AI Integration for Existing Products
Your product already works and your users already have data in it. I add LLM features on top: search that answers from your documents with a source for every answer, extraction that turns files into fields, and assistants that work inside your product instead of beside it.
The problem this solves
Your users keep asking for search or an assistant, but a model bolted on without your data gives confident wrong answers, and nobody on your team has time to wire it in safely.
Deliverables
- Document search that answers with citations
- Extraction of fields from PDFs and other files
- An assistant connected to your product's data
- Review flags for answers the model is unsure about
- Source code and deployment you own
How it works
I read your codebase and data first, then pick one feature to add, such as search over your documents. It is built into your existing stack and permissions, so users only get answers from data they are allowed to see. Anything the model is unsure about is flagged for a person to review. Further features follow on the same setup. The running cost depends mostly on which model each step uses, as I show in how much an AI agent costs to run.
When not to buy this
Not a fit if you want a new standalone agent on WhatsApp or Telegram (see AI Agent & Chatbot Development), or if the feature needs no model and a plain database query would do.
Stack
- OpenAI
- Anthropic
- LangChain
- PostgreSQL