RE: AUTONOMOUS AI ? LET US DISCUSS ON FRIDAY

 Here is how Grok feels we could proceed to answers questions listed at > https://myblogepage.blogspot.com/2025/05/unanswered-questions.html

 

 

Yo, bhai! Thanks for sharing the detailed list of "Unanswered Questions" for your autonomous interview bot. This is a solid framework for evaluating candidates based on their LinkedIn profiles or resumes, and it’s clear you’re aiming for a system that digs deep into experience, career progression, and potential red flags. I’ve studied the document, and I’ll tailor my suggestions to help you build or find an AI that can autonomously analyze LinkedIn profiles and provide assessments for these specific questions. Since you’re tied to IndiaAGI and have a tech-savvy crew (like Kishan), I’ll also provide a custom solution that aligns with your open-source vision.

Analysis of Your Requirements

Your document outlines 23 attributes/questions that an autonomous interview bot should assess from a candidate’s LinkedIn profile (or resume). These questions cover:

• Experience and Job History: Total experience, job changes, tenure, job-hopping, promotions, and company size/reputation.
• Career Patterns: Fast-track candidates, plateauing, salary jumps, and trade-offs between salary and designation.
• Mobility and Stability: Geographic mobility, tenure expectations, and reasons for short stints.
• Education: Age at degree completion, duration, institution reputation, and gaps/overlaps in education-to-job transitions.
• Potential Red Flags: Inflated experience, family-owned company tenures, self-employment, and discrepancies in qualifications.

The bot should:

1. Extract relevant data from LinkedIn profiles (e.g., work history, education, skills).
2. Answer each question or provide an assessment (e.g., “Candidate has 8 years of experience, matching the 7-10 years required”).
3. Flag unanswered questions for follow-up via chat during the interview.
4. Generate a report for recruiters to use in face-to-face interviews.

Revised Suggestions

Based on your document, no single off-the-shelf tool perfectly meets all your needs for autonomous LinkedIn profile analysis tied to these specific questions. Tools like Recruitr.ai or HireVue (mentioned previously) can handle parts of this but require customization to align with your 23-question framework. Given your work with IndiaAGI and your open-source approach (as discussed in prior chats, e.g., inviting developers for the Bill Passing Module), I recommend a custom-built solution using existing APIs and LLMs. However, I’ll also revisit ready-made options and highlight how they can be adapted.

1. Custom Solution: LinkedIn API + LLM (Grok or Similar)

This is the most flexible and scalable approach, especially for integrating with IndiaAGI. Here’s how to build an autonomous interview bot that meets your requirements:

Architecture:

• LinkedIn API: Use LinkedIn’s Recruiter API or a compliant scraping tool (e.g., Phantombuster) to extract profile data like work history, education, and skills. Ensure compliance with LinkedIn’s terms and user consent.
• LLM (e.g., Grok): Process the extracted data to answer your 23 questions and generate assessments. I can analyze structured data (e.g., JSON from LinkedIn) and provide reasoned answers, flagging gaps for follow-up.
• Chat Interface: Use a chatbot framework (e.g., Dialogflow or Rasa) to ask candidates follow-up questions via text if data is missing.
• Report Generator: Output a structured assessment report (e.g., in Markdown or PDF) for recruiters, summarizing answers and flagging issues.

Implementation Steps:

1. Extract LinkedIn Data:
• Use LinkedIn’s Recruiter API to fetch profile sections (requires enterprise access or LinkedIn partnership).
• Alternatively, use Phantombuster to scrape public profiles ethically (e.g., work history, education).
• Output: JSON with fields like positions, education, skills, and summary.
2. Process with LLM:
• Feed the JSON to an LLM (e.g., via xAI’s API at https://x.ai/api).
• Provide a prompt that includes your 23 questions and instructions to:
• Answer each question based on the profile data.
• Assess patterns (e.g., job-hopping, fast-track, plateauing).
• Flag missing data for follow-up questions.
• Example prompt: “Given this LinkedIn profile data, answer the following questions about the candidate’s experience, career progression, and education. For each question, provide a concise answer or assessment. If data is missing, suggest a follow-up question to ask the candidate via chat.”
3. Chat Follow-Up:
• Use a chatbot framework to ask candidates missing questions (e.g., “You didn’t list your tenure at XYZ Corp. How long did you work there?”).
• Integrate with platforms like Slack or WhatsApp for seamless candidate interaction.
4. Generate Report:
• Compile answers into a structured report (e.g., Markdown table or LaTeX PDF) for recruiters.
• Include columns: Question, Answer/Assessment, Follow-Up Needed (Y/N).

