Indigma provides a responsible AI platform that enables federated learning with differential privacy, homomorphic encryption, and secure multi‑party computation, keeping raw data on‑device while allowing collaborative model improvement. The solution includes blockchain‑anchored audit trails, explainable‑AI dashboards, and machine‑unlearning tools to meet GDPR, HIPAA, and other regulatory requirements for regulated enterprises.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and startups increasingly rely on AI, but centralized model training exposes sensitive data, creates compliance risks under regulations such as GDPR and HIPAA, and often lacks transparency or explainability. These constraints limit the adoption of advanced analytics, generative models, and autonomous agents in regulated sectors.
Solution
Indigma delivers end‑to‑end responsible AI platforms that keep raw data on‑device while still enabling collaborative model improvement. By combining federated learning with differential privacy, homomorphic encryption, and secure multi‑party computation, the company ensures that model updates cannot be reverse‑engineered. Blockchain‑based provenance records provide immutable audit trails for model lineage and data usage, supporting regulatory audits. Explainable‑AI dashboards surface feature importance and decision pathways, giving stakeholders clear insight into model behavior. The service suite includes custom generative AI and AI‑agent development, machine‑unlearning capabilities for data‑right compliance, and secure CI/CD pipelines that harden AI deployments against tampering. All solutions are packaged as consult‑to‑deployment engagements tailored to the client’s technology stack and compliance requirements.
Target Audience
The primary customers are regulated enterprises—such as financial services, healthcare providers, and telecom operators—and high‑growth startups that need privacy‑preserving, auditable AI while maintaining rapid innovation cycles.
Features
- Federated learning framework with secure aggregation, enabling model training across millions of edge devices without raw data transfer.
- Integrated differential privacy mechanisms that add calibrated noise to updates, providing provable privacy guarantees.
- Homomorphic encryption layer allowing encrypted model aggregation, so servers never see plaintext updates.
- Secure multi‑party computation (SMPC) protocol that distributes computation across multiple nodes, preventing any single party from accessing individual contributions.
- Blockchain‑anchored audit log for model versioning, data provenance, and immutable compliance records.
- Explainable AI (XAI) suite delivering feature attribution, counterfactual analysis, and interactive visualizations for regulatory reporting.
- Machine unlearning module that selectively removes user data from trained models to satisfy “right to be forgotten” requests.
- Custom generative AI and autonomous AI‑agent pipelines built with fairness constraints and continuous monitoring for bias drift.