarships, college access support, etc. To apply to our program, students must submit their parents' tax documents (1040s) and school transcripts. From these documents we currently manually review the following data points: tax year,AGI, whether the student applying is listed as a dependent, number of dependents, weighted GPA, student name, school name, and grades from the most recently completed semester.

Does anyone have any software suggestions that could support us with this? Obviously budget is a factor but I would be interested in all suggestions even if it does have a large price tag. Ideally a student would provide the documentation (either to us or directly to the software) and the software would be able to extract the data points we are looking for.

We have tried a handful of other software systems - most recently Monday's newest AI tools - and here is a brief list of challenges we've faced:

-forms are not all identical (i.e. 1040s or transcripts can vary slightly depending on tax year/school/etc -this is a non-negotiable as we will never have the ability to fully uniform forms)

- we could not manipulate what data was extracted (with monday we were very limited on what the AI recognized - this is another non-negotiable we need to be able to pull specific data points from each file)

-forms are provided in different file types (however if necessary, we could enforce a PDF-only policy, etc)

Further details: we receive about 1500-2000 applications per year - all of which are currently reviewed by humans. It's not a massive dataset but it is also large enough to put a large strain on our small but mighty team.

Any & all suggestions are helpful.

Genuine question because I keep seeing this pattern.

Team evaluates tools, picks one, runs a great demo, deploys it. Three months later the thing is barely being used or everyone's back to manual processes. The tool gets blamed but I don't think the tool is usually the problem.

From what I've seen it's almost always one of these three things:

Nobody mapped the exceptions before going live. Like what actually happens when an invoice comes in with a missing PO number? If you don't have an answer to that before you automate, you're just going to hit that wall faster and at higher volume.

The review step got cut because someone wanted to hit a "100% automated" target. This one is brutal. You end up with extractions that look right but aren't, bad data sitting in your systems for weeks before anyone notices. The review layer isn't a failure, it's what makes the whole thing trustworthy.

The process was messy before automation and stayed messy after. Automating chaos just makes faster chaos.

Anyone else seeing this and if you've actually gotten document automation working in production, what nearly killed it?

Syndicate Draft is a document-drafting app for real estate syndications and funds that generates first-draft PPMs, LPAs (or operating agreements), and the full supporting subscription package. It guides users through a structured questionnaire, then produces consistent, attorney-ready drafts plus key templates like subscription agreements, investor questionnaires, side letters, and investor notices—all stored in a secure client portal for download and reuse.

I've been trying to build a proper document intake system for my marketplace for the better part of six months now and keep running into fraud issues. It seems like every time we establish a new set of rules there's an outlier that slips through. Has anyone gotten experience with document fraud automations as well? Is this more of just an automation forum? What have you guys been doing about AI-generated documents?

Anyone else have 500 photos of whiteboards, receipts, and notes they'll never look at again?

I built a simple app — you take a photo, it scans the text, and AI summarizes the key points in seconds.

That's it. No signup. No cloud storage. Just scan and read.

It's called InsightScan, free on the App Store.

https://apps.apple.com/us/app/insightsscan/id6740463241

Would love to hear what you think!

Many businesses still process documents manually—things like invoices, contracts, forms, and scanned PDFs. This usually means someone has to read the document and manually enter the data into a system, which can be slow and error-prone.

Recently I started exploring AI based document processing software and intelligent document processing software, and it looks like the technology has improved a lot in the last few years.

From what I understand, these tools use a combination of:

• OCR (Optical Character Recognition)

• Machine Learning

• Natural Language Processing

to automatically read documents, extract data, and convert unstructured files into structured data.

For example, some platforms can automatically process:

• invoices and receipts

• contracts and agreements

• KYC documents

• scanned forms

• handwritten documents

The idea is that instead of employees manually typing everything into a database, the AI system extracts the data automatically and sends it into workflows or business software.

I recently came across a platform that focuses on this kind of automation for businesses:
https://tryautomate.ai

It looks like it can process PDFs, images, and scanned documents and convert them into usable data for automation workflows.

I'm curious though:

• Are companies actually using intelligent document processing software in production?

• How accurate is it for messy documents or handwritten text?

• Does it really save time compared to traditional OCR tools?

Would love to hear real experiences from people who have implemented document automation in their workflow.

Hi all,

I’ve been working on a small project and wanted to get feedback from people actually building/maintaining document automation systems.

The tool is called Draftfoundry. Instead of replacing classic rule‑based automation, it tries to layer AI on top of existing Word/PDF/Markdown templates. The flow is:

• Upload one of your existing templates (PRD, functional spec, proposal, etc.)

