Services
RAG & Semantic Search
Retrieval-augmented generation and semantic search that turn internal documents and data into accurate, searchable answers grounded in your own knowledge.
What you get
- Ingestion, chunking and embedding pipelines
- Hybrid keyword + vector retrieval
- Grounded answer generation with prompt engineering
- Evaluation of retrieval and answer quality
How I work
- 1
Scope & architecture
Clarify the goal, constraints and success metrics, then design the simplest architecture that meets them.
- 2
Build & evaluate
Ship in small increments with tests and evaluation, keeping performance, security and cost measurable.
- 3
Deploy & operate
Containerize, automate the pipeline and deploy to production — then monitor and improve.
Technologies
Examples from my work
Generative AI · RAG
RAG Knowledge Assistant
Production-oriented RAG systems that turn internal documents and data into searchable, actionable knowledge.
Read the case studySearch · AI
Semantic Search
Search infrastructure that goes beyond keyword matching, retrieving information through embeddings and contextual similarity.
Read the case study
Frequently asked questions
Can you deliver RAG & Semantic Search remotely?+
Yes. I'm based in Casablanca, Morocco and work with teams remotely and internationally, in English and French.
Which technologies do you use?+
Mainly Embeddings, OpenSearch, Redis, LLM and Python — chosen to fit the project rather than the other way around.
Do you have examples?+
Yes — see the case studies: RAG Knowledge Assistant and Semantic Search.
How do we start?+
Send a message on LinkedIn or Malt with a short description of the project; we'll scope it together before any commitment.
Abdelkabir El Bahmadi · Software / AI Engineer · Casablanca
Services
Have a project in mind?
Open to remote and international opportunities — engineering roles, AI projects and product collaborations.