We frequently encounter a typical scenario: a client writes to support with a complaint about an order. The chatbot offers templated responses, the client gets irritated, the request escalates to a customer service agent who spends 10 minutes understanding the context. Our conversational AI platform, leveraging LangGraph and RAG, powers a virtual assistant that automates customer support: it immediately recognizes the intent, extracts the order number from the message, queries the CRM, checks the delivery status, and suggests solutions—all in 2 seconds. If a return is needed, the agent initiates the procedure without human intervention. This automation boosts handling speed and reduces first-line support load, cutting costs by 30–40%. For a typical mid-market retailer, this means annual savings of $150,000–$250,000. A typical pilot project starts at $50,000 for a single-channel agent with 20-30 intents. For a multi-channel deployment with 50+ intents, the investment typically ranges from $80,000 to $150,000. Our system has processed over 500,000 support tickets for a client, achieving a 78% containment rate.
Why an AI Agent Outperforms Chatbots
Traditional chatbots with scripted decision trees fail on the first out-of-scope query. Our virtual assistant uses an LLM core orchestrated via LangGraph—a state graph where each step branches based on the client's intent and CRM data. Average first response time is under 1 second, intent classification accuracy reaches 88–94% depending on the domain. Compared to conventional bots, this yields 3–4 times higher containment rate (Gartner research). Response times are up to 10x faster than human operators.
Architectural Stack
The system is built on an LLM core with orchestration via LangGraph. Key components:
- Dialogue State Tracker – stores and updates conversation state: intent, extracted entities (slots), message history, current task status. Uses Redis with per-session TTL.
- Tool Executor – a set of tools:
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lookup_customer(phone/email)→ CRM data -
get_order_status(order_id)→ status from ERP/OMS -
create_ticket(params)→ ticket in Jira/Zendesk -
process_refund(order_id, reason)→ initiate return -
schedule_callback(datetime)→ calendar entry
We combine LLMs like GPT-4o with fine-tuning via LoRA and embedding-based RAG for accurate responses. The platform uses RAG for factual retrieval, LangGraph for state management, and fine-tuned LLMs via LoRA for brand-specific responses. Embeddings enable semantic search, and MLOps ensures continuous deployment.
from langgraph.graph import StateGraph, END def build_agent_graph(llm, tools, escalation_threshold=0.7): graph = StateGraph(DialogueState) graph.add_node("understand_intent", intent_classifier_node) graph.add_node("retrieve_context", crm_lookup_node) graph.add_node("generate_response", llm_response_node) graph.add_node("execute_action", tool_executor_node) graph.add_node("check_escalation", escalation_check_node) graph.add_node("human_handoff", handoff_node) graph.add_conditional_edges( "check_escalation", lambda state: "human_handoff" if state.escalation_score > escalation_threshold else "generate_response" ) return graph.compile()How We Adapt the Model to Your Brand
The base LLM (GPT-4o, Claude 3, Llama 3.1) requires tuning:
- System prompt engineering: detailed instructions for tone, forbidden topics, mandatory disclaimers.
- Few-shot examples: 50–100 brand-specific question-answer pairs.
- Fine-tuning via PEFT/LoRA on a corpus of real dialogues—improves tone alignment and reduces hallucinations about products.
For factual access we use RAG: a vector store (Qdrant, pgvector) with documentation, FAQ, and product specs. Before responding, the agent retrieves relevant context using embeddings for semantic search—this reduces hallucinations by 40% compared to a plain prompt.
Multi-Channel Support and Integrations
The agent is deployed simultaneously across multiple channels via a unified backend:
Channel Integration Website React/Vue widget, WebSocket Telegram Telegram Bot API WhatsApp WhatsApp Business API (360dialog, Twilio) Mobile app REST API + SSE Telephony Voicebot via Asterisk/FreeSWITCH + ASR/TTS Metrics We Track
Key KPIs from day one:
- Containment Rate – percentage of requests resolved without human operator: target 65–80% for e-commerce.
- CSAT bot – user satisfaction score after interaction.
- First Contact Resolution – resolution within a single dialogue.
- Escalation Precision – justification of transfers to operator.
Conventional chatbots achieve containment rates of 20–30%; the AI agent with the described architecture reaches 65–80%. In a recent e-commerce deployment, the resolution rate reached 78% within the first month.
How We Develop an AI Agent
The process includes six stages:
- Analysis of top 100 support queries and intent design.
- Tool set development and integration with CRM/ERP via REST API or WebSocket.
- Prompt engineering, collection and labeling of training dialogues.
- Deployment in 1–5 channels, A/B test on 10% of traffic.
- Metric monitoring and two iterations based on pilot results.
- Warranty support for 3 months after launch.
Our virtual assistant functions as a customer service agent across multiple channels, providing comprehensive support automation. With over 5 years in AI and 20+ automation projects, our MLOps pipeline ensures continuous model retraining and deployment. Our team has 5+ years of experience in AI and has successfully delivered 20+ automation projects.
What's Included
- Requirements documentation
- System architecture design
- Code repository with CI/CD
- Model fine-tuning with LoRA
- Integration with CRM/ERP
- Deployment to 1–5 channels
- Operator dashboard for escalations
- Training session for support team
- 3 months of post-launch support
Timeline Estimates
Complexity Duration Single channel, 20–30 intents, basic integrations 5–7 weeks Multiple channels, 50+ intents, ERP/CRM integration 8–12 weeks Voice + text, model fine-tuning 12–18 weeks How to Estimate Cost
Cost is calculated individually based on the number of intents, channels, and integration complexity. Schedule a consultation—we will analyze your scenario and prepare a commercial proposal within 2–3 business days.
Experience our AI agent with your own queries—contact us for a pilot project.
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