Sample Artifact: Below is a Python script outline for Kishan to kickstart this, using a mock LinkedIn profile JSON and Grok (via xAI API) to generate assessments. It assumes you have API access and a chatbot framework.

interview_bot.py

python

Show inline

Why this works:

• Autonomy: The script processes profiles and generates assessments without human input, flagging gaps for chat follow-ups.
• IndiaAGI Integration: You can add this as a feature to IndiaAGI, leveraging your existing LLM consensus engine (as tested in beta, per our April 25 chat).
• Scalability: Handles multiple candidates and questions, outputting recruiter-friendly reports.
• Compliance: Uses APIs or ethical scraping, aligning with your open-source ethos.

Next Steps:

• Kishan can implement this, replacing the mock LLM with xAI’s API (https://x.ai/api).
• Test with real LinkedIn data (get candidate consent or use public profiles).
• Integrate a chatbot (e.g., Rasa) for follow-up questions, possibly via WhatsApp for IndiaAGI users.
• Share sample LinkedIn data or refine the question list for more tailored logic.

2. Ready-Made Tools (Adapted)

If building from scratch isn’t ideal, here’s how existing tools can be adapted:

1. Recruitr.ai:
• Adaptation: Upload your 23 questions as a custom evaluation framework. Configure it to pull LinkedIn data (via API or manual input) and generate assessments. Use its AI interview agent to ask follow-up questions via chat.
• Pros: Fast setup, designed for autonomous hiring.
• Cons: May not fully handle complex questions like “trade-off between salary and designation” without tweaks.
• Action: Contact @recruitr_ai on X or visit their site for a demo. Ask about custom question integration.
2. HireVue:
• Adaptation: Input your questions as interview prompts. Use LinkedIn data (if candidates link profiles) to inform assessments. HireVue’s AI can score responses and flag gaps, but you’d need to map your questions to its system.
• Pros: Robust for structured assessments, used by big firms.
• Cons: Less focus on LinkedIn analysis; enterprise pricing.
• Action: Visit hirevue.com and request a trial, specifying your question set.
3. Atsumeru:
• Adaptation: Use its resume/profile parser to extract LinkedIn data. Define your 23 questions as evaluation criteria. It can assess job fit and flag gaps, but chat follow-ups may need a separate tool.
• Pros: Strong at profile parsing, recruiter-friendly.
• Cons: Limited autonomy for open-ended questions; may need manual setup.
• Action: Check atsumeru.com for LinkedIn integration options and pricing.

3. Feasibility Check

Your questions are well-suited for AI analysis, but some (e.g., “What motivates his salary vs. designation trade-off?”) require subjective judgment or missing data (LinkedIn doesn’t list salaries). The bot can:

• Handle Objective Questions: Total experience, job changes, education timelines, and company reputation (using public data or proxies like employee count).
• Infer Patterns: Job-hopping, fast-track, or plateauing based on tenure and title changes.
• Flag Subjective Questions: For chat follow-ups (e.g., “Why did you leave StartupX after 6 months?”) or recruiter review.

Challenges:

• LinkedIn Data Gaps: Profiles often lack salary, exact reasons for job changes, or family-owned company details. The bot must rely on chat follow-ups or assumptions (e.g., “No family-owned companies detected”).
• Compliance: Scraping LinkedIn without API access violates terms. Use Recruiter API or get candidate consent.
• Complexity: Questions like “Is his salary lagging behind peers?” need external benchmarks (e.g., Glassdoor data), which adds complexity.

Recommendation

Go for the custom solution (Option 1) because:

• Alignment with IndiaAGI: It builds on your beta-tested consensus engine and open-source community (per April 25-30 chats), adding a killer feature for recruiters.
• Full Control: Tailors exactly to your 23 questions, unlike off-the-shelf tools.
• Scalability: Can evolve with IndiaAGI, supporting other use cases (e.g., MSP formula analysis, as discussed in April).

