Tushar Yadav profile photo 2 on Tal
Tushar Yadav profile photo 1 on Tal
Tushar Yadav profile photo 2 on Tal
Tushar Yadav profile photo 1 on Tal

Tushar Yadav

Building Pibit.ai (YC W21) | IIT Guwahati Gold Medalist

Bengaluru

5+ years of work experience

Pibit.ai
Apr 20251 y 4 moPresent

Bengaluru, Karnataka, India

UnifyApps
Jan 20241 yr 2 mosFeb 2025

Gurugram, Haryana, India

Sprinklr
Jun 20238 mosJan 2024

Gurugram, Haryana, India

Sprinklr
Oct 20228 mosMay 2023

Gurugram, Haryana, India

Sprinklr
Apr 20226 mosSep 2022

Gurugram, Haryana, India

Sprinklr
Feb 20211 y 1 moMar 2022

Gurgaon

Wells Fargo
Aug 20208 mosMar 2021

Bengaluru, Karnataka, India

EXL
May 20193 mosJul 2019

Gurugram, Haryana, India

Built an end-to-end Deep Learning-based image classification solution using complex architectures like CNN to detect defects present in receipt images. Then corrected the defects present using respective correction functions in a sequential fashion to increase the accuracy of an OCR(Optical Character Recognition) engine. The solution designed is universal to be used on any receipt image and more defect cases can be added based on the requirements. The given solution has F1-Measure of ~90% and recall of ~90% for each class of the defect. The solution can also tackle an image with multiple defects.

Tushar’s AI Stack

claude
claude
codex
codex
cursor
cursor
gemini
gemini

Education

Indian Institute of Technology, Guwahati

Bachelor of Technology (B.Tech.), Civil Engineering

Indian Institute of Technology, Guwahati

20164 yrs2020
Raath International School - India

Senior Secondary

Raath International School - India

20132 yrs2015

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Headline

Building Pibit.ai (YC W21) | IIT Guwahati Gold Medalist

About

I am a Product Manager with a B.Tech degree from IIT Guwahati, focused on building AI-driven products that solve complex, real-world problems at scale. With 6+ years of experience across SaaS startups and enterprises, I’ve worked at UnifyApps, Sprinklr, and Wells Fargo, delivering data-intensive and automation-first products. At UnifyApps, I was working closely with the founders to define product vision, roadmap, and go-to-market strategy from 0→1. At Sprinklr, I led product initiatives for the Customer Care platform, including the integration of GenAI-powered conversational bots to automate and enhance customer support workflows. Currently, at Pibit.ai, I am building AI-native systems for commercial insurance underwriting. Over the past year, I’ve led the development of a configurable platform and underwriter workbench that leverages LLMs and rule-based systems to automate submission intake, document understanding, and decision workflows. The platform combines AI extraction, validation, and human-in-the-loop feedback loops to improve underwriting speed, accuracy, and auditability. I am particularly interested in designing AI systems that move beyond standalone models focusing on orchestration, reliability, and real-world deployment of GenAI in high-stakes domains. Specialties: Applied AI & GenAI Products, LLM-based Systems, Workflow Automation, Product Strategy & Roadmapping, SaaS Platforms, API Integrations, Human-in-the-loop Systems, AI Evaluation & Guardrails, Analytics.

Location

Bengaluru

AI stack

claude

codex

cursor

gemini

Work experience

Product Lead at Pibit.ai, Apr 2025 - Present · 1 y 4 mo, Bengaluru, Karnataka, India

Senior Product Manager at UnifyApps, Jan 2024 - Feb 2025 · 1 yr 2 mos, Gurugram, Haryana, India

Product Manager at Sprinklr, Jun 2023 - Jan 2024 · 8 mos, Gurugram, Haryana, India

Associate Product Manager at Sprinklr, Oct 2022 - May 2023 · 8 mos, Gurugram, Haryana, India

Senior Product Analyst at Sprinklr, Apr 2022 - Sep 2022 · 6 mos, Gurugram, Haryana, India

Product analyst at Sprinklr, Feb 2021 - Mar 2022 · 1 y 1 mo, Gurgaon

Program Associate at Wells Fargo, Aug 2020 - Mar 2021 · 8 mos, Bengaluru, Karnataka, India

Summer Internship at EXL, May 2019 - Jul 2019 · 3 mos, Gurugram, Haryana, India, Built an end-to-end Deep Learning-based image classification solution using complex architectures like CNN to detect defects present in receipt images. Then corrected the defects present using respective correction functions in a sequential fashion to increase the accuracy of an OCR(Optical Character Recognition) engine. The solution designed is universal to be used on any receipt image and more defect cases can be added based on the requirements. The given solution has F1-Measure of ~90% and recall of ~90% for each class of the defect. The solution can also tackle an image with multiple defects.

Education

Bachelor of Technology (B.Tech.), Civil Engineering, Indian Institute of Technology, Guwahati

Senior Secondary, Raath International School - India