MTN Data Foundry offers an AI‑powered integration layer that ingests any healthcare data source—EHRs, IoT devices, billing systems—without a predefined schema, automatically maps fields to a shared canonical model, and continuously adapts to schema changes. The platform provides versioned, auditable transformations with SOC 2‑aligned and BAA compliance, enabling health systems and AI teams to stream normalized data into existing warehouses, BI tools, or machine‑learning pipelines while eliminating integration debt and reducing reporting delays.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare organizations and AI teams must integrate data from dozens of disparate EHRs, IoT devices, and billing systems, each with its own schema and compliance requirements. The resulting integration debt leads to delayed reporting, manual reconciliation, stalled AI pilots, and revenue risk.
Solution
MTN Data Foundry provides an AI‑powered integration layer that ingests any data source without a predefined schema, automatically maps fields to a shared canonical model, and continuously adapts to schema changes. The platform learns from prior mappings, applies high‑confidence matches automatically, and routes low‑confidence cases to human reviewers, ensuring accurate harmonization without guesswork. All transformations are versioned with full audit logs and governance controls, meeting SOC 2‑aligned and BAA compliance standards. The normalized data is streamed to the customer’s existing warehouse, BI tools, or AI models, enabling rapid deployment of analytics and machine‑learning workloads while keeping downstream systems protected from breaking changes.
Target Audience
Primary customers are health system operators, AI/analytics teams, and enterprise portfolios that manage multiple acquired sites and need reliable, compliant data integration for AI deployments and business intelligence.
Features
- Schema‑agnostic ingestion from any EHR, IoT feed, or billing system with end‑to‑end encryption
- Automated field mapping using value patterns, naming conventions, and industry standards, with confidence scoring and human‑in‑the‑loop review for low‑confidence matches
- Self‑healing pipelines that detect source schema changes and re‑map fields automatically, preserving historical data integrity
- Full audit logging, versioned mappings, and role‑based access to satisfy healthcare governance and compliance requirements
- Non‑disruptive operation alongside existing data warehouses, BI platforms, and AI stacks; no need to replace or modify source systems
- Real‑time issue alerts that surface integration problems before they affect downstream analytics or models