Docuct.ai
AI-powered document extraction with human validation
Spent hours manually extracting data from documents? We felt that pain too.
So we built Docuct AI — a smarter way to convert unstructured documents into structured data in seconds.
✨ What it does:
• Extract data from PDFs, images, forms, screenshots
• Auto-structure into fields & tables
• Edit, validate, and refine with Human-in-the-Loop
• Export instantly to CSV / JSON / Excel
💡 Why it matters:
80% AI ⚡ + 20% Human 🧠 = 100% reliable data
No more manual entry. No more messy data. Just clean, structured output — fast.
👉 Try it here: https://docuct.ai
Try It → (Free Trail Available)
#indiehackers #saas #ai #automation #productivity #startups #founders #nocode #documentai #workflowautomation
80% AI + 20% Human = 0 Errors
Most teams still spend hours manually entering data from documents.
We built Docuct AI to change that.
It converts unstructured documents into structured data (CSV, JSON, Excel) in seconds — while still giving you full control.
👉 Add tables, rows, columns
👉 Define custom fields based on your workflow
👉 Edit and enter data easily
👉 Human-in-the-Loop to review and ensure accuracy
The idea is simple:
Let AI handle 80% of the work
Humans perfect the critical 20%
→ Result: faster workflows with near-zero errors
💡 What used to take 20 minutes now takes seconds.
We’re building this for teams in healthcare, logistics, finance, and more.
Would love your feedback 🙌
👉 https://docuct.ai
Try It → (Free Trail Available)
#indiehackers #DocumentAI #IntelligentDocumentProcessing #HumanInTheLoop #Automation #AI #SaaS #Productivity #DataAutomation #Startup
-
1
This ratio feels right — and it matches what we've seen running autonomous AI agents in production for the past 10+ months.
The 20% human layer isn't just for catching errors either. It's where the agent learns what "good" actually looks like in context. Without that feedback loop, error rates stay flat even as task volume scales.
The hardest part is figuring out *which* 20% to keep human. Routing that decision correctly is its own unsolved problem.
-
1
Apologies for the delayed response.
I completely agree with your perspective.
The human-in-the-loop layer plays a crucial role in defining quality, not just correcting errors.
Without that feedback loop, systems tend to scale inaccuracies rather than improve.
Identifying the right 20% for human involvement is indeed the most challenging part. -
1
This comment was deleted 6 months ago
-
90% of document extraction tools get you 90% of the way there.
But the last 10% often means hours of manual corrections, missed fields, and frustrated teams.
That’s exactly why Docuct.ai was built differently.
Most AI extraction tools treat documents as a “set it and forget it” problem. But real-world invoices, contracts, and financial documents rarely follow perfect layouts.
AI alone isn’t enough. Human judgment still matters.
Docuct combines Vision Language Models with a powerful Human-in-the-Loop workflow designed for real teams.
→ AI understands document layout, context, and relationships between fields to extract structured data.
→ Smart review queues highlight only the documents that actually need human attention.
→ Action items can be assigned based on confidence scores, document type, and team roles.
→ Reviewers can edit extracted values, add fields, modify tables, approve, flag, or route documents — all in one place.
The result:
Work that once took hours of manual effort can now be completed in minutes, while humans stay fully in control of the final data.
We’re not replacing your team.
We’re giving them better tools.
Try it → docuct.ai
Try It → (Free Trail Available)
#DocumentAI #IntelligentDocumentProcessing #HumanInTheLoop #VisionAI #Automation #AI #SaaS #Fintech #Productivity #docuct
Comment