Skip to content Skip to footer
0 items - $0.00 0
Get Started
0 items - $0.00 0
Get Started
0 items - $0.00 0
KIMBODO

AI-Powered Talent & Employer Chatbot with Human Escalation

Close

Overview

A well-known U.S. talent agency needed an intelligent chatbot to automate and streamline interactions between job-seeking talent and hiring employers. The existing live chat system required 25 human agents to manually handle thousands of daily queries, leading to high operational costs, delays, and inconsistent user experiences reduced to 5 human agents.
Key challenges included:
High Human Workload – Too many queries required manual responses, reducing efficiency.
Context-Switching Complexity – Talent and employer queries required different knowledge bases, making a single bot ineffective.
Delayed Resolution – Escalations to human agents caused long wait times.
Lack of Real-Time Monitoring – No way for human agents to oversee conversations in real time.
Scalability – The system was unable to handle surges in demand without hiring additional staff.

AI-Powered Talent & Employer Chatbot with Human Escalation

Customer Challenge

A well-known U.S. talent agency needed an intelligent chatbot to automate and streamline interactions between job-seeking talent and hiring employers. The existing live chat system required 25 human agents to manually handle thousands of daily queries, leading to high operational costs, delays, and inconsistent user experiences reduced to 5 human agents.

Key challenges included:

  1. High Human Workload – Too many queries required manual responses, reducing efficiency.
  2. Context-Switching Complexity – Talent and employer queries required different knowledge bases, making a single bot ineffective.
  3. Delayed Resolution – Escalations to human agents caused long wait times.
  4. Lack of Real-Time Monitoring – No way for human agents to oversee conversations in real time.
  5. Scalability – The system was unable to handle surges in demand without hiring additional staff.

Solution Overview

A two-agent chatbot system was developed using Dialogflow, AWS Lambda, and React.js to create a real-time, scalable solution. The chatbot could:

  • Dynamically route queries to either the Talent Agent or the Employer Agent based on user responses.
  • Retry twice before escalating to a human agent.
  • Allow human agents to take over in real time, with live chat notifications and conversation logs.
  • Reduce human agents from 25 to 5 by automating most interactions.

Technologies Used

1. Conversational AI (Dialogflow CX & ES)

  • Two AI agents:
    • Talent Agent – Helps job seekers find relevant positions, update resumes, and answer application questions.
    • Employer Agent – Assists hiring managers with job postings, interview scheduling, and candidate screening.
  • Context-Aware Conversations – Dialogflow manages conversation flow, setting context from the first interaction.
  • Fallback Handling – After two failed attempts to understand a user query, the chat escalates to a human agent.

2. Serverless Backend (Node.js + AWS Lambda)

  • AWS Lambda handles intent matching and API requests, ensuring fast execution and auto-scaling.
  • Socket.io enables real-time bidirectional communication between users and human agents.
  • Separate Knowledge Bases are loaded per session to match the right responses.

3. Frontend (React.js)

  • Custom Chat UI (Desktop & Mobile)
  • Human Takeover – If escalation occurs, agents see blinking chat handles for urgent intervention.
  • Real-Time Logs & Monitoring – Human agents can monitor chatbot-user interactions in real time.
  • Push Notifications – Immediate alerts are sent to agents when intervention is needed.

Implementation Flow

  1. User initiates chat → Identifies as Talent or Employer.
  2. Dialogflow assigns the query to the appropriate agent.
  3. Backend (Node.js + AWS Lambda) processes requests and fetches relevant responses.
  4. Chatbot attempts two automated responses; if both fail, the system escalates to a human agent.
  5. Human agent notification triggers, and a blinking chat handle appears for immediate attention.
  6. Human agent joins live chat in React UI to resolve the issue.
  7. Conversation logs are recorded for analytics and training improvements.

Success Metrics & Results

  • Reduced human agents from 25 → 5, cutting costs by 80%.
  • Automated 90% of talent & employer queries without human intervention.
  • Improved response time from 5-10 minutes to under 10 seconds.
  • Increased user satisfaction due to fast and precise responses.
  • Enhanced operational insights with real-time chat logs and analytics for agent performance tracking.

Future Enhancements

🔹 AI-Driven Job Matching – Recommend job postings dynamically based on resumes & employer needs.
🔹 Sentiment Analysis – Detect frustration and auto-escalate before user dissatisfaction rises.
🔹 Voice-Based Interaction – Add Google Assistant & Alexa integration for voice-based job searches.
🔹 Multilingual Support – Expand chatbot capabilities to support Spanish, French, and other languages.
🔹 Advanced Analytics Dashboard – Deeper insights into user behavior, dropout rates, and agent efficiency.

Conclusion

By leveraging Dialogflow, AWS Lambda, and React.js, the talent agency significantly reduced manual workload, cut costs, and improved response times. This scalable, AI-powered solution now efficiently manages thousands of conversations daily while ensuring human intervention only when necessary. Future enhancements will make the system even more intelligent, personalized, and proactive.