AI features in your existing product
Document Q&A over your own data, MCP servers, transcription, extraction and LLM automation — added to the app you already run, not bolted on as a separate demo. Designed around cost, latency, privacy and the ways models fail.
What I build
- Answers from your own documents (RAG). Search over your PDFs, knowledge base or records with pgvector, with sources shown for every answer so users can check it.
- Voice to structured text. Whisper transcription turned into notes, forms or tickets.
- MCP servers for your app. Your own operations — orders, customers, reports — exposed as MCP tools so AI assistants can work with them safely. On Odigix, the new admin gets its MCP server through Laravel MCP.
- Extraction and automation. Turning emails, invoices or free text into structured data your app can act on, with a human check where mistakes cost money.
What makes it production-ready
A demo answers the question it was tested on. A feature has to handle the rest:
- Cost and latency budgets per request, with caching where answers repeat.
- Failure modes designed in. Timeouts, empty retrievals and low-confidence answers fall back to something useful instead of an invented answer.
- Privacy by default. Personal data de-identified before it leaves your system, and a clear record of what is sent where.
- A way to measure it. A small set of real questions with expected answers, run before every change to prompts or models, so "it feels better" isn't the test.
Where I've done it
DocmateAI (at The Trybe): a platform for healthcare professionals with RAG over medical documents using FastAPI and pgvector, Whisper transcription of consultations, and de-identified notes. The case study covers the architecture and what I'd change.
أسئلة شائعة
- Do we need to rewrite our app in Python to add AI?
- No. Most AI features are an API call plus careful data handling. They can live in your Laravel or Node app; a small Python service (FastAPI) only makes sense for heavier pipelines, which is how DocmateAI's RAG was built.
- Which models do you use?
- Whatever fits the task, cost and data rules — typically OpenAI or Anthropic APIs, plus Whisper for speech. The model sits behind one interface in your code so it can be swapped without touching the feature.
- What about sensitive data?
- It's decided before any code: what may leave your servers, what gets de-identified first, and what's logged. DocmateAI handled medical notes, so personal identifiers were removed before any text reached a model.
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