Services
Software algorithms protecting privacy during AI processing using homomorphic encryption and differential privacy techniques ensuring individual data points cannot be reverse-engineered from trained model outputs. Sub-services include differential privacy algorithm implementation, fully homomorphic encryption for AI inference, privacy-preserving ML development, and secure multiparty AI computation.
Implementation of fully homomorphic encryption (FHE) libraries like Microsoft SEAL or OpenFHE to enable AI inference and training directly on encrypted data, allowing regulated industries to use cloud AI without exposing sensitive raw information.
Integration of mathematically rigorous differential privacy mechanisms (e.g., Gaussian noise, Laplace mechanism) into AI training pipelines to guarantee that model outputs cannot reveal individual data points, ensuring compliance with GDPR and CCPA.
Development of distributed cryptographic protocols enabling multiple parties to jointly train AI models or perform inference on their combined datasets without any party seeing the others' raw data, ideal for cross-enterprise collaborations.
End-to-end development of machine learning pipelines that incorporate privacy-enhancing technologies (PETs) from data ingestion through model deployment, ensuring privacy by design for sensitive applications like healthcare diagnostics.
Architecture and deployment of scalable, low-latency inference endpoints that apply techniques like secure enclaves or on-premise deployment to process user data without storing or exposing it, critical for consumer-facing applications.
Specialized development of image and video analysis models that use techniques like federated learning on edge devices or encrypted inference to process biometric and surveillance data without creating centralized privacy risks.
Building text analysis and language models that protect conversational data, using methods such as on-device processing, encrypted embeddings, and private fine-tuning to comply with communications privacy laws.
Technical assessment and verification services to measure the privacy guarantees of existing AI systems, using tools like privacy loss accountants and attack simulations to ensure regulatory claims are defensible.
Integration of zk-SNARKs and zk-STARKs to allow AI systems to prove the correctness of an inference or a training step without revealing the underlying model weights or input data, enabling verifiable and private AI.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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