Knowledge Systems
Intelligent retrieval and Q&A systems built on your organization's documents, data, and expertise.
We build RAG (Retrieval-Augmented Generation) systems that let your teams and customers find, query, and interact with your knowledge. From internal wikis to customer-facing documentation search, we design systems that understand your content.
What this covers
RAG Architecture
Design retrieval pipelines with chunking strategies, embedding models, and reranking optimized for your content type.
Enterprise Search
Build search systems that span multiple document sources with access controls that respect your security model.
Document Q&A
Create interfaces that let users ask natural language questions and receive precise, sourced answers from your documents.
Knowledge Graph Integration
Combine vector search with structured knowledge graphs for more accurate, context-aware retrieval.
Quality & Evaluation
Measure retrieval quality, answer accuracy, and hallucination rates with systematic evaluation frameworks.
Content Management
Build ingestion pipelines that keep your knowledge system current as documents change and grow.
Frequently asked questions
What types of content can a knowledge system handle?
Documents, PDFs, wikis, code repositories, support tickets, product documentation, internal policies — essentially any text-based content. We can also incorporate structured data from databases and APIs.
How accurate are RAG systems?
A well-designed RAG system with quality chunking, appropriate embedding models, and reranking can achieve high relevance. But no system is 100% accurate. We build in source attribution and confidence indicators so users can verify answers.