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
Enterprise data is often fragmented across multiple systems, leading to inconsistent reports, reconciliation challenges, and a lack of confidence in analytical decisions. Without reliable data preparation, teams spend excessive time cleaning and validating data instead of generating actionable insights.
Solution
aidnn provides an automated platform that ingests, cleans, and reconciles disparate enterprise data sources, continuously learning an organization’s rules, standards, and historical analyses through its self‑learning cognitive layer, Neocortex. Specialized AI agents plan, execute, and verify each analytical step, producing formally verified results that are version‑controlled and auditable. The platform translates natural‑language questions into executable code, runs the analysis, and subjects the output to a separate verifier agent to ensure correctness before delivery. All actions are logged end‑to‑end, enabling reproducibility, change management, and integration with existing CI/CD pipelines. By handling data preparation and verification, aidnn frees analysts to focus on high‑value insights while delivering decision‑ready answers with documented trust.
Target Audience
Primary customers are finance, operations, and analytics teams in mid‑size to large enterprises that need reliable, cross‑system insights for reporting, forecasting, and strategic decision‑making.
Features
- Automated data cleaning and reconciliation across heterogeneous systems, detecting missing values, duplicates, and outliers with transparent resolution reports
- Neocortex self‑learning engine that continuously models organizational rules, standards, and past work to guide agents’ planning and execution
- Multi‑agent architecture with role‑based specialization and a verifier agent that reviews plans, queries, and results for accuracy
- Version‑controlled analyses and CI/CD integration, providing audit trails and reproducible decision pipelines
- Natural‑language interface that converts user questions into code, enabling non‑technical users to request complex analytics
- Built‑in verification, testing, and change‑management workflows that benchmark prompts and skills on real data and roll back regressions