The startup provides an AI platform that embeds engineered safety protocols—automated alignment scoring, red‑team testing, and programmable kill‑switches—directly into model development cycles to keep autonomous agents aligned with human intent. It also offers a federated learning SDK for secure on‑device model updates with differential privacy, and a modular edge runtime that leverages chiplet‑based and neuromorphic accelerators for sub‑10 ms latency and high energy efficiency. Standardized APIs, provenance metadata, and compliance dashboards enable seamless CI/CD integration and automated regulatory reporting.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying autonomous AI agents face high risks of unintended behavior, hallucinations, and security exploits, while strict data‑privacy regulations limit the use of centralized training pipelines. Additionally, conventional monolithic AI hardware struggles to meet the latency and energy efficiency demands of edge deployments.
Solution
The company delivers an end‑to‑end AI platform that embeds engineered safety protocols directly into model development cycles, providing automated evaluation, red‑team testing, and kill‑switch mechanisms to keep agent behavior aligned with human intent. It couples this safety layer with a federated learning framework that enables on‑device model updates across smartphones, industrial controllers, and hospital networks, preserving data locality and reducing inference latency. The platform also offers a modular hardware abstraction that leverages chiplet‑based accelerators and neuromorphic inference engines, allowing customers to scale compute performance while minimizing power consumption. All components expose standardized APIs and provenance metadata, facilitating seamless integration into existing CI/CD pipelines and compliance reporting tools.
Target Audience
Primary customers are enterprises building autonomous agents—such as autonomous vehicle manufacturers, robotics firms, and regulated sectors like healthcare and finance—that require safety‑engineered AI, privacy‑preserving training, and high‑efficiency edge compute.
Features
- Automated safety pipeline with continuous alignment scoring, adversarial red‑team simulations, and programmable kill‑switch controls
- Federated learning SDK supporting secure aggregation, heterogenous data schemas, and on‑device differential privacy guarantees
- Edge runtime optimized for sub‑10 ms inference, with dynamic model partitioning across heterogeneous compute resources
- Chiplet integration toolkit that abstracts mixed‑die accelerators (compute, memory, analog) for rapid hardware customization
- Neuromorphic inference engine designed for sparse, event‑driven workloads, delivering up to 10× energy efficiency over GPU baselines
- Transparent model provenance ledger tracking dataset lineage, weight versions, and dependency graphs for auditability
- RESTful and gRPC APIs for model deployment, monitoring, and policy enforcement across cloud and edge environments
- Compliance dashboard generating GDPR, HIPAA, and industry‑specific audit reports automatically