Build AI systems that actually know your data. Retrieval-Augmented Generation combines LLMs with your documents for accurate, grounded responses.
RAG bridges the gap between powerful LLMs and your proprietary knowledge, creating AI that truly understands your domain.
Ground AI outputs in your actual data, dramatically reducing hallucinations and improving factual accuracy.
Unlike fine-tuned models, RAG systems can access the latest information without retraining.
Keep sensitive data in your infrastructure while still leveraging powerful LLMs for generation.
Avoid expensive fine-tuning and reduce token usage by retrieving only relevant context.
Cite sources and show users exactly where information comes from, building trust.
Get to production faster than fine-tuning approaches with flexible, iterative development.
We build every component of your RAG system, from document ingestion to response generation.
Ingest, chunk, and prepare your documents for semantic search
Convert text to vector representations for similarity search
Store and query embeddings at scale with millisecond latency
Find the most relevant context for each query
Combine retrieved context with user queries effectively
Generate accurate, grounded responses with citations
Get a free consultation and architecture review for your RAG project.
RAG powers a wide range of AI applications that need accurate, domain-specific knowledge.
Let users ask natural language questions about your documentation, policies, or internal knowledge.
Extract insights, summarize, and answer questions about contracts, reports, and legal documents.
Go beyond keyword matching to understand user intent and find truly relevant results.
Build copilots that understand your specific domain and provide contextual help.
We work with the best tools in the RAG ecosystem, selecting the right combination for your needs.
LLM Provider
LLM Provider
Vector DB
Vector DB
Vector DB
Vector DB
Framework
Framework
Re-ranking
Document Processing
Framework
Embeddings
A proven methodology for building production-ready RAG applications.
We analyze your data sources, use cases, and requirements to design the optimal RAG architecture.
Build robust pipelines to ingest, chunk, and embed your documents with the right strategies.
Optimize retrieval for accuracy with hybrid search, re-ranking, and metadata filtering.
Integrate LLMs with optimized prompts for accurate, well-cited responses.
Launch with monitoring, feedback loops, and continuous improvement systems.
Free Consultation
Talk to our AI team today. We'll review your data and provide a RAG architecture recommendation within 48 hours.
Common questions about building RAG systems and retrieval-augmented generation.
RAG (Retrieval-Augmented Generation) combines the power of large language models with your own data. When a user asks a question, the system first searches your documents to find relevant information, then includes that context in the prompt to the LLM. This grounds the response in your actual data rather than relying on what the model was trained on.
Fine-tuning trains a model on your data, which is expensive, slow, and creates a snapshot that can become outdated. RAG keeps your data separate and retrieves it at query time, meaning updates are instant, costs are lower, and you maintain full control over your data. RAG is typically the better choice for knowledge bases and Q&A systems.
RAG systems can process virtually any text-based content: PDFs, Word documents, web pages, Notion, Confluence, Google Docs, code repositories, emails, chat logs, and more. We also support semi-structured data like CSVs and tables. For images and scanned documents, we use OCR to extract text.
RAG significantly improves accuracy over base LLMs by grounding responses in your data. However, accuracy depends on retrieval quality, chunking strategy, and prompt design. We implement citation systems so users can verify sources, and confidence scoring to flag uncertain responses. Well-tuned RAG systems achieve 85-95% accuracy on domain-specific questions.
Your data never leaves your control. We can deploy vector databases in your own infrastructure, use private LLM endpoints, and implement encryption at rest and in transit. For sensitive industries, we offer fully air-gapped solutions using open-source models that run entirely within your environment.
A basic RAG system with a single data source can be built in 4-6 weeks. More complex implementations with multiple data sources, advanced retrieval, and custom interfaces typically take 2-4 months. We recommend starting with an MVP to validate the approach before expanding.
RAG systems need regular attention: keeping document embeddings updated as content changes, monitoring retrieval quality, updating prompts as use cases evolve, and managing vector database performance. We offer maintenance packages or can train your team to handle these tasks.
Costs depend on complexity, data volume, and infrastructure choices. Beyond development, factor in ongoing costs for vector database hosting, LLM API calls, and embedding generation. We provide detailed cost projections including both build and run costs. Contact us for a custom estimate based on your requirements.
Let's build an AI system that truly understands your business and delivers accurate, grounded responses.