RAG Development Services for Enterprise Knowledge

Our RAG development services give your team a private ChatGPT for business — an enterprise knowledge assistant that lets people chat with company documents, policies and databases. Retrieval-augmented generation (RAG) grounds every answer in your own content, cites the source it came from, and respects who is allowed to see what.

The Problem

What We Usually See

Your company's knowledge is scattered across PDFs, wikis, spreadsheets and old email threads. New employees can't find answers, and even experienced staff waste time searching instead of working.

Our Solution

How Our RAG Development Services Work

We build retrieval-augmented assistants that index your internal documents and data sources, then answer questions in plain language — always grounded in your actual content, with sources cited, and respecting your existing access controls.

Key Features

What's Included

Secure indexing of internal documents, wikis and databases

Source-cited answers — no hallucinated facts

Role-based access control respecting your existing permissions

Continuous re-indexing as your documents change

Deployable internally (Slack, Teams, intranet) or customer-facing

Use Cases

Where This Fits in a Real Business

Internal policy and HR Q&A assistants

Engineering knowledge bases for faster onboarding

Customer-facing product documentation assistants

Compliance and audit document search for regulated industries

Deliverables

What You Walk Away With

A deployed knowledge assistant, an indexing pipeline for your document sources, access-control configuration, and usage analytics.

Tech Used

Built On

RAG architectureVector databasesLangChainClaudeOpenAI
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Common Questions

Questions About Our RAG Development Services

Public chatbots don't know your internal documents and can invent answers. RAG first searches your own approved content, then asks the AI model to answer using only what it found — with links to the source — so answers are specific and checkable.

No. Your documents are indexed in a store you control, and we use AI providers and settings that do not train on your data. Retrieval happens at question time; nothing is added to a public model.

PDF, Word, Excel, PowerPoint, web pages, wikis and help centres, shared drives such as Google Drive or SharePoint, and databases — kept up to date with scheduled re-indexing.

We ground answers in retrieved passages, show citations, tune the assistant to say 'I don't know' when sources are missing, and test it against a set of real questions before launch.

Yes. The assistant can respect your existing permissions, so a person only gets answers from documents they are already allowed to open.

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