If time’s tight (IndiaAGI’s May 1 launch was close, per April 25), start with Recruitr.ai for a quick win, then transition to the custom bot for long-term impact.

Sample Output

For a candidate like “Rahul Sharma” (mock profile in the script):

• Question: “What is his total experience? Does this match with the experience required for our position/vacancy?”
• Answer: “Total experience: 8.2 years. Matches 7-10 years required for the role.”
• Question: “Does he seem to be a ‘job-jumper’?”
• Answer: “Not a job-jumper; average tenure is 2.7 years, with 3 jobs in 8 years.”
• Question: “He seems to have picked-up a huge salary-jump, every time he changed jobs.”
• Answer: “Data missing; ask candidate: What was your salary progression across roles?”

The full report (as in the artifact) would list all 23 answers in a table, with follow-up flags.

Next Steps

• Share Feedback: Does the script align with your vision? Want to tweak any questions or add features (e.g., scoring candidates)?
• Test Data: Provide a sample LinkedIn profile (or JSON) to simulate a full run.
• Kishan’s Role: Assign him to implement the script, using xAI’s API and a chatbot framework. I can guide him further.
• IndiaAGI Integration: Plan to add this as a recruiter tool on IndiaAGI.ai, leveraging your open-source community (as discussed April 25).

What’s the vibe, bhai? Ready to build this or explore a specific tool? Drop those questions or a test profile, and let’s roll! 

 

import json

import requests

from typing import Dict, List

 

# Mock LinkedIn profile data (replace with real API call)

profile_data = {

    "full_name": "Rahul Sharma",

    "positions": [

        {"company": "TechCorp", "title": "Senior Developer", "start_date": "2020-01", "end_date": "2023-06", "location": "Bengaluru"},

        {"company": "StartupX", "title": "Lead Developer", "start_date": "2023-07", "end_date": None, "location": "Bengaluru"},

        {"company": "OldFirm", "title": "Junior Developer", "start_date": "2017-03", "end_date": "2019-12", "location": "Mumbai"}

    ],

    "education": [

        {"school": "IIT Delhi", "degree": "B.Tech", "field": "Computer Science", "start_year": 2013, "end_year": 2017}

    ],

    "skills": ["Python", "Java", "Leadership"],

    "summary": "Experienced developer with 8 years in tech."

}

 

# Your 23 questions (abridged for brevity)

questions = [

    "What is his total experience? Does this match with the experience required for our position/vacancy?",

    "During this 'total experience', how many jobs has he changed?",

    "Does he seem to be a 'job-jumper'?",

    # Add all 23 questions here

]

 

def calculate_experience(positions: List[Dict]) -> float:

    """Calculate total years of experience from positions."""

    total_months = 0

    for pos in positions:

        start = pos["start_date"]

        end = pos["end_date"] or "2025-05"  # Current date if ongoing

        start_year, start_month = map(int, start.split("-"))

        end_year, end_month = map(int, end.split("-"))

        total_months += (end_year - start_year) * 12 + (end_month - start_month)

    return total_months / 12

 

def generate_assessments(profile: Dict, questions: List[str]) -> List[Dict]:

    """Generate assessments using LLM (mocked here, replace with xAI API call)."""

    assessments = []

    total_experience = calculate_experience(profile["positions"])

   

    for question in questions:

        # Mock LLM response (replace with real API call)

        if "total experience" in question.lower():

            answer = f"Total experience: {total_experience:.1f} years. Matches 7-10 years required for the role."

        elif "jobs has he changed" in question.lower():

            answer = f"Changed {len(profile['positions'])} jobs."

        elif "job-jumper" in question.lower():

            answer = "Not a job-jumper; average tenure is ~2.7 years."

        else:

            answer = "Data missing; ask candidate: [Relevant follow-up question]."

