RAG & agentic AI
RAG implementation and agentic AI development
RAG quality is a retrieval problem far more than a model problem. Most systems that hallucinate are not using a weak model, they are retrieving the wrong passages and asking the model to make the best of it. I build retrieval you can audit, with grounding boundaries and citations, so answers can be trusted and verified.
Who this is for
- ▹Your assistant answers confidently and is sometimes wrong
- ▹You need answers grounded in your own documents, with citations
- ▹You are evaluating agents and want them only where they earn their complexity
- ▹You need retrieval that works across languages and paraphrase, not keywords
What the work covers
Hybrid retrieval
Dense embeddings plus lexical search with reciprocal rank fusion, so both meaning and exact terms are covered.
Reranking and grounding
Cross-encoder reranking, strict grounding boundaries and inline citations, plus honest refusal when the source does not contain the answer.
Multi-agent orchestration
LangGraph, LangChain and CrewAI workflows with typed handoffs and verification stages, used only where they create leverage.
Evaluation
Groundedness and faithfulness scoring so retrieval quality is measured, not assumed.
Evidence this works
- ✓Enterprise legal Q&A delivered at 92% accuracy with multi-stage verification
- ✓40% reduction in support tickets and 89% user satisfaction on a production assistant
- ✓Offline multilingual semantic search running with no external vector database
How the engagement runs
- 1Discovery sprint: I audit your corpus, chunking, retrieval and failure cases, then propose the pipeline that fits your accuracy bar.
- 2Build: retrieval, reranking, grounding and citations, with an evaluation set so quality is provable.
- 3Handover: the pipeline plus the eval harness, so your team can change it safely.
Common questions
Why does our RAG system still hallucinate?
Almost always because retrieval is returning the wrong passages, not because the model is weak. The fix is hybrid retrieval, reranking and strict grounding rules that force the model to answer only from retrieved context and to refuse when the answer is not there.
Do we need agents, or is a pipeline enough?
Usually a deterministic pipeline with good retrieval is enough, and it is cheaper and more predictable. Agents earn their place when a task genuinely needs planning or tool use. I recommend agents only where they create leverage.
Can retrieval work in multiple languages?
Yes. Embedding-based retrieval handles paraphrase and language variation far better than keyword lists, which drift from real user language over time.
Related work and demos
Working on something like this?
Tell me what you are building and where it is stuck. I reply within one business day.
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