Ishan Chhabra

CEO

San Francisco, California, United States12 yrs 10 mos experience

Highly StableAI Enabled

Key Highlights

Expert in AI-driven revenue operations.
Developed innovative deal intelligence solutions.
Strong background in research and system design.

Stackforce AI infers this person is a SaaS expert with a strong focus on AI and revenue operations.

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Skills

Core Skills

AiRevenue OperationsResearchData PrivacySystem DesignCloud Computing

Other Skills

Deal IntelligenceAI agentsSales methodologiesData analysisUser similarity computationPrivacy-aware applicationsApplication DevelopmentDistributed systemsCloud-based systemsJavaSOAPApache CassandraCC++C#

About

I’m Ishan—an engineer turned GTM fixer. I started as an ML engineer and stumbled into RevOps by accident. Most days, I felt clueless.With product, you can instrument, isolate, debug, fix. Clear signals. Fast loops.With revenue, there’s no single pane of glass. It’s a mesh of systems—pipe gen, deal execution, retention, expansion, enablement—running in parallel as customers move through them.Data is fragmented. Bottlenecks hide. Everyone optimizes their silo; nobody sees the whole machine.After shipping ML at scale and living inside those sales orgs, I kept running into the same truth: CRMs don’t capture reality; conversations do.That’s why I built Oliv: to map the entire revenue process, show what’s working, what’s breaking, and where to apply energy. AI helps you see the truth, debug the journey, and actually fix it—end to end.At Oliv, we help transform your AI strategy in 3 simple ways:1. Unify signals – Emails, calls, meetings, product + 3rd-party data in a single, clean view of each account and deal.2. Agents execute – Auto-clean the pipeline, update fields, brief managers, and flag expansion/renewal risk so reps can just sell.3. Leaders forecast from reality – Signal-driven forecasts improve confidence and reduce Friday fire drills over “stale” fields.We work with revenue teams to transform their AI strategy end to end—not just slideware, but live agents in your stack.Have you started building your AI strategy for 2026 yet? If not, DM me!

Experience

Oliv ai

Chief Executive Officer

Feb 2023 – Present · 3 yrs 1 mo · San Francisco Bay Area

At Oliv AI, we solve one of the biggest problems for revenue teams: unreliable deal data.
Oliv captures Deal Intelligence from every meeting, call, and email—without any rep involvement. The result is a clear, detailed view of every deal, presented in scorecards built on trusted sales methodologies like MEDDICC, BANT, and SPICED.
Our AI agents are built for sales teams—sales managers, AEs, and RevOps—handling the work that takes them away from selling.
Deal Driver is the first of its kind, an agent built for sales managers to stay on top of deals, track progress, and take action based on real, unbiased insights.
With Oliv AI, sales teams can bring back focus on deals, strategy, and conversation.

Deal IntelligenceAI agentsSales methodologiesAIRevenue Operations

Instaworks studio

Chief Experimenter

Mar 2019 – Jan 2023 · 3 yrs 10 mos · San Francisco, California, United States · On-site

Built out various micro saas services that got viral adoption.

Rocket fuel inc.

5 roles

Director Of Engineering

Promoted

Mar 2017 – Oct 2017 · 7 mos · San Francisco Bay Area

RTB Platform (Bidding and Adserving Systems), User Store and other Real Time systems and services

Senior Engineering Manager

Jan 2016 – Feb 2017 · 1 yr 1 mo · San Francisco Bay Area

Engineering Manager

Jan 2015 – Dec 2015 · 11 mos · San Francisco Bay Area

Senior Software Engineer

Promoted

Jan 2014 – Dec 2014 · 11 mos · San Francisco Bay Area

Software Engineer

Jun 2012 – Dec 2013 · 1 yr 6 mos · San Francisco Bay Area

Bell laboratories

2 roles

Research Assistant

Aug 2011 – Jun 2012 · 10 mos · Bengaluru Area, India

Researching on:
+ improving quality of user similarity computation using local history and global consumption data.
+ enabling location privacy seamlessly using a design similar to P3 (See below for details on P3).
+ building a privacy aware, on-the-go realtime friend discovery application titled ‘Friendipity’.
+ establishing strict anonymity guarantees for P3.

Summer Research Intern

May 2011 – Jul 2011 · 2 mos · Bengaluru Area, India

+ Worked with Dr. Animesh Nandi on design, development and evaluation of a high performance, fault tolerant, large, distributed cloud based system for P3, the privacy preserving personalization project.
+ Used various technologies like Java, SOAP based web services, Apache Cassandra, Pastry, Memcached and Tor.
+ Built a log-based testing and monitoring framework for the system.
+ Infused various new ideas, researched on ways to improve the quality of recommendation algorithm and defined further research directions for the project.
+ Was highly appreciated by various heads at Bell Labs for innovativeness and rigor of the work.
P3 is a multi-party owned middleware based solution that aims to integrate seamlessly in the current internet landscape and help users enjoy personalization benefits like targeted ads, Google news recommendations, Foursquare place recommendations, etc without revealing their content consumption and identity to respective service providers. See http://bit.ly/p3-idea for more details.

User similarity computationPrivacy-aware applicationsResearchData Privacy

Aston university

Summer Reserch Intern

Jun 2010 – Jul 2010 · 1 mo · Birmingham, United Kingdom

+ Researched on visualizing web search results to enable users to view, explore and interact with search results as points on a 2-dimentional plane clustered by similarity with similar clusters lying close to each other.
+ Experimented with significant phrase extraction techniques and various dimensionality reduction algorithms to model the search result documents as a bag of phrases and reduce the space of documents to 2 dimensions keeping semantic relationships intact.
+ Learned about various NLP techniques like stemming, lemmatization, POS tagging, word sense disambiguation, collocation identification, etc and state of the art probabilistic learning algorithms for dimensionality reduction like GTM, NeuroScale, SOM, PCA, etc.
+ Quickly prototyped a text and language processing pipeline in Python using NLTK and added various features to Data Visualization and Modeling System in Matlab to analyze the results.

Distributed systemsCloud-based systemsSystem DesignCloud Computing