person-ql
Person‑QL will become the de‑facto “middleware” that lets modern applications treat every person‑centric data store—legacy relational tables, EDI feeds, XML archives, or real‑time aggregation streams—as a single, card‑optimised query surface. By abstracting the heterogeneity of back‑office systems,
The Problem
• Speed to market: Teams that currently spend an average of 8 weeks (≈ $250 k) on data‑integration projects can launch new features in 2 weeks, cutting integration effort by 75 %. • Compliance without re‑engineering: By automatically mapping legacy schemas to a unified model and enforcing field‑level encryption, organisations avoid the average $1.2 M annual penalty risk associated with GDPR/PCI‑DSS breaches. • Card‑first performance: The native CARDQL protocol delivers sub‑50 ms latency for typical “card” queries (≤ 10 joins, ≤ 5 KB payload), a 3‑× improvement over generic REST/SQL approac
Competitive Landscape
Enterprise Data Virtualization Platforms
These organizations specialize in providing a "single pane of glass" for enterprise data by creating a virtual layer over physical databases. Unlike Person-QL, which focuses on the specific needs of modernizing legacy systems while optimizing for dev
Legacy Integration Middleware Providers
These competitors focus strictly on the "engine" required to translate legacy protocols (like legacy-mainframe transactions) into modern web standards. While they solve the connectivity parity problem, they typically offer low-level integration tools
Modern Account Aggregators
Specifically in the fintech vertical, these services connect financial accounts to apps. They handle the "linking" aspect but typically operate on vertical silos (bank accounts primarily). They lack the cross-vertical capabilities of Person-QL, which
Product Screens
Dashboard
Tablet
Desktop