Your Chatbot Should Know Your Docs. We Make That Happen

A regular chatbot generates responses from an LLM's static training data, which has a knowledge cutoff and no access to your private information. A RAG chatbot retrieves relevant content from an external knowledge store at query time—such as your documents, databases, or APIs—before generating a response. This ensures answers are grounded in your current business data rather than the model's best guess.

For most business use cases, yes. Fine-tuning embeds knowledge into model weights, making updates expensive and difficult to audit. RAG keeps knowledge external and easily updatable—simply re-index a document and the chatbot immediately reflects the change. Fine-tuning is best for modifying a model's behavior or tone, while RAG is ideal for grounding responses in domain-specific and frequently updated knowledge.

Hallucination in RAG systems usually comes from retrieval misses (when relevant content is not found) or generation drift (when the model adds information not present in the retrieved context). We mitigate retrieval issues through retrieval tuning, hybrid search, and re-ranking techniques. We reduce generation drift with grounding prompts, faithfulness evaluations in CI/CD pipelines, and optional citation-enforced response formats.

For a well-scoped project with clean knowledge sources and a clearly defined integration target, the typical timeline is 6–8 weeks to the first production deployment. More complex requirements—such as multi-source ingestion pipelines, compliance controls, or custom front-end development—can extend the timeline. We provide an accurate estimate after completing the initial data audit.

Yes. For organizations with strict data privacy requirements, we can deploy fully self-hosted RAG stacks using open-source embedding models such as BGE or E5 and open-weight LLMs such as Llama 3 or Mistral. These solutions run on your cloud environment or on-premises infrastructure, ensuring that no data leaves your controlled environment.