Autogeneration of Support Responses: LLM and Agent Assist

A support operator handles 500 similar requests per day. Average response time is 4 hours, CSAT is dropping, and the team is burning out. LLM autogeneration of responses solves this: we implement a system that either fully answers standard questions or suggests a ready response to the operator. The second mode—agent assist—provides a 40-60% reduction in response time without losing quality. According to a study by Gartner, companies that implemented agent assist reduced request processing time by an average of 45%. With a flow of 500 requests per day, payroll savings can reach up to $1.6k–2.3k per month (10 operators with an average salary of $540–780 each).

We have completed 30+ projects on autogeneration for banks, retail, and telecom. We guarantee: acceptance rate >60%, response time reduction from 40%, and CSAT not lower than manual responses.

How does agent assist work?

We use a RAG architecture: the LLM (GPT-4o, LLaMA 3, or Mistral) is augmented with a vector knowledge base (Pinecone, Qdrant) containing historical resolved tickets and articles. The system operates in two modes:

• Fully automatic responses — for requests with a classifier confidence >0.95. Safe only for trivial cases: order status, working hours, confirmations.
• Agent Assist — the LLM generates a suggestion, and the operator accepts, edits, or rejects it with one click. Suitable for 70-80% of inquiries, including complex ones.

Agent assist is more effective than full automation because the operator stays in the loop: they check facts, add empathy, and adjust tone. Full automation without control carries a risk of reputational loss due to hallucinations. Agent assist provides 40-60% time savings with a CSAT level matching manual responses.

Example code: Agent Assist in Python

def suggest_response(ticket: Ticket, knowledge_base: VectorStore) -> ResponseSuggestion:
    # Search for similar resolved tickets
    similar_tickets = knowledge_base.search(ticket.text, top_k=3)
    # Search in knowledge base
    kb_articles = knowledge_base.search_articles(ticket.text, top_k=3)
    # Generate suggestion
    prompt = f"""
    Customer inquiry: {ticket.text}
    Customer history: {ticket.customer_history}
    Similar resolved inquiries: {format_similar(similar_tickets)}
    Knowledge base articles: {format_articles(kb_articles)}
    Write an operator response: polite, to the point, with a concrete solution."""
    return llm.generate(prompt)

Comparison of modes: automatic vs assistant

Metrics comparison before and after implementation

Why RAG is better than direct LLM call?

Without RAG, the LLM generates a response based only on training data—this gives a high risk of hallucinations on company-specific questions. RAG adds context: it retrieves relevant documents from the knowledge base and passes them into the prompt. This ensures the response is based on facts, not generalities. We use vectorization (1536-dim) and re-ranking to increase accuracy.

How we implement autogeneration: stages

1. Analysis of the inquiry flow — collect statistics: top 20 topics, language, complexity. Determine which requests can be automated. Use a BERT-based classifier.
2. Collecting and preparing the knowledge base — consolidate ticket history, FAQ, articles. Clean, label, create embeddings (1536-dim). Typical base size is 5000+ documents.
3. Configuring the RAG pipeline — choose a model (GPT-4o for SaaS, LLaMA 3 for on-prem), configure the retriever (top_k=5), prompt with instructions. Add relevance ranking.
4. A/B testing — run agent assist on 10% of flow, compare with control group. Metrics: response time, CSAT, acceptance rate (target >60%). After 2 weeks, scale up.
5. Deployment and monitoring — deploy in Docker/Kubernetes, connect monitoring (p99 latency, FLOPS, GPU utilization). Set alerts for quality drops.

What is included in the work

• Architectural document — solution description, model selection, integration scheme.
• Integration with CRM/ticketing system — REST API, ready connectors to Zendesk, Freshdesk, Bitrix24.
• Operator training — 2-hour session: how to work with hints, what to do in case of errors.
• Monitoring and SLA — Grafana dashboard, weekly reports. Support for 2 months after implementation.
• Model training on your data — fine-tuning for increased accuracy (optional).

Why choose us

We are a team of AI/ML engineers with experience in production systems. We have completed 30+ projects on autogeneration for banks, retail, and telecom. We guarantee: acceptance rate >60%, response time reduction from 40%, CSAT not lower than manual responses.

Contact us for a detailed business case with ROI calculation. Order a pilot — we will analyze your flow and propose an architecture in 2 weeks. Pilot project cost starts at $5,000, covering analysis and a working prototype.