We construct private AI systems that process media streams, extract document data, and search corporate archives without exposing sensitive data.
Chandraja Infotech maintains operational teams delivering state-of-the-art results in this domain. We work closely with our clients to customize architectures, normalize database entities, and ensure robust security audits matching enterprise benchmarks.
We deploy local vector databases and run models within isolated VPC environments, preventing sensitive corporate documentation from being sent to public AI training sets.
Retrieval-Augmented Generation (RAG) is a technique that queries private company documents to provide context to a LLM, ensuring accurate responses without hallucinations.
Yes, we optimize lightweight machine learning models (like TensorFlow Lite and custom OpenCV filters) to execute directly on edge gateways or mobile hardware.
Request a design proposal from our system architects. We supply structured estimates, capability matrices, and architectural briefs.
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