Author · Zappio

Mohit Kumar

Technology Lead, Zappio

In PropTech and analytics since 2022; founder of two ventures

Technology Lead at Zappio, with earlier roles across PropTech and analytics at NoBroker and Housr, and as founder of Wisdom Peak Analytics.

2022Working in PropTech and analytics since
2Ventures founded
2PropTech companies before Zappio

Technology Leadership
AI Voice Platforms
Data Analytics
PropTech

LinkedIn profile Read the Zappio blog

About

About Mohit Kumar

Mohit Kumar is Technology Lead at Zappio, the AI voice calling platform that calls, qualifies and follows up with inbound leads for sales teams.

His earlier roles span PropTech and analytics: Associate at NoBroker, Senior Analyst at Housr and founder of Wisdom Peak Analytics. He also founded UrbanHelp.

His expertise lines up with the technical side of the Zappio blog: how an AI calling platform is built, how it connects to a CRM, and how call data is measured and improved.

Expertise

Topics Mohit covers

Areas of the Zappio blog that match Mohit's professional background.

Written by this author

Articles by Mohit Kumar

No articles published under Mohit's byline yet

New posts by Mohit will appear here as they are published.

Browse all Zappio articles

From the Zappio blog

Written by other Zappio contributors on topics close to Mohit's work.

AI Calling Analytics, Conversation Intelligence & Continuous Optimization

Building a Real Estate Call Quality Scorecard — AI-Assisted Evaluation for Human Follow-Up Calls

A framework for building an AI-assisted call quality scorecard for real estate relationship manager follow-up calls — the eight evaluation dimensions from speed-to-engagement to compliance, a full Python scoring implementation, team-level benchmarks correlating scores to conversion, and safeguards for using scores in performance appraisal.

11 Jul 2026 · 12 min read

AI Calling Analytics, Conversation Intelligence & Continuous Optimization

Micro-Market Demand Signals from AI Calling Data — How Developers Time Project Launches Using Call Patterns

A demand intelligence framework for real estate developers using AI Calling transcript data — the five demand signals (inquiry volume trend, urgency concentration, price tolerance, competitor awareness, configuration shift), a launch readiness scoring model, and a Dwarka Expressway Sector 108 launch timing case study.

10 Jul 2026 · 12 min read

AI Calling Analytics, Conversation Intelligence & Continuous Optimization

Drop-Off Rate Analysis in AI Calling — Where Real Estate Leads Disconnect and Exactly Why

A diagnostic framework for AI Calling drop-off rate analysis in real estate — the five points of failure in a qualification call from pre-answer to commitment friction, drop-off benchmarks by lead source, and a Python diagnostic system for attributing script fixes to specific turns.

10 Jul 2026 · 12 min read

AI Calling Analytics, Conversation Intelligence & Continuous Optimization

AI Call Sentiment Analysis — How Emotion Detection Improves Lead Qualification in Real Estate

A framework for AI call sentiment analysis in real estate qualification — the three signal layers (lexical, acoustic, contextual emotional arc), a full sentiment classification code implementation, sentiment-triggered routing tiers, and campaign-level sentiment intelligence for developers.

10 Jul 2026 · 12 min read

AI Calling Analytics, Conversation Intelligence & Continuous Optimization

Real-Time AI Calling Dashboards for Real Estate — Monitoring Live Campaign Performance During Project Launches

A dashboard architecture guide for real-time AI Calling monitoring during real estate project launches — the five core live views (call status, qualification funnel, geographic heat map, sentiment tracker, slot utilization), the mid-campaign intervention playbook, and the dashboard technology stack.

10 Jul 2026 · 11 min read

Technical Architecture & Developer Guides

Prompt Engineering for Real Estate AI Calling Agents — Qualification Logic That Actually Works

A complete prompt engineering guide for real estate AI Calling Agents — the four-layer prompt architecture (system prompt, state context, few-shot examples, turn context), a production-grade system prompt template, five critical few-shot scenarios, a hybrid LLM + state machine flow control pattern, a common-failures-and-fixes table, and a 50-case prompt evaluation framework.

6 Jul 2026 · 13 min read

Verify Mohit's profile

Independent places to confirm Mohit Kumar's role and background.

Profile last reviewed 23 Oct 2026

See the platform Mohit works on

Zappio calls, qualifies and follows up with every inbound lead. Book a demo to see it on your own lead flow.

Read the Zappio blog