Sathus AI 2.0 is now generally available — evaluation harnesses and guardrails included. Explore
Ground LLMs in confidential enterprise documents with hybrid dense/sparse vector search, semantic reranking, and zero-hallucination guardrails.
Common obstacles that prevent enterprises from realizing the full value of AI initiatives.
Simple vector search misses domain context, retrieves irrelevant chunks, or exposes unauthorized records.
Core capabilities that enable production-grade AI systems with enterprise governance.
Combine keyword precision (BM25) with semantic vector embeddings (Qdrant/Milvus).
Re-rank retrieved context using domain-tuned cross-encoders before prompt injection.
Document parser, semantic chunker, hybrid vector DB, Cohere reranker, security filter, and LLM response generator.
The tools and platforms we use to deliver production-grade AI systems.
RAG engineering framework.
Configure custom chunking rules for PDFs, CAD files, and tabular data.
Evaluate OpenAI, Cohere, and open-source embedding models against domain data.
Enforce document-level permission filters inside vector queries.
Measurable results that drive enterprise value from AI initiatives.
Precision context retrieval across complex technical manuals.
98%
Real-world examples of how we've delivered AI solutions for enterprise clients.
HealthNet Global • Healthcare • 5 Months
Physicians spent 20+ minutes searching un-indexed EHR charts during patient visits.
HIPAA-compliant RAG pipeline with Qdrant vector search and fine-tuned embeddings.
Reduced chart review time to under 30 seconds with 98% factual precision.
Common questions about our AI Engineering practice and approach.
We embed user group permissions directly into vector payloads, filtering out unauthorized document chunks before context is ever sent to the LLM.
Let's discuss how our engineering practices can accelerate your business outcomes.
