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Systems evaluation demonstrates compliance-safe portfolio intelligence for retail investors, highlighting the benefit of decoupling financial calculation from language model narration.
This paper presents AI CFO India, a deterministic-first, retrieval-grounded multi-agent architecture for explainable and compliance-safe portfolio intelligence for Indian retail investors. The system separates deterministic financial computation from AI-generated explanation. Seven independent deterministic agents compute portfolio findings, while a single isolated LLM-based Explainability and Synthesis Agent provides grounded narration. A retrieval-based knowledge base and hard compliance engine are used to reduce hallucination and prevent directive or advisory language. The implementation uses FastAPI, PostgreSQL, pgvector, and a React/TypeScript frontend. An implementation-level evaluation reports 65 passing automated tests covering unit, integration, resilience, and compliance behaviour.
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Sakshi Satish Patole (2026) studied this question.
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