EchoSphere

Knotic

25 Aug

Hybrid

4 Prizes

Live

5Tracks

5Problem Statements

$3,000Prizes

1Coordinated AI Interview Panel 1 Problem Statements

Build an adaptive voice interview platform in which one or more AI interviewers represent different roles.

The interview panel may include a technical interviewer, hiring manager, customer, product manager or behavioural interviewer.

The system should adapt its questions based on the candidate’s previous answers rather than following only a predefined question list.

The solution should demonstrate

Real-time and interruptible voice interviews
Multiple interviewer roles or personalities
Shared candidate context between interviewer roles
Dynamic follow-up questions
Controlled interviewer turn-taking
Role-play or scenario-based questions
Difficulty adjustment based on candidate performance
Identification of vague or contradictory answers
Evidence-based feedback linked to the interview transcript
A structured final assessment
Clear disclosure that the candidate is interacting with AI

Example scenario

A candidate provides a technically correct solution but does not explain its impact on customers.

The technical interviewer may accept the implementation, while the product interviewer should challenge the candidate to explain the business implications.

Problem Statements

ps11Build an adaptive voice interview platform in which one or more AI interviewers represent different roles. The interview panel may include a technical interviewer, hiring manager, customer, product manager or behavioural interviewer. The system should adapt its questions based on the candidate’s previous answers rather than following only a predefined question list. The solution should demonstrate Real-time and interruptible voice interviews Multiple interviewer roles or personalities Shared candidate context between interviewer roles Dynamic follow-up questions Controlled interviewer turn-taking Role-play or scenario-based questions Difficulty adjustment based on candidate performance Identification of vague or contradictory answers Evidence-based feedback linked to the interview transcript A structured final assessment Clear disclosure that the candidate is interacting with AI Example scenario A candidate provides a technically correct solution but does not explain its impact on customers. The technical interviewer may accept the implementation, while the product interviewer should challenge the candidate to explain the business implications.

2Adaptive AI Sales and Negotiation Agent 1 Problem Statements

Build a real-time voice AI sales agent that can conduct a complete customer qualification and sales conversation.

The agent should understand the customer’s requirements, identify objections, answer product questions and adapt its sales approach based on what happens during the call. It must not depend entirely on a fixed script.

The experience should feel like a live conversation in which the customer can interrupt, ask follow-up questions, change their requirements or return to something discussed earlier.

The solution should demonstrate

Natural turn-taking and interruption handling
Customer qualification through spoken conversation
Memory of details shared earlier in the session
Dynamic handling of pricing, trust and product objections
Retrieval of product, pricing or availability information
Integration with a CRM, calendar or lead-management system
Human-agent escalation with conversation context
A clear outcome such as meeting booking, lead qualification or follow-up creation

Example scenario

A customer begins by asking about pricing, interrupts the agent to compare the product with a competitor, changes the expected number of users and later requests an enterprise demonstration.

The agent should adapt the conversation and move the customer toward a meaningful next action.

Problem Statements

ps21Build a real-time voice AI sales agent that can conduct a complete customer qualification and sales conversation. The agent should understand the customer’s requirements, identify objections, answer product questions and adapt its sales approach based on what happens during the call. It must not depend entirely on a fixed script. The experience should feel like a live conversation in which the customer can interrupt, ask follow-up questions, change their requirements or return to something discussed earlier. The solution should demonstrate Natural turn-taking and interruption handling Customer qualification through spoken conversation Memory of details shared earlier in the session Dynamic handling of pricing, trust and product objections Retrieval of product, pricing or availability information Integration with a CRM, calendar or lead-management system Human-agent escalation with conversation context A clear outcome such as meeting booking, lead qualification or follow-up creation Example scenario A customer begins by asking about pricing, interrupts the agent to compare the product with a competitor, changes the expected number of users and later requests an enterprise demonstration. The agent should adapt the conversation and move the customer toward a meaningful next action.

3Live AI Classroom Co-Teacher 1 Problem Statements

Build a voice AI co-teacher that participates in a live digital classroom with a teacher and multiple students.

The AI should understand the ongoing lesson, wait for suitable opportunities to speak and help students without unnecessarily interrupting the teacher.

The system may provide explanations, conduct spoken quizzes, answer contextual questions and identify common learning gaps.

The solution should demonstrate

Real-time participation in a live classroom
Awareness of teacher and student roles
Appropriate turn-taking
Contextual answers based on the ongoing lesson
Different explanation levels for different students
Spoken quizzes or interactive exercises
Multilingual or code-switched conversations
Student identification through session or user identity
Post-class summaries or learning insights
A mechanism for the teacher to control or override the AI

Example scenario

During a mathematics class, different students struggle with the same concept. The AI should identify the repeated learning gap, provide a simpler explanation at an appropriate moment and help the teacher understand which students may require additional support.

