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 180,000 rubles per month (10 operators with an average salary of 60,000 rubles 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
| Criterion | Full auto-replies | Agent Assist |
|---|---|---|
| Safety | Only confidence >0.95 | Operator controls |
| Inquiry coverage | 10-30% | 70-80% |
| Hallucination risk | Medium (monitoring needed) | Low (human checks) |
| Operator time savings | 100% on those queries | 40-60% |
| CSAT | Depends on quality | Often higher than manual |
Metrics comparison before and after implementation
| Metric | Before | After |
|---|---|---|
| Average response time | 4 hours | 2 hours |
| CSAT | 3.5 | 4.2 |
| Operator throughput | 100 req./day | 170 req./day |
| Acceptance rate | - | 65% |
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
- Analysis of the inquiry flow — collect statistics: top 20 topics, language, complexity. Determine which requests can be automated. Use a BERT-based classifier.
- Collecting and preparing the knowledge base — consolidate ticket history, FAQ, articles. Clean, label, create embeddings (1536-dim). Typical base size is 5000+ documents.
- 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.
- 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.
- 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.