Pratik Patil
AI Integration for Existing Systems | Agents & SaaS
A typical example. Same accounting app. Same team. 7 manual steps turned into 1 click.
Invoices arrive by email, AI reads the PDF and fills the fields, a draft entry is created through the app's existing API, and a person approves it in one click. Anything unusual gets flagged...
An AI demo is easy. An AI integration that survives real data is the hard part.
Three guardrails I add to every system I plug AI into - structured outputs your software can trust, a human approval step for anything irreversible, and logs plus a test set so quality never silently...
Most businesses don't need a new system to use AI. They need AI plugged into the one they already run.
The 4 places I usually add it
1. In the inbox - emails, PDFs and forms turned into clean records
2. In the workflow - AI sorts and routes work so people only handle exceptions
3....
Hand-written scrapers break every time a vendor changes its UI. So I'm building an AI browser agent that learns a new web app, writes its own Playwright scripts, runs them in parallel, and repairs them itself when a selector breaks. Read-only by design, API-first, human review...
Most brands lose leads in their comment sections. I built an LLM classifier that sorts every social comment by intent (buying signal, question, complaint, spam) and routes the good ones straight into the CRM. Claude + SocialAPI.ai + GoHighLevel. Case study below 👇
