RAG consultant for a company: how to launch an AI knowledge base and reduce the load on the team

When the volume of regulations, instructions, contracts and correspondence increases in a company, employees begin to spend too much time searching for an answer. At this point, a standard chatbot can no longer cope: it may sound confident, but it does not always rely on real company documents.

A RAG consultant solves this problem differently. It first searches for relevant snippets in your knowledge base, then generates an answer based on the sources found. Thanks to this, the number of errors is reduced and user confidence in the AI ​​assistant increases.

If you plan to launch such a circuit in production, it is more convenient to go through a separate service creating an AI consultant with RAG, where infrastructure, security and integration are already taken into account.

Where a RAG consultant gives maximum effect

In practice, scenarios where the team has a lot of repetitive questions pay off the fastest:

• first line support and service desk;
• onboarding of new employees;
• internal sales regulations and document flow;
• product answers for account managers;
• assistance to call center operators.

The main selection criterion: the question must have support in the source being verified. If the answer is not in the company’s data, the RAG consultant should correctly report this, and not “make it up.”

What components does the working RAG circuit consist of?

The basic architecture typically includes five layers:

1. Sources of knowledge: CRM, Wiki, instructions, PDF, ticket database, internal portal.
2. Data preparation: cleaning, deduplication, chunking, metadata enrichment.
3. Indexing: vector index + keyword search (hybrid mode).
4. LLM layer: generating a response based on found documents.
5. Quality control: logs, relevance assessment, list of “red” scenarios.

For companies with high compliance requirements, such an architecture is usually deployed in a secure loop. This scenario goes well with Out-of-the-box installation and AI configuration, if you need to keep data inside the company's infrastructure.

What KPIs to set for a pilot?

A common mistake is to look only at the “beautifulness” of the answer. Measurable metrics are more important for business:

• proportion of issues closed without escalation per person;
• average time to receive a correct answer;
• reducing the load on the first line of support;
• error rate for critical scenarios;
• NPS/CSAT on interaction with assistant.

In a pilot, it is useful to fix a target range for 4-6 weeks: for example, reducing operator workload by 20-35% with a controlled error rate.

Why projects fail

Even with a good model, implementation often stalls due to organizational reasons:

• a “raw archive” without structure is loaded into the knowledge base;
• do not identify knowledge owners and the updating process;
• do not share access rights and sensitive data;
• do not configure fallback for cases when the source is not found;
• do not implement quality control and error analysis.

Technically, this can be treated, but it is cheaper to lay down these requirements before the pilot starts.

Launch plan in 6 weeks

1. Define 20-30 typical question scenarios and quality criteria.
2. Prepare priority knowledge sources and cleanse data.
3. Set up hybrid search and primary response generation.
4. Conduct a test on a control sample of requests.
5. Connect CRM/helpdesk and implement a user interface.
6. Run a pilot on a limited group and collect metrics.

This approach allows you not to “do AI for AI’s sake,” but to get a controlled product that saves the team’s time and improves customer service.

What to do after the pilot

After the first results, you should go to scaling:

• expand sources of knowledge on the priority of business functions;
• launch separate prompt profiles for employee roles;
• connect SLA, monitoring and regular quality audits;
• create an internal standard for working with an AI consultant.

If you need to evaluate a project for your infrastructure (on-premise or cloud), they usually start with an architectural session and a risk map.

FAQ

Is a RAG consultant only suitable for support?
No. It is also useful for sales, internal training, legal and operational functions.

Is it possible to run RAG with Russian and foreign LLMs at the same time?
Yes, the hybrid circuit allows you to choose a model based on the type of request, cost and safety requirements.

How long does the first launch usually take?
The pilot most often takes 3-6 weeks with ready-made data sources and an agreed-upon architecture.