AIQA provides a cloud‑native SaaS platform that automates data‑quality validation and enrichment for regulated enterprises. It offers a rule‑based, explainable AI mode for compliance‑ready outputs (e.g., EMIR, SFTR, MAS) and an LLM‑enhanced mode that harmonizes unstructured data across sources, delivering >95% accuracy with audit trails and API integration.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises in regulated industries often contend with fragmented, unstructured data and insufficient data‑quality controls. Missing, duplicate, or incorrectly formatted records lead to costly compliance errors, manual remediation, and delayed reporting. Existing data‑quality tools require deep technical expertise, limiting adoption by non‑technical business users.
Solution
AIQA delivers a cloud‑native, AI‑driven platform that treats data quality as a proactive service. The solution offers two interchangeable modes: AIQA Reg, which applies deterministic, rule‑based logic with explainable AI to generate submission‑ready, audit‑traceable outputs for regimes such as EMIR, SFTR, and MAS; and AIQA Infinite, which leverages LLM‑powered enrichment, RAG scoring, and multilingual harmonisation to transform messy operational data into clean, structured datasets. Both modes run on the same scalable engine built with Java, cloud micro‑services, and machine‑learning pipelines, providing >95 % accuracy on real‑world inputs while maintaining full traceability. The platform operates entirely as a SaaS service—no client‑side installation—allowing continuous monitoring, autonomous error correction, and seamless integration with downstream systems via secure APIs.
Target Audience
Primary customers are compliance officers, regulatory operations teams, and risk‑reporting groups in banking and financial services, as well as data analysts and commercial operations leaders who need to unify and cleanse large, unstructured data sets.
Features
- Deterministic, rule‑based engine with explainable AI delivering compliance‑ready results and full audit trails
- Regulatory schema mapping for EMIR, SFTR, MAS and other global regimes, supporting deterministic validation >95 % accuracy
- LLM‑enhanced data harmonisation across heterogeneous sources, file types (PDF, Excel, email) and languages
- Retrieval‑Augmented Generation (RAG) scoring and enrichment pipelines that augment raw records with external knowledge bases
- No‑code UI and REST/GraphQL APIs for easy embedding into existing data pipelines and BI tools
- Cloud‑native micro‑service architecture (Java, Kubernetes) providing elastic scaling and high‑availability SLAs
- End‑to‑end encryption, role‑based access control, and compliance with industry security standards (e.g., ISO 27001, SOC 2)
- Continuous learning loop that auto‑detects recurring patterns and refines correction rules without manual intervention