Service · 02
RAG Chatbots & Machine Learning
Chatbots grounded in your data — retrieval done right so answers stay true.
How it's built
(1) Data audit — source inventory, cleaning, chunking strategy. (2) Retrieval design — embedding/index choice, hybrid search + reranking. (3) Build — pipeline and chat orchestration with citations and memory. (4) Eval harness — groundedness and recall sets, hallucination gates. (5) Launch — feedback loop and continuous index updates.
Core fundamentals
- answers cite sources
- hallucination rate measured, not assumed
- retrieval benchmarked on your real questions
- index stays fresh automatically
Build blueprint
Deliverables
- ingestion pipeline
- chat API/widget
- eval report
- admin docs
Stack
PythonEmbeddingsVector DBLangChainEvals
Custom quote