RAG Development with Malahim Haseeb
AI that actually knows your data
I build retrieval pipelines that let AI search and reason over your documents, PDFs, and knowledge base without hallucinating about content that is right there in the files. I handle chunking, embeddings, retrieval, and answer grounding.
Who it is for
Teams with a body of documents the AI should answer from. Internal wikis, support archives, compliance docs, research libraries, product documentation.
What it covers
- Internal knowledge base Q and A
- Support ticket history search
- Document Q and A for enterprises
- Semantic search over user content
How it works
- 01
Ingest and chunk
I load your documents and split them into retrieval sized chunks.
- 02
Embed and index
I generate embeddings and set up the vector store.
- 03
Retrieve and ground
I wire retrieval into the LLM with source citations and fallbacks.
- 04
Evaluate
I test answer accuracy against real questions before shipping.
Questions
How do you make sure RAG answers accurately?
I test retrieval before wiring the model. Chunking, embeddings, and search quality get checked against real questions before the LLM is ever asked. Then I add evals so accuracy is measured, not assumed.
How long does a RAG build take?
It depends on how much documentation you have and how specific the questions need to be. I scope the ingestion work before quoting.
Do you support PDFs and internal wikis?
Yes. I have built pipelines for PDFs, plain text docs, and wikis. The ingestion approach changes per source type, and I set that up during scoping.
Can you integrate RAG into an existing app?
Yes. I have worked with existing codebases and added RAG features without breaking what was running. I read the code first, then design the AI layer around it.
How much does an AI agent or RAG system cost to build?
It depends on scope. A chatbot with a knowledge base is one kind of project, an agentic system with tool use and evaluation is another. I scope the first call before quoting, so you know what the build costs before anyone writes code.
How do you make sure an AI chatbot answers accurately?
I test retrieval before wiring the model. Chunking, embeddings, and search quality get checked against real questions before the LLM is ever asked. Then I add evals so accuracy is measured, not assumed.
Can you integrate AI into an existing product?
Yes. I have worked with existing codebases and added AI features without breaking what was running. I read the code first, then design the AI layer around it.
What happens when the model or the data is not good enough?
I build fallbacks. Retrieval fallbacks, model fallbacks, confidence handling, and human escalation paths. An agent that loops forever or answers from the wrong context is a failure mode I design against.
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