Aiden — Natural Language Analytics for Enterprise Data

Aiden is an AI-powered natural-language analytics platform for querying MongoDB, Snowflake, PostgreSQL, CSV, XLSX, and enterprise datasets without writing SQL or MongoDB aggregation pipelines.

Vivek Vedant works on AI and backend systems behind Aiden, including schema discovery, relevant-data selection, query planning, retrieval-grounded context, SQL and MongoDB query generation, validation, conversational analytics, and dashboard or report-style output workflows.

The Problem

Business users often know the question they want answered but do not know database schemas, collection names, joins, field meanings, SQL, or MongoDB query syntax. Aiden bridges that gap by translating natural-language questions into executable analytics workflows over connected enterprise data.

How Aiden Works

1. Natural-language question
2. Schema discovery across databases and files
3. Relevant table, collection, field, and metadata selection
4. Query planning and validation
5. SQL, MongoDB aggregation pipeline, or Python analysis execution
6. Answer, chart, table, dashboard, or report generation

Technical Architecture

Aiden uses Python, FastAPI, MongoDB, Snowflake, PostgreSQL, LLMs, retrieval pipelines, metadata grounding, source-aware execution, and visualization workflows to turn user questions into reliable analytics output.

Building Reliable Natural-Language Analytics

Generating syntactically valid database queries is only part of the problem. A reliable analytics system must first determine whether it has identified the correct datasets, fields, relationships, filters, and aggregations required to answer the user's question.

My work on Aiden focuses on this layer: schema discovery, relevant data selection, table selection, query planning, MongoDB and SQL generation, validation, and converting query results into useful answers and data visualization.