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🤖 Built an AI Agent with n8n to automate student record management
I built a simple AI-powered workflow that turns a chat message into a complete student record update, without manual data entry or repetitive admin work.
How it works:
→ Receives a student update through chat
→ AI Agent understands the request using OpenAI + Memory
→ Finds and updates the relevant record in Google Sheets
→ Sends an automatic confirmation email through Gmail
The goal was simple: replace a multi-step manual process with one conversational command.
This project is a good example of how AI Agents can go beyond answering questions, they can actually understand requests, interact with business data, and execute actions automatically.
Built with n8n + OpenAI + Google Sheets + Gmail.
Always experimenting with practical AI automation workflows that can save businesses time and reduce repetitive work. 🚀
#n8n #AIAutomation #AIAgents #WorkflowAutomation #OpenAI #Automation #NoCode #Contra
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AI contract review SaaS on Make.com: 1,000-page contracts read with citations, 94.9% accuracy
A property-law specialist in Australia wanted contract review that a lawyer can trust, which means every answer has to point at the clause it came from. No citation, no answer.
We built the product as a multi-tenant SaaS: a Next.js front end on Supabase, with Make.com as the entire backend. A firm uploads a property contract of 100 to 1,000 pages. Make.com splits it, drives the AI extraction and drafting, and returns a finished review document in the app and by email, every fact carrying a page reference. Each firm has its own quota, Stripe billing and an admin re-run tool.
The app itself is a proper product, not a form: a reviews table grouped by type with date filters and sort backed by the database, per-review run history with re-run, DOCX and PDF export, in-app or email delivery, a monthly review quota per firm and a file retention date on every submission. Front end on Vercel, database on Supabase, backend on Make.com.
The hard part was not the AI, it was long documents and rate limits. A 100 to 1,000 page contract cannot go through a model in one call, so the pipeline chunks, queues and resumes, honours Retry-After headers and uses idempotency keys so a retried chunk never writes twice. We built a golden-test set from real contracts and score every prompt version against it, with a five-category failure taxonomy.
Result: out-of-sample accuracy rose from a 70.2% mean to 79.5% between versions, with a best run of 94.9%, and a latent 1,000-page context bug was caught before it reached a client.
The full case study, with the product screens, is on my profile.
Prem Patel, Nex Automations. Make.com Level 5 certified, official Make partner, Zapier Certified Expert.
This sounds so helpful! I love the concept for this and the amount of effort.
Check it out. Just built a workflow for a client outside of Contra. Their workflow was complex, but due to confidentiality, I am only allowed to share a generic pattern here with my audience. Please have a look and enjoy the case study!
