WhiteBoxAI co‑creates custom machine‑learning models with municipal, public‑sector and enterprise clients, delivering fully explainable AI that logs provenance and provides visual decision pathways. Its privacy‑by‑design pipeline uses synthetic or encrypted data and low‑overhead architectures to enable on‑premise or edge deployment while meeting EU AI Act and data‑protection requirements. The platform supports rapid 2–4‑week pilots, API integration, and ongoing monitoring and support.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations such as municipalities, government agencies, and large enterprises often need AI-driven decision support but lack transparent, privacy‑preserving solutions that give them full control over model behavior and data handling, making compliance with regulations like the EU AI Act difficult.
Solution
WhiteBoxAI delivers “Closed AI” systems that are built jointly with the client through a structured co‑creation process. By involving stakeholders from the outset, the company designs custom machine‑learning models that are fully explainable, allowing users to trace how inputs lead to outputs. The solution uses privacy‑by‑design techniques, including dummy data for testing and secure, encrypted pipelines, to keep sensitive information protected. Rapid pilot cycles of 2–4 weeks enable quick validation of feasibility and value before full deployment. The resulting models are optimized for low computational overhead, facilitating integration into existing workflows and ensuring compliance with relevant AI regulations. Ongoing support and a platform for continuous innovation help clients scale AI use responsibly.
Target Audience
Primary customers are municipal governments, national or regional public agencies, and large enterprises that require strict data governance, explainability, and regulatory compliance for AI deployments.
Features
- Co‑creation methodology that engages client experts throughout data preparation, model design, and validation phases.
- Fully explainable AI models with built‑in provenance logs and visual decision pathways for auditability.
- Privacy‑preserving pipeline that employs synthetic or anonymized data for testing and encrypts all production data in transit and at rest.
- Rapid pilot implementation framework delivering validated prototypes within 2–4 weeks.
- Resource‑efficient model architectures tuned for minimal CPU/GPU usage, enabling deployment on on‑premise or edge environments.
- Compliance‑ready design adhering to the EU AI Act and other sector‑specific data protection standards.
- API‑driven integration layer and web dashboard for real‑time monitoring, performance metrics, and model management.
- Continuous support package offering training, documentation, and updates to keep the AI solution aligned with evolving business needs.