Clinical documents · Semantic search · Grounded AI
TietAI Agentic RAG turns clinical documents, guidelines, web sources, and knowledge graphs into a governed retrieval layer for healthcare AI.
Teams can organize libraries, process content into searchable evidence, retrieve the most relevant passages, and generate answers with source attribution and conversation context.
multi-format document ingestion
semantic vectors plus keyword retrieval
RAG responses with source attribution
PubMed, NICE, WHO, ClinicalTrials.gov, RSS, and web pages
The document layer manages upload, organization, processing, metadata, deduplication, archiving, and external sources so retrieval starts from clean knowledge assets.
Group clinical documents into libraries by team, pathway, disease area, project, or customer so retrieval stays scoped and relevant.
Extract text, split content into chunks, generate embeddings, and prepare documents for search after upload.
Detect repeated content by organization so teams avoid indexing the same clinical document multiple times.
Attach custom metadata to documents for filtering, governance, reporting, and retrieval context.
Archive or remove documents while keeping knowledge libraries manageable as clinical guidance changes.
Bring in curated external knowledge from medical databases, guidelines, clinical trial registries, RSS feeds, and web pages.
The retrieval layer combines semantic similarity, keyword search, source tracking, memory, and knowledge graph context so AI can answer with evidence instead of guesses.
Use vector similarity to find clinically relevant passages even when the query and source documents use different wording.
Combine semantic search with keyword matching for precise clinical concepts, drug names, procedure terms, and guideline language.
Generate responses from retrieved evidence and include the sources used so clinicians and operators can inspect the grounding.
Support multi-turn workflows where users refine questions without losing the context of the previous exchange.
Manage token limits and retrieved evidence so answers stay focused on the most useful passages.
Extract diseases, drugs, symptoms, and procedures, then map relationships such as treats, causes, prevents, and diagnosed by.
Agentic RAG is designed for teams that need secure access, organization-level isolation, compliance readiness, monitoring, and extensibility.
Keep documents, libraries, retrieval results, and knowledge graph data separated automatically by organization.
Support authenticated access through bearer tokens, API gateway authentication, and signed gateway requests.
Align document retrieval workflows with HIPAA and GDPR expectations for protected healthcare content.
Monitor upload latency, vector search time, RAG response time, embedding generation, and document processing failures.
Run document intelligence from development to production with cache, queue, vector database, and graph database support.
Extend the platform with custom source connectors for hospital repositories, internal guidelines, or specialty databases.
Teams upload documents or connect external sources. The service validates files, extracts text, chunks content, generates embeddings, and optionally extracts medical entities into a graph.
When a user asks a question, Agentic RAG retrieves the best passages, combines semantic and keyword evidence, returns cited answers, and keeps context across the workflow.
Give care teams, researchers, operators, and AI agents a governed retrieval layer for clinical documents and medical knowledge.
