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The document AI question I get asked most is which model to run. It's rarely what decides the outcome.
How the PDF becomes text decides it. One benchmark had a parser recovering 75% of the words and 13% of the document structure, and no model reading that output can rebuild a table that already got flattened.
Cost is the argument that ages fastest here. Hosted inference keeps getting cheaper, so a spreadsheet that justified self-hosting in January often flips by summer. A clause saying the data cannot leave your infrastructure doesn't flip.
Full breakdown of where each option earns its place: https://www.duskolicanin.com/blog/ai-document-processing-local-vs-openai-2026
#AI #DocumentAI #SaaS #SoftwareEngineering
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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.
AI can build a website.
But it can’t replace the thinking behind a great one.
You still need humans to ask the right questions, understand the context, make the decisions, challenge the obvious answers, and turn all of that into something meaningful.
What do you think about this feature section?
Empathy and Design Strategy are irreplaceable no matter the AI you use.
👋 Hi Contra!
I'm a full-stack developer with 7+ years of commercial experience, and right now I'm actively looking for new projects.
I build things from idea to production:
• Websites and web apps with Next.js & React
• Backend and APIs with Python / FastAPI / Node.js
• PostgreSQL and databases
• Telegram bots, Mini Apps and automation
• AI-powered tools and integrations
• MVPs and internal tools
I recently joined Contra and have already completed my first project — now I'm looking to take on more work and build long-term relationships with great clients.
At the moment, I have availability and I'm flexible with project budgets, especially for interesting projects, startups and long-term collaborations.
If you need someone who can take an idea, build the frontend and backend, connect APIs, AI services or payments — and actually ship a working product — I'd love to talk.
And if you know someone looking for a developer, I'd really appreciate a connection or a share 🙌
Impressive experience and skill set; great to see strong full-stack expertise paired with polished, production-ready work! 👏🚀