Most modern AI systems require users to upload sensitive information to external providers. For many organizations—and increasingly for individuals—that creates a trust problem.
Modern search tools create a difficult tradeoff: privacy vs functionality. Most solutions force users to choose one. Organizations cannot safely run cloud-based semantic search tools across proprietary documentation without risk of leakage. I wanted to explore whether both high-precision search and total user data ownership could coexist.
The question behind Argus was simple: Can modern AI-powered search remain useful without requiring users to surrender ownership of their data?
I designed and built a local-first retrieval system that processes, indexes, encrypts, and searches documents entirely on local hardware, requiring zero external APIs.
The system combines a background crawling daemon to parse documents asynchronously, a local FAISS similarity index, and direct Server-Sent Events to stream conceptual matches safely to the user client interface.
Parsing multi-gigabyte directories containing diverse document types requires I/O efficiency and resource containment. I developed the core ingestion daemon in Go, utilizing a bounded worker pool pattern to manage computational workloads.
To support semantic search queries (e.g. searching for conceptual ideas rather than exact text characters), document segments are transformed into dense embeddings. I integrated a localized embeddings pipeline mapping text to vectors, loaded directly into a custom FAISS (Facebook AI Similarity Search) index structure. High-dimensional vector calculations are executed in-memory, delivering semantic results in under 10 milliseconds.
To maintain strict cryptographic isolation, plain text document segments are never written directly to disk. The parsed texts are encrypted using AES-256 CTR (Counter) mode and matched with specific vector coordinate indices.
When a search is submitted, it is vectorized and sent to the local similarity index. FAISS returns the closest matching index IDs; the platform retrieves only the corresponding encrypted files, decrypts them in secure temporary memory buffers, and streams them instantly to the client interface using Server-Sent Events (SSE). Plain text fragments are never persisted, and users receive real-time, token-by-token visual feedback.
Utilizing low-latency, highly specialized systems languages and optimized mathematical engines to secure absolute privacy:
Argus demonstrated that high-quality semantic search does not require cloud infrastructure. The system achieved sub-10ms retrieval, zero cloud dependencies, encrypted storage, and a local-first architecture.
The project became less about search and more about a broader question: What would privacy-first AI infrastructure actually look like?