Enterprise Retrieval-Augmented Generation Systems
Wolk Inc designs and builds production RAG pipelines: document ingestion, embedding, vector search, LLM orchestration, and evaluation — grounded in your proprietary data, not generic training sets. No hallucinations. Auditable citations.
Zero
Hallucination Target
Private
LLM Deployment Options
RAGAS
Evaluation Framework
Hybrid
Dense + Keyword Search
RAG Pipeline Deliverables
Document Ingestion & Embedding Pipeline
End-to-end ingestion pipeline for PDF, Word, Confluence, Notion, Sharepoint, and custom data sources. Chunking strategy design (fixed, semantic, or hierarchical), metadata extraction, embedding model selection (OpenAI ada-002, Cohere, or open-source alternatives), and vector upsert with deduplication and incremental update support.
Vector Database Design & Query Architecture
Vector store selection and configuration: Pinecone, Weaviate, Qdrant, pgvector, or Milvus based on your data volume, latency requirements, and existing infrastructure. Hybrid search design combining dense vector search with BM25 keyword scoring for improved recall. Namespace or collection design for multi-tenant RAG applications.
LLM Orchestration & Prompt Engineering
LangChain or LlamaIndex orchestration layer design, retrieval chain configuration, context window management for long documents, system prompt engineering for factual grounding and citation behaviour, and tool-use integration for RAG systems that need to take actions (search, database queries, API calls) alongside retrieval.
Evaluation Framework & Quality Monitoring
RAG evaluation pipeline using RAGAS or custom metrics: context recall, context precision, faithfulness, and answer relevancy. A/B testing framework for chunking strategies and retrieval configurations. Production monitoring for latency, token cost, and retrieval quality drift over time as your document corpus grows.
RAG Stack Coverage
Production RAG. Measured Quality.
RAG Pipeline Questions
What is RAG and when should an enterprise use it?▾
Retrieval-Augmented Generation (RAG) is an architecture pattern where an LLM's responses are grounded in documents retrieved from your private knowledge base — rather than relying solely on the LLM's training data. Enterprises use RAG when they need an AI application to answer questions about proprietary content (internal documentation, contracts, product knowledge bases, customer communications) with accurate, auditable citations. RAG is the right choice when your data changes frequently, when hallucination risk is unacceptable, or when you cannot fine-tune a model on proprietary data.
How does Wolk Inc decide on chunking strategy and embedding model?▾
Chunking strategy depends on your document type and query patterns. Fixed-size chunking is simple and works well for structured documents. Semantic chunking (splitting at topic boundaries) improves retrieval precision for narrative documents. Hierarchical chunking (parent + child chunks) is useful when queries sometimes need broad context and sometimes need specific passages. Embedding model selection depends on your language requirements, latency budget, and whether you need on-premises deployment. Wolk Inc runs a small benchmark on your actual documents before committing to a configuration.
Should we use a managed vector database (Pinecone) or self-hosted (Qdrant, pgvector)?▾
Pinecone and Weaviate Cloud are faster to deploy and require less operational overhead — good choices for teams without dedicated ML infrastructure. Qdrant and Milvus are self-hosted options with lower per-query cost at scale and stronger data sovereignty properties. pgvector is the right choice for teams already on PostgreSQL with moderate vector search requirements (under ~5M vectors) who want to avoid an additional datastore. Wolk Inc recommends based on your data volume, query throughput, compliance requirements, and operational capacity.
How do you evaluate whether a RAG system is working correctly?▾
Wolk Inc implements a RAGAS evaluation framework measuring four key metrics: (1) Context Recall — whether the retrieved chunks contain the information needed to answer the question; (2) Context Precision — whether retrieved chunks are relevant (not noisy); (3) Faithfulness — whether the LLM answer is grounded in the retrieved context; (4) Answer Relevancy — whether the answer actually addresses the query. These metrics run against a golden question dataset assembled from your domain experts. We set up a Langsmith or W&B Traces dashboard for ongoing production monitoring.
Can Wolk Inc build a RAG system that works with a private LLM (no data sent to OpenAI)?▾
Yes. Wolk Inc builds RAG systems on private LLM deployments: Llama 3, Mistral, or Mixtral hosted on AWS Bedrock (custom model), Azure OpenAI (with your own deployment), GCP Vertex AI, or self-hosted via vLLM or Ollama for air-gapped environments. Private LLM deployments require more compute planning and typically have lower throughput than OpenAI API — we include a capacity model and latency benchmark in the architecture proposal.