AI-powered Conversational Finance Intelligence Platform that transforms Excel-based financial reports into an interactive analytics environment. The solution enables finance teams to access KPI insights, analyze business performance through natural language conversations, standardize financial knowledge, and reduce manual effort in reporting, variance analysis, and decision-making.
The organization’s finance team relied on multiple Excel reports to monitor business performance, analyze contribution margins, and track key financial metrics. The objective was to reduce manual reporting effort, improve accessibility of financial insights, and establish a governed knowledge framework that could deliver consistent and explainable analytics across the organization.
Finance teams spent significant time consolidating Excel files, validating calculations, preparing management reports, and answering repetitive business queries. This reduced the time available for deeper financial analysis and strategic decision support.
Business users depended heavily on finance experts to interpret KPIs, explain variances, and provide performance insights. Accessing information often required manual analysis and delayed decision-making.
Financial metrics, business terminology, abbreviations, and calculation logic were maintained across spreadsheets and individual knowledge repositories, creating inconsistencies and increasing dependency on key personnel.
A Retrieval-Augmented Conversational Finance Intelligence Platform built around governed KPI knowledge, semantic search, and expert feedback workflows.
A cloud-native finance intelligence solution that converts Excel-based finance reports into a centralized analytics and knowledge platform. Financial reports uploaded through the Data Hub are processed and stored within PostgreSQL, while KPI definitions, metrics, abbreviations, synonyms, and training examples are managed through a dedicated Knowledge Management layer.
The solution was built using React 18 (Vite) for the frontend, FastAPI for backend APIs, SQLAlchemy for data management, and PostgreSQL for structured storage. LlamaIndex was used as the agent framework to orchestrate retrieval workflows, while FAISS provided semantic vector search across finance data and knowledge assets. Claude Sonnet and Haiku models hosted on AWS Bedrock generated contextual responses to business questions.
A human feedback loop was embedded into the platform to continuously improve response quality. Users can rate answers using thumbs-up and thumbs-down feedback. Questions requiring validation are automatically routed to finance experts through the Actions module, where responses can be reviewed, corrected, and incorporated into the knowledge base for future use.
A centralized Finance Intelligence Workspace with role-based access for finance users and administrators to manage reports, govern knowledge, and interact with financial data through conversational analytics.
Upload, review, organize, and manage Excel-based finance reports while providing filtered access to uploaded datasets through a centralized reporting repository.
Manage KPI definitions, metrics, abbreviations, synonyms, and training examples to establish a governed financial knowledge foundation for consistent analytics.
Ask natural language questions, access predefined finance FAQs, explore follow-up questions, and receive explainable responses generated directly from report data.
Create custom dashboards, monitor important KPIs, manage user feedback, review unanswered questions, and continuously improve AI responses through expert validation workflows.
The platform transformed financial reporting from a spreadsheet-driven process into a self-service intelligence experience. By centralizing financial knowledge and enabling conversational access to business data, the organization reduced dependency on finance experts, improved KPI governance, and established a scalable foundation for AI-driven financial analytics.