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An automated customer support ecosystem powered by Make.com and Google Gemini AI. It receives tickets across channels, uses AI to analyze sentiment, priority, and intent, logs ticket data into Google Sheets, and sends instant automated responses via Gmail to reduce support response times.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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AI-Powered Lead & Business Process Automation
Built a multi-branch automation system with Make, OpenAI, Google Sheets, Gmail, Telegram, Slack, Notion and other integrations. The workflow automatically captures incoming data, analyzes requests with AI, routes them based on conditions, updates databases and sends notifications to the right channels.
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.