CAPABILITY
Grounded AI systems with retrieval, citations, and operational discipline
Inference Stack builds retrieval-augmented generation systems that help organizations unlock institutional knowledge without surrendering answer quality, traceability, or operational rigor. We design the full retrieval lifecycle: ingestion, chunking, embeddings, vector storage, hybrid retrieval, reranking, answer grounding, and evaluation.
The result is not simply "chat over documents," but a production-ready knowledge system that can support internal users, external users, and workflow-specific AI experiences with greater precision and trust.
What this capability includes
Document ingestion pipelines
Chunking and normalization strategy
Embeddings and vector indexing
Metadata-aware retrieval
Hybrid search
Reranking
Citation-aware answer generation
Evaluation harnesses for retrieval quality
What we deliver
Enterprise knowledge assistants
Retrieval-backed Q&A systems
Grounded decision-support interfaces
RAG foundations reusable across multiple products and teams
Retrieval systems instrumented for tuning and governance
Enterprise considerations we address
Source freshness
Chunking quality
Permission-aware access patterns
Vector store design choices
Metadata schema design
Reranking and recall quality
Hallucination reduction through grounding
Evaluation and regression detection
Typical implementation patterns
Pipeline-based ingestion
Embedding abstraction
Pinecone or PostgreSQL/pgvector retrieval layers
Hybrid search and metadata filtering
Retrieval trace logging
Citation rendering
Answer confidence and escalation patterns
Related technologies
Need retrieval systems that answer with grounding, not guesswork?
Inference Stack designs RAG and knowledge systems with the architectural rigor, retrieval quality, and runtime visibility required for real enterprise trust.