aidnn provides an AI-driven platform that cleans, reconciles, and verifies fragmented enterprise data, turning natural‑language questions into executable code for analytics. Its self‑learning “Neocortex” system continuously models organizational context, applying defined rules and past work memories to generate formally verified decisions. The platform enables businesses to automate complex analyses while maintaining ownership of the software development lifecycle.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often have data spread across multiple systems, with inconsistencies, missing values, and duplicate records that require extensive manual cleaning and reconciliation before any analysis can be performed. This fragmented data landscape leads to slow reporting cycles, unreliable insights, and high operational overhead.
Solution
aidnn provides an automated platform that ingests fragmented enterprise data, cleans and reconciles it, and then answers natural‑language queries with formally verified analytics. Its self‑learning cognitive layer, Neocortex, continuously models an organization’s rules, standards, and historical analyses to guide specialized AI agents in planning, executing, and validating data transformations and queries. The system generates code from user prompts, runs multi‑level verification—including inline plan checks and offline consistency audits—and logs every step for full auditability. By integrating seamlessly with existing data sources, aidnn delivers analysis‑ready results and visualizations without requiring manual data preparation.
Target Audience
Primary users are finance, analytics, and operations teams in mid‑size to large enterprises that need reliable, automated data cleaning, reconciliation, and insight generation without extensive engineering effort.
Features
- Automated detection and resolution of missing values, duplicates, and outliers with detailed resolution reports
- End‑to‑end data reconciliation across disparate sources, preserving original data semantics
- Natural‑language query interface that translates user questions into executable code
- Multi‑model AI agents with role‑based specialization and critic veto for collaborative, privacy‑preserving analysis
- Continuous self‑learning (Neocortex) that captures organizational rules, habits, and past work to improve future decisions
- Multi‑level verification: inline plan and result checks plus deeper offline consistency validation
- Version‑controlled analysis pipelines with CI/CD integration and full audit trails
- Automatic generation of visualizations (e.g., revenue waterfalls, sankey charts) from plain‑language requests