AI-Powered Taxation AssistantFinancial-Statement & Competitor-Based Tax Insights (RAG + LLM)

A smart assistant that maps financial data to tax treatments using Retrieval-Augmented Generation and Large Language Models.

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🧩 System Architecture

RAG + LLM Engine

Combines retrieval and generation to provide context-aware, citation-backed tax insights.

Financial-to-Tax Mapping

Automatically links accounting line items to relevant tax provisions and Finance Act clauses.

Competitor Benchmarking

Analyzes peer company financials to highlight anomalies or risk-prone tax positions.

Risk-of-Notice Scoring

Machine-learning model estimating likelihood of scrutiny and suggesting remediation steps.

Explainability & Citations

Every recommendation includes statute references and source provenance for auditability.

🧠 About the Project

This AI-Powered Taxation Assistant is designed to make sense of complex financial and tax data using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).

It analyzes:

Company and competitor financial statements
CBDT & Finance Ministry circulars
Finance Act amendments (current and previous FY)

and produces:

Tax-Regime Classification
Personalized Compliance Suggestions
Risk-of-Notice Scoring
Competitor Benchmarking