AI CFO India: A Deterministic-First, Retrieval-Grounded Multi-Agent Architecture for Explainable, Compliance-Safe Portfolio Intelligence for Indian Retail Investors

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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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-27
DOI
https://doi.org/10.5281/zenodo.22125739
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
preprint
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preprint

AI CFO India: A Deterministic-First, Retrieval-Grounded Multi-Agent Architecture for Explainable, Compliance-Safe Portfolio Intelligence for Indian Retail Investors

Sakshi Satish Patole
Zenodo (CERN European Organization for Nuclear Research)
Explainable Artificial Intelligence (XAI)
preprint

AI CFO India: A Deterministic-First, Retrieval-Grounded Multi-Agent Architecture for Explainable, Compliance-Safe Portfolio Intelligence for Indian Retail Investors

Sakshi Satish Patole
preprint en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Rajarshee Chhatrapati Shahu Maharaj Government Medical College and CPR Hospital Kolhapur (IN)
Explainable Artificial Intelligence (XAI)
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AI CFO India: A Deterministic-First, Retrieval-Grounded Multi-Agent Architecture for Explainable, Compliance-Safe Portfolio Intelligence for Indian Retail Investors — Sakshi Satish Patole · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS