Integrate AI Chatbot with Jivo, LiveChat, and Carrot Quest

You launched a live chat on your site; clients ask the same questions, and operators drown in routine. We've seen this in dozens of projects: an AI bot handles 70-80% of first-line queries, leaving complex tasks to humans. Integration with Jivo, LiveChat, or Carrot Quest is not just "attaching" an API—it's building a pipeline: from receiving a message to delivering a meaningful response that considers history and business rules. We have 5 years of AI integrations and over 100 projects completed.

Problems We Solve

Context Loss When Transferring to Operator

A client has chatted with the bot for 10 minutes—the bot knows the whole history. When escalating to an operator, the bot sends a brief summary, not the raw log. We implement this with LangChain's ConversationSummaryMemory compressing the dialog into 3-4 sentences. Without this mechanic, the operator spends time reading the log, and NPS drops by 15-20%.

Latency p99 Above 3 Seconds

If the model takes longer than 2.5 seconds to respond, the client leaves. The solution—quantization to INT4 and vLLM with continuous batching. In tests with LLaMA 3 70B, p99 dropped from 4.2s to 1.2s—vLLM provides 3x lower latency than standard inference. For Carrot Quest, we additionally cache frequent requests with Redis (TTL 30 minutes)—covering 40% of dialogues without calling the LLM.

Complex Escalation Rules

You can't just transfer everything. We use a rule system: if a user asks "Where is my order?" three times—immediate escalation, even with high confidence. Or if sentiment is negative (library cardiffnlp/twitter-roberta-base-sentiment), the bot responds politely and offers an operator. Result: the operator gets only 20% of clients, already with context.

How We Integrate AI Bot with Jivo, LiveChat, and Carrot Quest

From our practice: for one online store, we deployed a bot on FastAPI inside Kubernetes. Stack:

• Model: OpenAI GPT-4o (temperature 0.3) with a system prompt describing the product catalog
• Embeddings: text-embedding-3-small (1536-dim) for RAG over the catalog (ChromaDB)
• Escalation: rules based on langchain with a confidence threshold of 0.7 and stop words
• Deploy: SageMaker with Triton Inference Server running two models (LLM + embedding)

Jivo sends a webhook (see below), FastAPI processes it, invokes the LangChain chain. Response is a JSON with fields event, chat_id, message. Processing time—1.8s at p95.

from fastapi import FastAPI
app = FastAPI()

@app.post("/jivo-webhook")
async def handle_jivo(data: dict):
    event = data.get("event")
    if event == "client_message":
        client_message = data["message"]["text"]
        chat_id = data["chat_id"]
        response = await ai_bot.process(client_message)
        return {
            "event": "bot_message",
            "chat_id": chat_id,
            "message": {"type": "text", "text": response}
        }
    return {"event": "ignore"}

Jivo supports quick reply buttons, operator transfer (escalation), and typing status. The AI bot works until an escalation condition triggers.

Carrot Quest API

Carrot Quest is a more functional platform with its own no-code bot builder and Python SDK. For custom logic: Webhook Triggers + API for sending messages.

Carrot Quest's feature: rich user data—browsing history, site events. The AI bot can consider: "You've viewed product X several times—want to ask a question?"

AI Modes in Live Chat

Transition rule to operator: explicit request + low AI confidence + negative sentiment + VIP client tag from CRM.

Model Comparison for Live Chat

Model choice depends on your priorities: maximum quality or data privacy. For confidential data we use open-source models with local deployment.

How the AI Bot Decides When to Transfer to Operator

A scoring system: the model returns response confidence (logprobs). If below 0.7—escalation. Additionally: stop words ("operator", "real person"), negative sentiment (model nlptown/bert-base-multilingual-uncased-sentiment), more than two repeated client questions. All configurable to your rules.

Which LLM Models We Use and Why

Proprietary (GPT-4o, Claude 3.5) for maximum quality. Open-source (LLaMA 3 70B, Mistral Large) for confidential data or cost reduction. In production we deploy via vLLM with INT4 quantization: latency p99 below 1.5s on 70B models. If fine-tuning for a specific domain is needed, we use LoRA for 2-3 epochs. In practice, GPT-4o handles 90% of queries without escalation, while LLaMA 3 only 75%.

Work Process

1. Analytics & Architecture—we analyze your knowledge base, configure RAG, design LangChain chains.
2. Development—write integration code, configure models (fine-tuning or LoRA if needed), test on historical dialogues.
3. Testing—A/B test AI vs operators (metrics: resolved dialogues, CSAT, average time).
4. Deployment—to your server or our cloud cluster, set up CI/CD pipeline via GitHub Actions.
5. Training—instructions for operators on how to take over the chat and how AI changes standard responses.

Timeline Estimates

Basic integration (one channel, one model)—from 2 weeks. If you need RAG over catalog, two models, multiple channels—up to 4 weeks. Cost is calculated individually, based on complexity and data volume. We provide a code warranty (30 days free support) and a certificate for the solutions used, and guarantee SLA (response time p99 < 2s, availability 99.9%).

What's Included

• Architectural scheme (PDF)
• Source code of the bot with comments
• Deployment instructions (Docker Compose, Kubernetes)
• Metrics dashboard (Grafana + Prometheus)—handled/escalated/average time
• 30 days of support after launch

Want to automate your support? Contact us for a consultation and a rough project estimate. Order a pilot on one channel in 2 weeks.