Human-in-the-Loop Twitter Auto-Responder for KBM/Hike

1. Introduction

Product Name: Twitter Auto-Responder (HITL Agent)
Client: Kavin Bhartiya Mittal (Founder, Hike Private Limited)
Category: AI-Powered Social Media Automation
Vision: To empower founders & executives to maintain authentic engagement on social media without spending significant personal time, while ensuring responses reflect their unique voice, tone, and knowledge context.

2. Problem Statement

Social media engagement is a critical part of personal branding for startup founders. However:

Time Constraint: Kavin was unable to manually monitor & respond to the large volume of Twitter replies/comments due to time limitations.

Relevance Issue: Low-effort or spam replies (“Yes”, “No”, abusive comments) cluttered engagement, making it hard to identify meaningful conversations.

Authenticity Need: Generic bots couldn’t reflect Kavin’s personal voice, tone, and contextual knowledge, which risked diluting his brand authenticity.

Challenge:
“How can we automate meaningful, high-quality Twitter responses while keeping Kavin in control, ensuring every reply matches his unique voice?”

3. Solution Overview

We built a Human-in-the-Loop Twitter Auto-Responder Agent that:

Filters irrelevant/low-effort replies.

Loops through valid comments twice daily (morning & evening).

Generates two personalized reply options (A & B) using a fine-tuned GPT model trained on Kavin’s past tweets, tone, and knowledge.

Sends the options via Telegram, where Kavin (or his team) can pick the preferred response.

Posts the selected reply automatically back on Twitter.

This approach balanced automation (speed & scale) with human oversight (authenticity & control).

4. Target Audience

Primary: Kavin Bharti Mittal (Hike Founder).

Secondary (Future Expansion): Startup founders, CXOs, and thought leaders seeking scalable, authentic engagement tools.

5. Goals & Success Metrics

Goals:

Automate filtering & drafting of Twitter replies.

Ensure every reply sounds 100% authentic to Kavin’s voice.

Reduce founder’s engagement time by >70%.

Success Metrics:

✅ Filtering Accuracy: >85% of irrelevant comments removed.

✅ Human-in-the-loop satisfaction: >90% of AI-suggested replies accepted.

✅ Time Saved: 2–3 hours/day saved for the founder.

✅ Engagement Lift: 20–30% higher quality replies posted vs before.

6. The Design & Development Process

Step 1 – Discovery & Research

Interviewed Kavin & his team to understand engagement challenges.

Analyzed past 6 months of his tweets & replies to extract tone, style, and content themes.

Benchmarked existing auto-responder tools → found they were too generic and not founder-specific.

Step 2 – Core Features Defined

Comment Filtering – remove one-word replies, spam, abusive content.

Loop & Batch Scheduling – run 2x daily, fetching latest replies.

Personalized AI Reply Generation – fine-tuned GPT on Kavin’s Twitter data + knowledge context.

Telegram Integration (Human-in-the-Loop) – send two reply options to Kavin’s Telegram, get his choice.

Auto-Post Back to Twitter – seamless execution after approval.

Step 3 – Workflow Design

Tool Used: n8n automation platform.

Flow:

Trigger (Scheduled) → Search Tweets → Filter → Loop → AI Agent → Telegram → Human Chooses → Auto-Post.

(Refer to workflow screenshot attached for visual)

Step 4 – Personalization Layer

Fine-tuned OpenAI GPT model with Kavin’s:

Previous tweets & replies.

Writing style (short, sharp, witty).

Knowledge base (Hike’s vision, Web3 insights, India-first product philosophy).

Step 5 – Pilot Launch

Ran a 1-week pilot with real replies.

Observed AI reply relevance: ~80% accurate to Kavin’s tone.

Telegram flow ensured zero risk of wrong/controversial auto-posts.

7. Challenges & Solutions

Tone Matching – AI initially produced generic replies.

✅ Solution: Fine-tuned model + few-shot prompting using past replies.

Abuse & Spam Handling – AI sometimes tried replying to trolls.

✅ Solution: Added stronger filter rules (sentiment & length check).

Founder’s Control – Kavin wanted to avoid full automation.

✅ Solution: Built HITL via Telegram, allowing quick selection.

Latency – Generating 2 options per comment slowed pipeline.

✅ Solution: Optimized batch size & caching for repeat queries.

8. Monetization Model (Future Scope)

While built specifically for Kavin, this system could evolve into a SaaS tool for founders & executives:

Subscription Model: $49–$99/month.

Premium Features: Voice cloning, multi-platform (LinkedIn, Instagram).

Team Mode: Assistants can approve replies.

9. Results & Impact

Time Efficiency: Kavin saved ~2 hrs/day on social media replies.

Authenticity: 92% of AI-generated replies were approved as-is.

Engagement: Higher-quality replies → ~28% increase in meaningful interactions.

Scalability: System designed to handle hundreds of comments/day without manual overload.

10. Future Roadmap

Multi-Platform Expansion – LinkedIn, Instagram, Threads.

Full Analytics Dashboard – Engagement reports, top conversations.

Advanced Personalization – Use founder’s podcasts, blogs, and interviews as training data.

Smart Prioritization – Reply first to high-value users (investors, industry leaders, loyal followers).

11. Key Learnings as a Product Manager

Balance Automation & Control: 100% automation in sensitive areas like personal branding risks authenticity. HITL is the sweet spot.

Founder’s Time = High Value: Even saving 1–2 hours/day is a major ROI driver.

Contextual AI Wins: Off-the-shelf tools fail → personalization (tone, knowledge base) is critical.

Iterative Pilots Are Essential: Early testing revealed tone mismatch & spam issues that we fixed before scaling.

📌 Conclusion

As the founders office role but thinking as Product Manager, I led the end-to-end creation of this AI-powered Twitter Auto-Responder with Human-in-the-Loop for Kavin Bharti Mittal (Hike).

The product successfully balanced automation for efficiency with human oversight for authenticity, enabling Kavin to scale his engagement without compromising his voice.

This solution not only solved an immediate founder pain point but also opened opportunities to productize the system into a SaaS for other thought leaders.