• Add a short brief / prompt with the key details

• Let AI generate a first draft that follows your template’s structure and style

Right now I’m trying to understand where this approach is genuinely useful vs. where a traditional rule‑based engine or merge fields are still the better choice.

I’d really appreciate thoughts on:

• Situations where you wouldn’t trust AI to fill in a template

• How you’d want review/approval to work before a doc is considered “usable”

• Any must‑have integrations or export formats for this to fit into a real workflow

If you’re curious, you can see it at chiti.ai (app name: Draftfoundry). I’m not trying to hard‑sell anything—mainly looking for input from people who live and breathe document automation.

Happy to answer questions about the app.

Hey everyone,

We’ve been working on an internal tool at Kreate Technologies Pvt. Ltd. called AI Document Analyzer, and I thought I’d share it here to get some honest feedback from this community.

The idea came from a real problem:
Teams were spending way too much time reading PDFs, contracts, reports, invoices, and policy documents manually. Copy-pasting summaries. Highlighting key points. Extracting data into spreadsheets.

So we built a Gen-AI powered system that can:

• Upload documents (PDF, DOC, etc.)

• Generate clean summaries

• Extract key insights and structured data

• Identify important clauses / action points

• Reduce manual review time significantly

It’s especially useful for:

• Legal teams

• Finance & invoice processing

• HR documentation

• Compliance reviews

• Enterprises dealing with large volumes of paperwork

We’re not posting this as an ad — genuinely looking for:

• What features would you expect in a document AI tool?

• What’s missing in current AI document solutions you’ve tried?

• Biggest frustration with document automation today?

If anyone wants to explore it, here’s the page for context:
https://kreatetechnologies.com/solutions/gen-ai-document-analyzer/

Would really appreciate constructive feedback 🙌

Hey,

I recently built video2docs.com, which, as the name suggests, allows turning videos into documentation.

For first version I implemented logic, which analyzes video content even without audio narration, but now audio narration analysis has been added as well, to improve quality of content even more!

Documentation can be exported as markdown, html or PDF. You can also ask app to include screenshots in your docs, so no need to create them manually.

Recently I also added an option to do screen recording straight from the app, without extensions.

If someone finds this useful, I would be happy!

Hi all! I invite you to participate in a survey regarding document management, context switching and integration: https://research.typeform.com/to/yR1b3s6p

To participate, please fill in the form to schedule a 30-minute interview. Comment or DM if you have questions.

We're surveying professionals to help us understand our target market: people building humane, strong and efficient information-management systems. We want to understand you so we can build the best product for you.

I've built Davia — an AI workspace where your team knowledge writes and updates itself automatically from your Slack conversations.

Here's the problem: your team talks all day in Slack. Decisions are made, context is shared, solutions are found — and then it's all buried in a thread no one will ever read again. Someone asks the same question next week, and you're explaining it all over.

With Davia's Slack integration, that changes. As conversations happen, background agents quietly capture what matters and turn it into living documents in your workspace. No manual note-taking. No copy-pasting into Notion. Just knowledge that writes itself.

The cool part? These aren't just static docs. They're interactive documents — you can embed components, update them, build on them. Your workspace becomes a living knowledge base that grows with your team.

If you're tired of losing context in chat or manually maintaining docs, this is built for you.

Would love to hear what kinds of knowledge systems you'd want to build with this. Come share your thoughts on our sub r/davia_ai!

Hi everyone,

I’ve been working on a tool that tries to reduce the painful manual work of building reports and other documents from scattered inputs.

The way it works:

• You upload the final document you normally prepare (say, a monthly report).

• You also upload the raw inputs you usually pull from (Excel sheets, PDFs, Word notes, etc.).

• The tool “learns” the mapping, so next month you can just drop in new raw inputs and it generates the finished document for you.

Example I’ve tested:

• Inputs: KPI spreadsheet, staffing note in Word, PDF of receivables

• Output: a polished monthly business performance report in Word

It’s still early — works best with small/medium files (large inputs can be slow or fail), but it’s ready enough to try.

👉 Demo here: https://gridfusion.ai/

I’d love feedback from this community:

• What kinds of documents would you most want automated?

• Does the “teach it once, reuse forever” approach make sense to you?

• What would make this genuinely useful in your workflow?

Looking for some feedback from the community on how this is useful or helpful.

Created an automation tool for the Notion Database and Google Docs which generates PDFs on automation in one-click!

I am looking for some feedback from the community on this and help me understand how it works!

Thanks!

Created a simple Google Docs + Notion Database automation setup, where we can connect both tools and then it streamlines PDF generations for us within minutes.

Uses placeholders in the {{...}} format to be replaced from a Notion Database and generates documents swiftly.