Problem Statements

ps31Build a voice AI co-teacher that participates in a live digital classroom with a teacher and multiple students. The AI should understand the ongoing lesson, wait for suitable opportunities to speak and help students without unnecessarily interrupting the teacher. The system may provide explanations, conduct spoken quizzes, answer contextual questions and identify common learning gaps. The solution should demonstrate Real-time participation in a live classroom Awareness of teacher and student roles Appropriate turn-taking Contextual answers based on the ongoing lesson Different explanation levels for different students Spoken quizzes or interactive exercises Multilingual or code-switched conversations Student identification through session or user identity Post-class summaries or learning insights A mechanism for the teacher to control or override the AI Example scenario During a mathematics class, different students struggle with the same concept. The AI should identify the repeated learning gap, provide a simpler explanation at an appropriate moment and help the teacher understand which students may require additional support.

4Voice AI Incident Commander 1 Problem Statements

Build a real-time AI incident commander that joins a live operational or technical incident room.

The AI should listen to the discussion, organize information and help the team maintain a shared understanding of the incident.

It should distinguish confirmed facts from assumptions, track decisions, maintain a timeline and follow up on unresolved actions.

The solution should demonstrate

Real-time participation in a live team voice room
Recognition of participant roles
Extraction of facts, hypotheses, decisions and action items
Assignment and tracking of task ownership
Detection of missing or conflicting information
A continuously updated incident timeline
Integration with tools such as Jira, Slack, PagerDuty or monitoring systems
Spoken status summaries at appropriate moments
Human confirmation before executing critical actions
A final incident summary with unresolved risks

Example scenario

A payment system experiences an outage. Engineers, support teams and business leaders join the incident room and share incomplete or conflicting information.

The AI should organize the evidence, track responsibilities and keep the team aligned without pretending to independently determine the root cause.

Problem Statements

ps41Build a real-time AI incident commander that joins a live operational or technical incident room. The AI should listen to the discussion, organize information and help the team maintain a shared understanding of the incident. It should distinguish confirmed facts from assumptions, track decisions, maintain a timeline and follow up on unresolved actions. The solution should demonstrate Real-time participation in a live team voice room Recognition of participant roles Extraction of facts, hypotheses, decisions and action items Assignment and tracking of task ownership Detection of missing or conflicting information A continuously updated incident timeline Integration with tools such as Jira, Slack, PagerDuty or monitoring systems Spoken status summaries at appropriate moments Human confirmation before executing critical actions A final incident summary with unresolved risks Example scenario A payment system experiences an outage. Engineers, support teams and business leaders join the incident room and share incomplete or conflicting information. The AI should organize the evidence, track responsibilities and keep the team aligned without pretending to independently determine the root cause.

5Multilingual Assistance-Line Agent with Human Escalation 1 Problem Statements

Build a real-time multilingual voice AI agent for a customer assistance, public information or non-clinical support line.

The caller may be stressed, speaking from a noisy environment, using more than one language or unable to clearly explain the issue.

The agent should calmly collect essential information, confirm its understanding and transfer the caller to a human when confidence is low or the situation requires human judgement.

The solution should demonstrate

Multilingual and code-switched voice interaction
Natural interruption handling
Information collection through conversation
Repetition and confirmation of critical details
Low-confidence detection
Prioritized question flow
Background-noise resilience
Human escalation with context preservation
Integration with ticketing or case-management systems
Clear boundaries around what the AI is allowed to do

Example scenario

A caller begins in Hindi, switches to English and provides incomplete details while people are speaking in the background.

The AI should collect and confirm the minimum required information and transfer the case to a human with a concise conversation summary.

Safety restriction

The prototype must not:

Provide medical diagnosis
Replace trained emergency responders
Provide legal, financial or emergency instructions as authoritative advice
Present uncertain AI-generated information as confirmed fact

Problem Statements

ps51Build a real-time multilingual voice AI agent for a customer assistance, public information or non-clinical support line. The caller may be stressed, speaking from a noisy environment, using more than one language or unable to clearly explain the issue. The agent should calmly collect essential information, confirm its understanding and transfer the caller to a human when confidence is low or the situation requires human judgement. The solution should demonstrate Multilingual and code-switched voice interaction Natural interruption handling Information collection through conversation Repetition and confirmation of critical details Low-confidence detection Prioritized question flow Background-noise resilience Human escalation with context preservation Integration with ticketing or case-management systems Clear boundaries around what the AI is allowed to do Example scenario A caller begins in Hindi, switches to English and provides incomplete details while people are speaking in the background. The AI should collect and confirm the minimum required information and transfer the case to a human with a concise conversation summary. Safety restriction The prototype must not: Provide medical diagnosis Replace trained emergency responders Provide legal, financial or emergency instructions as authoritative advice Present uncertain AI-generated information as confirmed fact

DaysHoursMinutesSeconds

Hackathon Dates

Starts26 Aug 202612:00 AM IST

Ends12 Sep 202612:00 AM IST

Registration Dates

Opens21 Jul 202611:00 AM IST

Closes25 Aug 202612:05 AM IST

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