RAG Development Services That Turn Company Knowledge Into Accurate Answers

Retrieval-augmented generation (RAG) connects a language model to your own content, so it looks up relevant passages before answering. EnactOn's RAG development services cover source audits, data pipelines, retrieval design, permissions, evaluation, and deployment, producing assistants and search tools that answer from your verified knowledge with citations.

Fine-tuning changes how a model writes, while RAG changes what it knows at answer time. RAG is usually faster and cheaper to keep current, since updating a document updates the answers. EnactOn often recommends RAG first and adds fine-tuning only when tone or format needs it.

It starts with mapping the questions customers or staff ask, then connecting the chatbot to approved sources such as help articles, policies, and product data. EnactOn's RAG chatbot development adds citations, handoff to human agents, and logging of unanswered questions, so the bot improves as it learns where content is missing.

Yes. EnactOn syncs access rules from sources such as SharePoint, Confluence, and Google Drive, and filters results before the model sees them. Each user only receives answers drawn from documents they are already allowed to open, which keeps confidential content protected.

Accuracy depends on content quality, retrieval design, and testing. Our team builds a golden set of real questions with approved answers, scores every release against it, and tracks faithfulness and citation rates in production. When EnactOn's systems cannot find support in your content, they say so instead of guessing.

Projects with EnactOn start at $5,000 for a proof of concept on one knowledge source, with production applications and enterprise platforms costing more. Model, embedding, and vector database usage are billed separately by those providers, and every quote estimates both so running costs are predictable.

A proof of concept on one source typically takes three to five weeks. Production applications with integrations and permissions usually take six to ten weeks, and multi-source enterprise AI knowledge systems take a few months. EnactOn rolls out to pilot users first, so value appears early.

Choices follow your needs, not a fixed stack. EnactOn works with models such as GPT, Claude, Gemini, and open-source options, vector databases such as Pinecone, Weaviate, Qdrant, and pgvector, and frameworks such as LangChain and LlamaIndex, all designed so components can be swapped later.

Look for proof of production systems rather than demos, a clear evaluation method, permission-aware retrieval, and transparent running-cost estimates. EnactOn meets each point, with a knowledge platform that has handled around 71,000 questions and weekly quality reporting on every build.

You do. EnactOn deploys in your cloud or ours as you prefer, and hands over code, pipelines, prompts, evaluation sets, and documentation. Your documents and embeddings remain your data, and you can extend the system in-house or keep our team for ongoing improvement.