In most cases, RAG first: it relies on your up-to-date data, cites its sources and costs less. Fine-tuning is mostly for adjusting a style or a format, rarely for adding knowledge.

Do I need to rewrite my application to add AI?

No. AI is added as a service: a FastAPI API next to your application, or a direct integration into Laravel or Symfony. I work remotely from Antananarivo, with your team.

Does my data stay confidential?

We pick the architecture based on your constraints: a provider API with a no-retention commitment, or a model hosted on your side. Document access respects each user's permissions.

Which model should I choose?

The one that handles your use case at the best cost, measured on your own data. I compare several models before recommending one.

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AI & RAG Integration: add AI to your existing application

Add intelligence to your existing app, no need to start from scratch.

The problem I solve

You want to put your documents, your knowledge base or your business data to work with AI, but a ChatGPT demo is not enough: you need reliable, sourced answers, integrated into your application, secure and at a controlled cost. Rebuilding your product is not an option: AI has to be added to what already exists.

What you get

✓RAG pipeline on your business data
✓Contextual chatbot or automatic summarization
✓Production-ready Python API
✓OpenAI / Groq / local model integration

How I work

1
Use cases
We identify where AI genuinely adds value (search, support, summarization, extraction), and where it does not.
2
RAG pipeline
Chunking and embeddings of your documents, a vector database (pgvector, Qdrant or Chroma), hybrid search and answers with sources.
3
Integration
A Python FastAPI API or an AI SDK in Laravel, streamed answers, tools and guardrails against prompt injection.
4
Production
Answer quality evaluation, monitoring, cost per request tracking and model choice (OpenAI, Claude, Groq or a local model).

Case study

3 sources

RAG assistant for my portfolio (personal project)

A chatbot that answers visitors' questions from 3 indexed sources (portfolio content, LinkedIn export, GitHub READMEs). FastAPI API, LangChain, ChromaDB vector database, embeddings computed locally, model switchable between Groq (Llama 3 70B) and a local Ollama, answers in French or English.

Tech Stack

Python FastAPILangChainLaravel pgvectorRESTful APIs

Frequently asked questions

RAG or fine-tuning?: In most cases, RAG first: it relies on your up-to-date data, cites its sources and costs less. Fine-tuning is mostly for adjusting a style or a format, rarely for adding knowledge.
Do I need to rewrite my application to add AI?: No. AI is added as a service: a FastAPI API next to your application, or a direct integration into Laravel or Symfony. I work remotely from Antananarivo, with your team.
Does my data stay confidential?: We pick the architecture based on your constraints: a provider API with a no-retention commitment, or a model hosted on your side. Document access respects each user's permissions.
Which model should I choose?: The one that handles your use case at the best cost, measured on your own data. I compare several models before recommending one.