Building DataNexus - An AI Financial Analytics Platform Processing 50+ Enterprises
#React#NodeJS#FastAPI#LangChain#LanceDB#Pandas
Client
Mid-Market B2B FinTech Analytics Firm - OpenCloud
Languages
Javascript, TypeScript, Python
Fig. 1 — DataNexus AI Platform featured view (click to expand)
01
The Problem
Business Impact
Financial analysts spent weeks sorting through scattered operational data, tax forms, and ERP spreadsheets to locate transactional irregularities.
Constraints
Strict client privacy rules restricted sending data to public AI APIs, demanding locally run compliance models.
02
Technical Execution
Architectural Strategy
Designed a self-contained AI analytics platform using local FastAPI vector indexing and local embeddings - no data leaves the client's infrastructure.
Depth of Execution
Built a natural language query router that converts user questions like "Show tax irregularities in Q3" into filtered SQL queries executed over Pandas dataframes.
03
Results
✓Audit data analysis times decreased from weeks to seconds.
✓Identified operational irregularities in over 89% of processed data.
✓Successfully scaled to support 50+ large enterprises.
Future Proofing
Constructed query layers with abstract adapters, allowing clients to swap database layers (e.g. LanceDB to PostgreSQL) without re-engineering.
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04
Client Feedback
"Excellent communication and technical skills. Will definitely use Adarsh again for future projects."
