RAG & Custom AI Assistants

A generic AI assistant that does not know your business is not useful. It hallucinates, gives generic answers, and cannot access the information your team actually needs.

A well-built RAG system changes that. It gives AI grounded access to your documents, databases, and internal knowledge, so the answers are accurate, traceable, and specific to your operations.

What We Build

Internal Operations Assistants

• HR policy and employee handbook assistants
• Legal document search and summarization
• Compliance and regulatory reference tools
• Support team knowledge base assistants

Customer-Facing AI

• Website and product assistants grounded in your actual documentation
• Support ticket deflection with accurate, source-cited answers
• Onboarding guides that adapt to user context

Document Processing Pipelines

• Ingest contracts, reports, SOPs, and structured data
• Extract, summarize, and classify at volume
• Continuous ingestion as new documents arrive

What "Proper" RAG Means

Most RAG implementations fail because of chunking. If documents are split incorrectly, retrieval returns the wrong context and the AI gives wrong answers. We build RAG with:

• Semantic chunking: splits on meaning, not character count
• Metadata filtering: queries retrieve from the right subset of documents
• Hybrid retrieval: combining vector search with keyword search for better recall
• Source citation: every answer includes the document and passage it came from
• Evaluation pipelines: we test retrieval accuracy before you go to production

When You Need This vs. a General LLM

If you are asking an LLM questions about your own business data and getting generic or hallucinated answers, you need RAG, not a better prompt. If the information lives in documents, databases, or internal systems, it needs to be indexed, not guessed.

Stack

• Vector Databases: Pinecone, Weaviate (selected based on scale and query patterns)
• Embedding Models: OpenAI, Cohere, or open-source (e.g., BGE) for self-hosted
• LLMs: Claude, GPT-4o, Gemini, selected for accuracy and cost profile
• Frameworks: LangChain, LlamaIndex
• Data Ingestion: PDF, Word, HTML, structured databases, APIs
• Infra: Cloud or self-hosted, including air-gapped for sensitive environments