QuantPi provides deep, component-level testing of Retrieval-Augmented Generation (RAG) architectures across enterprise environments. These systems combine an information-retrieval pipeline with a generative language model to provide answers grounded in a designated corporate knowledge base. Where outputs drive downstream decision-making with strict legal, financial, or operational consequences, silent subcomponent failures must be isolated at the root cause.
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• Subcomponent alignment: Isolating retrieval weaknesses from generative flaws.• Context relevance: Precision and completeness of retrieved text chunks.• Faithfulness: Factual grounding of the output within the source context.• Answer relevance: Direct alignment of the final response to user intent.• Robustness to query perturbations: Stability under typos, semantic drift, abbreviations and rephrasing.• Retrieval drift: Performance consistency across expanding knowledge bases over time.A root-cause diagnostic map, explicitly isolating end-to-end pipeline failure points across atomic information-flow states.
A recommendation with concrete next steps per failure mode (prompt, chunk size, retriever, reranker, model), sliced by relevant metadata for your domain.
A traceable, repeatable evidence package supporting deployment decisions and compliance tracking.
Applied across corporate compliance, financial research, and enterprise knowledge management contexts.
