We build retrieval-augmented generation systems that give LLMs accurate, up-to-date knowledge from your documents, databases, and internal tools.
Large language models are powerful but limited by their training data. They cannot access your company documents, internal policies, product specifications, or real-time data. Retrieval-augmented generation bridges this gap by fetching relevant information from your data sources and providing it as context to the LLM at query time. The result is responses that are accurate, current, and grounded in your specific knowledge base rather than generic training data.
RAG has emerged as the most practical approach to building AI systems that work with proprietary data. Unlike fine-tuning, which requires retraining models on your data at significant cost, RAG lets you update the knowledge base simply by adding or modifying documents. A well-built RAG pipeline can answer questions about content that was added minutes ago, making it ideal for dynamic environments where information changes frequently.
Arthiq has built RAG pipelines for applications ranging from customer support knowledge bases to legal document research tools. Our experience with our own products, particularly InvoiceRunner and AgentCal, has given us deep practical knowledge of what makes RAG systems succeed or fail in production. We bring this operational expertise to every client engagement.
The quality of a RAG system depends almost entirely on its retrieval step. If the system fetches irrelevant documents, even the most capable LLM will produce poor answers. Arthiq invests significant effort in retrieval architecture design, including chunking strategies that preserve semantic meaning, embedding model selection tuned to your domain, and hybrid search approaches that combine semantic similarity with keyword matching for maximum recall.
We implement advanced retrieval patterns including multi-stage retrieval where an initial broad search is followed by a reranking step using cross-encoder models. For complex queries that span multiple topics, we use query decomposition to break the original question into sub-queries, retrieve relevant documents for each, and synthesize a comprehensive answer. These techniques significantly outperform basic single-query retrieval, especially for nuanced questions.
Our architectures also address the practical challenges of document ingestion. We build robust data pipelines that extract text from PDFs, Office documents, HTML pages, and structured databases, handle deduplication and versioning, and maintain metadata that enables filtered retrieval. When your source data updates, our incremental ingestion processes ensure the knowledge base stays current without full reindexing.
The vector database is the backbone of any RAG system. Arthiq has production experience with Pinecone, Weaviate, Qdrant, Chroma, and PostgreSQL with pgvector, and we select the right solution based on your scale, latency requirements, filtering needs, and infrastructure preferences. For cloud-native deployments, managed services like Pinecone offer the fastest path to production. For teams that need full control over their data, self-hosted options like Qdrant or Weaviate provide that flexibility.
We optimize vector database performance through careful index configuration, appropriate distance metrics, and query tuning. For large knowledge bases with millions of documents, we implement sharding strategies and approximate nearest neighbor algorithms that maintain sub-100ms query times. We also design hybrid storage architectures where vectors live in a specialized database while full document content resides in a traditional data store, reducing storage costs without sacrificing retrieval quality.
Beyond storage, we build the operational infrastructure around the vector database: monitoring for index health and query performance, automated backup and recovery procedures, and data governance controls that ensure sensitive documents are only retrievable by authorized users.
Building a RAG pipeline is only the beginning. Measuring and improving its performance is an ongoing process. Arthiq implements systematic evaluation frameworks that test retrieval relevance, answer accuracy, and faithfulness to source documents. We use both automated metrics and human evaluation to identify weaknesses in the pipeline and prioritize improvements.
Common issues we diagnose and resolve include retrieval gaps where relevant documents are missed, answer hallucinations where the LLM generates information not present in retrieved context, and context window overflow where too many documents are retrieved and the LLM struggles to synthesize them effectively. Each issue has specific technical solutions that we apply based on empirical analysis.
We also implement feedback loops where end users can flag incorrect or unhelpful responses. These signals feed into a continuous improvement process where we adjust chunking strategies, refine embedding models, update reranking configurations, and improve prompts. Over time, this data-driven approach compounds into a system that gets measurably better at serving your users.
RAG is not a weekend project. Building a system that delivers accurate, reliable answers at scale requires deep expertise across embeddings, vector databases, retrieval algorithms, and LLM prompt engineering. Arthiq brings proven experience across all of these domains, informed by real production deployments rather than theoretical knowledge.
We deliver RAG pipelines in focused engagements that start with a data audit and architecture design, proceed through iterative development with regular quality benchmarks, and conclude with production deployment and monitoring setup. Our clients see measurable improvements in answer quality within weeks, not months.
Contact our team at founders@arthiq.co to discuss how a RAG pipeline can transform your data into an intelligent, queryable knowledge system.
Our team will design and deploy a RAG pipeline that transforms your documents and data into intelligent, queryable knowledge that your team and customers can access instantly.