RAG: answers from company knowledge
RAG (retrieval-augmented generation) first retrieves passages from the company's documents and databases, and only then writes an answer based on them. The model does not need to know that knowledge from training.
Service
AI agents, RAG and integrations with company systems
CrAIT designs AI agents and RAG solutions that work with company knowledge and data, not on the model's general knowledge. We build them together with integrations, secure APIs and answer-quality control.
Basics
RAG and AI agents — how they differ
Both use a language model, but they do different things. The choice depends on whether the company needs answers or actions.
AI agent: actions in systems
An AI agent does more than answer — it takes steps: pulls data from a system, updates a record, triggers a workflow. That is why it needs clearly defined permissions and human approval points.
Integrations
Company knowledge and data in one flow
Agents and RAG are only as good as the data they can reach. That is why we design integrations together with them, not afterwards.
Company systems
We connect CRM, ERP, documents, databases and APIs into one flow ready for AI and further automation.
Secure APIs
The agent gets access only to the data and operations its task requires.
Permissions
The permission model is built on the roles already in force in your source systems, so that access to knowledge through AI does not exceed what the user is authorised to access.
Audit trail
A record remains of what the agent did, based on which data, and who approved the result.
Quality
Answer-quality control
A language model can give an answer that is convincing and wrong at the same time. That is why we check quality before go-live and after it.
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01
Test questions
Together with the team we collect the questions and tasks the solution must handle.
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02
Sources
We check that each answer rests on the right documents and data.
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03
Pilot
The solution works with real users in a small, measurable scope.
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04
Monitoring
After go-live we track the agent's answers and actions in logs.
FAQ
Questions about this service
How is an AI agent different from a chatbot?
A chatbot answers questions, while an AI agent carries out tasks: it pulls data from systems, updates records and triggers workflows. That is why, for an agent, we design permissions, human approval points and an audit trail.
When should we choose RAG and when an AI agent?
RAG when the team needs accurate answers from the company's documents and knowledge bases. An agent when an answer must be followed by an action in a system. Work often starts with RAG, and an agent is added once the answers are reliable.
Which systems can an AI agent be integrated with?
Those that can be reached securely through an API or a data export: CRM, ERP, document repositories and databases. The diagnosis sets the integration scope — it shows which sources are needed immediately and which can wait.
Next step
Tell us what knowledge and which systems your AI agent should work with.
Write to CrAIT
We reply within 2 business days.