About AIDNI
Built by Risk Professionals, for Decision Makers
We built AIDNI because traditional software cannot handle the semantic complexity of Indian BFSI regulations. We combine deep domain expertise in risk management with GPU-accelerated AI and graph databases to build regulatory intelligence infrastructure at scale.
The Problem
Indian BFSI institutions navigate a complex web of regulators — RBI, SEBI, IRDAI, PFRDA, IFSCA, NABARD, MCA, IBBI, ICAI, and others — each issuing hundreds of circulars, master directions, and regulatory requirements annually. These documents are semantically complex, cross-referential, and use domain-specific legal language that generic NLP and keyword search cannot meaningfully parse. The result: risk teams, boards, and business leaders depend on manual tracking, spreadsheet-based monitoring, and institutional memory — leading to missed obligations, delayed regulatory responses, and avoidable penalties.
Our Approach
We don't build thin wrappers on third-party APIs. AIDNI runs its own data infrastructure — from ingestion pipelines and GPU-accelerated embedding models to a multi-database retrieval engine spanning vector search, time-series analytics, and knowledge graph traversal. Every component is purpose-built for regulatory NLP.
Multi-Database Architecture
Milvus (vectors), QuestDB (time-series), Neo4j (knowledge graph) working in concert
Semantic NLP Pipeline
Late-chunking embeddings and LLM-powered extraction of obligations and named entities
Knowledge Graph
Regulatory structures, cross-references, entity relationships, and regulatory lineage mapped for impact tracing
GPU-Accelerated Compute
Embedding and extraction pipelines on GPU for real-time document processing at scale
Company
Vision
Indian financial regulation is growing in volume and complexity every year. We believe the only sustainable approach is to treat regulatory text as structured data — extractable, queryable, and computable. AIDNI is building the infrastructure to make that possible at national scale.