AI Services
RAG Knowledge Base Systems
Knowledge systems that let AI answer from your documents, policies, research, tickets, databases, and internal expertise with citations and controls.
What's Getting in the Way
Company knowledge is usually scattered across docs, PDFs, support tickets, wikis, cloud drives, databases, and people. Generic search is not enough, and generic chatbots hallucinate when the answer depends on source material. A RAG system needs retrieval design, document processing, permissions, evaluation, and maintenance.
- Teams cannot quickly find accurate answers across internal documents and systems
- Generic AI tools do not respect your sources, access rules, or citation needs
- Document collections are messy, duplicated, outdated, or hard to retrieve from
- No one knows whether the system is retrieving the right context or making things up
What You Get
Knowledge Source Audit
We inventory documents, systems, formats, permissions, freshness, and retrieval requirements.
Document Processing Pipeline
We design ingestion, chunking, metadata, cleaning, updates, and source tracking for your knowledge base.
Retrieval and Answer System
We build the search and generation flow so the system retrieves relevant context and answers with source grounding.
Citations, Permissions, and Guardrails
We add source links, access rules, fallback behavior, and limits for cases where the knowledge base cannot answer.
Retrieval Evaluation
We test whether the system finds the right sources, answers accurately, and fails safely when information is missing.
The Process
Audit the Knowledge
We review the documents, systems, users, access rules, and answer types the system must support.
Design Retrieval
We define chunking, metadata, indexes, source filters, permissions, prompts, and evaluation examples.
Build the System
We implement ingestion, search, answer generation, citations, and integrations into the intended interface.
Evaluate and Handover
We test retrieval quality, document the update workflow, and hand over the system to your team.
Pricing
$5K-$15K
RAG Knowledge Base Systems
Timeline: 3-6 weeks
Final price depends on scope. We confirm exact pricing after a free scoping call.
Scope your knowledge systemQuestions About This Service
What does RAG mean?
Retrieval-augmented generation. It lets an AI system retrieve relevant source material before answering, instead of relying only on the model memory.
What sources can be used?
PDFs, docs, websites, Notion, Google Drive, help desk articles, support tickets, databases, and custom APIs, depending on access.
Can it cite sources?
Yes. Source citation is a core part of the build when users need trustworthy answers.
Can it respect permissions?
Yes. Permission handling depends on your source systems, but access control is part of the architecture when needed.
How do we keep the knowledge base current?
We define an update pipeline and operating process so new or changed knowledge can be ingested reliably.
Often Combined With
Let's build it.
Tell us your context. We'll scope it in one call.