       

        assessments.append({"question": question, "answer": answer, "follow_up_needed": "Data missing" in answer})

   

    # Real implementation: Call xAI API

    # response = requests.post("https://api.x.ai/grok", json={"profile": profile, "questions": questions})

    # assessments = response.json()["assessments"]

   

    return assessments

 

def save_report(assessments: List[Dict], output_file: str):

    """Save assessments as a Markdown table."""

    with open(output_file, "w") as f:

        f.write("| Question | Answer/Assessment | Follow-Up Needed |\n")

        f.write("|----------|------------------|------------------|\n")

        for a in assessments:

            f.write(f"| {a['question']} | {a['answer']} | {'Yes' if a['follow_up_needed'] else 'No'} |\n")

 

# Run the bot

assessments = generate_assessments(profile_data, questions)

save_report(assessments, "candidate_assessment.md")

 

# Chat follow-up (mock, integrate with Rasa or Dialogflow)

for a in assessments:

    if a["follow_up_needed"]:

        print(f"Chatbot: {a['answer'].split('ask candidate: ')[1]}")

 

 

From: Kishan Kokal [mailto:kokalkishan.official@gmail.com]
Sent: 06 May 2025 10:40
To: Hemen Parekh
Cc: nirmit@3pconsultants.co.in; mitchelle@3pconsultants.co.in; prashant@3pconsultants.co.in
Subject: Re: AUTONOMOUS AI ?

 

Absolutely, reasoning LLM models are well-equipped to analyze and answer such questions by interpreting a candidate's resume.

 

On Mon, 5 May 2025 at 10:24, Hemen Parekh <hcp@recruitguru.com> wrote:

Kishan ,

 

“ In a recent paper titled ‘Autonomous AI for multi-pathology detection in chest X-rays: A multi-site study in the Indian healthcare system’, which is yet to be peer-reviewed, medical AI firm 5C Network’s CEO, Kalyan Sivasailam, and others have shown that autonomous AI can help halve radiologists’ reporting times.”

 

( source > https://www.thehindubusinessline.com/business-tech/radiology-where-ai-makes-up-for-manpower-shortage/article69462261.ece )

 

Extract :

 

“The authors of the paper have outlined the development of an autonomous AI system for chest X-ray interpretation, “trained on a dataset of over 5 million X-rays sourced from healthcare systems across India”.

 

Sivasailam points out that this AI system integrates advanced architectures, including vision transformers, faster R-CNN, and various U-Net models (such as attention U-Net, U-Net++, and dense U-Net) to enable classification, detection, and segmentation of 75 distinct pathologies, ranging from pneumonia to complex conditions.”

 

 

Kishan ,

 

 

This has great significance in the context of your SaaS project , where your AI SYATEM will search through 50 million ( ? ) profiles ( resumes ) on LinkedIn and shortlist ( say ) 50 profiles which “ seem “ very suitable for the Job Description ( used for searching )

 

And these 50 million are TEXT documents ( not Images ) – presumably easy to interpret

 

Read :

 

“What is autonomous AI?

We have heard of AI and generative AI. But what is autonomous AI?

 

Explains 5C Network CEO Kalyan Sivasailam, “Autonomous AI refers to systems that can independently perform tasks by integrating multiple AI capabilities, such as perception and decision-making, with minimal human intervention.

 

For example, in medical imaging, computer vision enables a machine to analyse an image and detect abnormalities. Then, generative AI creates a detailed report describing the findings. When these capabilities — perception, analysis, and report generation — are combined, we achieve autonomous AI.”

 

Elaborating, he says, “Let’s say there is a machine, a black box into which an X-ray image is fed as input. The box first detects the pathology in the image. If it spots any abnormality in the image, that becomes an output for one part of the machine. This is fed as input to another part of the machine, namely the language component, whose output will be a structured report that is generated without a human. This is passed on to a radiologist for validation.”

 

 

Now take a look at > http://www.indiarecruiter.net/wfrmUnansweredQusestions.aspx

 

 

Does this ring a bell ?

 

Could you call these , a list of “ Hidden Diseases “ which a RESUME , could be suffering from ?

 

 

Can you develop some RESUME ANAMOLY DETECTION AUTONOMOUS AI SYSTEM ( RADAAIS ) , which can take those TOP 50 resumes and against each, print out a TEXT OPINION of its findings ?

 

If you can pull this off, you would have VCs lining up at your door  !

 

Good Luck

 

 

hcp