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🟢 I built an AI agent that reads invoices so humans don't have to. Manual invoice entry is slow, boring, and full of typos. So I automated it end to end in n8n.⚫
Here's how it works:
🟢 Invoice comes in: A webhook receives the PDF through a POST request
⚫ Text extraction: The file is parsed and the text pulled out
🟢 Safety check: If the file is invalid, the system responds with an error right away
⚫ Smart chunking: Large invoices are split into smaller pieces so nothing gets cut off or lost
🟢 AI extraction: A loop sends each chunk to an Information Extractor powered by Anthropics Claude, which pulls out the fields the client needs (vendor, invoice number, dates, line items, totals)
⚫ Merge and validate: The results are combined into one record and checked before they are sent
🟢 Clean JSON response: The structured data goes straight back through the webhook The hardest part was'nt the AI. It was making it production-ready. 💡
Long, multi-page invoices break most simple AI workflows because they exceed the models limits. Chunking, looping, merging, and validating are what let this handle large invoices reliably. Where this fits in the real world:
⚫ Accounting and finance teams processing hundreds of vendor invoices
🟢 Businesses syncing invoice data into ERPs or spreadsheets
⚫ Freelancers and agencies automating their billing workflows What used to take minutes per invoice now takes seconds, with no copy-pasting. ⚡ I'm currently going deeper into n8n and AI automation.
If you are looking to automate document processing or any repetitive workflow, lets connect! 🤝
#n8n #Automation #AIAutomation #InvoiceProcessing #WorkflowAutomation #Claude #FullStackDeveloper #NoCode
For the chunk-and-merge step, I'd keep page and row references on each extracted line item so overlapping chunks don't create duplicate charges. How do you handle a line item that starts in one chunk and continues in the next before validating the total?
Great build! I also automate invoices with n8n + VAPI — would love to connect!
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👋 I'm new to Contra and I did my first post without realizing I hadn't even introduced myself!
Hey I'm Katya, I'm an AI Systems Architect, marketer and yoga teacher.
I build AI systems that bring in business and handle the work behind it. A voice agent that calls new leads back within seconds and books the appointment. A pipeline that traced 16,900+ leads to the ads that brought them. Listing videos made from the photos a real estate agent already has.
The marketing side came first. I built the multi-channel campaign for Jay Shetty's launch (22 million followers). After that I discovered the tech world and when AI came along, I realized I'm a builder at heart and never looked back! Building is so much fun 🤍
Here's what I've shared:
My first post: on building a fully AI-made YouTube video, end to end, because I'm camera shy 🙈 https://contra.com/community/MP9mFRuT-im-building-my-first-fully-ai-made
Last thing, if you're building with AI video or automation, I'd like to see what you're working on
And if you're hiring: I may be new to Contra but definitely not new to this work. Reach out for AI agent builds, apps, websites or anything in between.
P.s The video below is my AI avatar. My profile video is the real me :)
Nice Portrait....Has everything from a great pose, great composition and lighting and perfect environment setting.
A system I designed for local service businesses that consistently lose out on Google reviews. Full build in progress
Automated Review Requests for Local Service Businesses
The Problem:
Google reviews are one of the strongest signals for local search ranking, this is well established across HVAC companies, cleaning services, salons, contractors, and landscaping businesses. Every owner in this space knows reviews matter. Very few of them ask for one consistently.
The reason isn't a lack of care, it's a lack of a system. Asking every customer, right after every job, every single time, is a small task with no natural trigger behind it. It depends entirely on someone remembering in the moment, usually right when the business is busiest and least likely to remember. The result is a steady stream of satisfied customers who were never actually asked, and a review count that grows far slower than it should.
The Solution👍
I designed a system that removes the dependency on memory entirely, so a review request goes out every time, without anyone having to think about it.
The moment a job is marked complete in the CRM, the system picks it up automatically. It waits a short period first, so the request doesn't feel rushed or robotic, the customer gets a little time to settle in before being asked. Then it sends a review request by SMS or email.
From there, the system tracks what happens next. If the customer clicks through and leaves a review, that's logged as a win and the process ends there, no further messages, no risk of annoying someone who already did what was asked. If there's no response after a few days, the system sends exactly one follow-up, never more than that, since a second or third nudge tends to do more harm than good. Whatever the outcome, sent, clicked, or reviewed, it's recorded in a tracking sheet, so the business owner always has a clear, accurate picture of where every customer stands.
The result is simple: every completed job gets a consistent, well-timed ask, with no manual list to maintain and no customer slipping through the cracks.
What's Next
The full workflow build, including the architecture and the tools behind it, is going up in the next few days.
That email trigger right after a job finishes should cut out the manual reminder step and keep reviews coming in.
Super clean work!