Industry: Finance
Built for finance teams to extract deep analytics from multi-table databases using natural language, removing the need for manual SQL coding.
Problem
The reliance on technical staff for database queries created significant reporting delays and operational bottlenecks.
- Technical Barriers: Non-technical users could not access complex analytics stored across multiple relational tables without SQL knowledge.
- Information Latency: Waiting for IT departments to generate custom reports delayed critical financial decision-making.
- Resource Drain: Data teams spent excessive time on repetitive, ad-hoc query requests instead of high-level analysis.
Solution
The platform utilizes LLM orchestration to bridge the gap between natural language questions and structured database queries.
- NLP to SQL Translation: Automated conversion of plain-English questions into complex, multi-join SQL queries.
- API-First Architecture: Developed a robust FastAPI backend to manage high-speed request processing and database connectivity.
- Multi-Model Orchestration: Leveraged LangChain to integrate OpenAI and Anthropic models for improved query accuracy and validation.
- Structured Data Delivery: Transformed raw database outputs into intuitive, human-readable formats for immediate use in reports.
Tech Stack
- Backend Framework: FastAPI.
- Orchestration: LangChain.
- AI Models: OpenAI and Anthropic.
- Language: Python.
Results & Impact
- Instant Reporting: Enabled users to obtain complex insights—such as monthly tax lows by sector—in seconds rather than days.
- Operational Independence: Eliminated data bottlenecks by reducing the finance department’s reliance on IT for standard reporting.
- Data Integrity: Ensured high-precision data retrieval through automated query validation and error handling.
- Scalable Analytics: Provided a single interface capable of querying massive, multi-table financial environments at scale.