Divyansh Lohia

Katalyst

The AI agent for account development | Automate key workflows and get 80% time back talking to more customers.

Stealth - Feb to Sep

Co-Founder, CEO

RevGenius

RevGenius is a community of sales and gtm leaders.

Member

Datadog

Led GTM for Datadog Real User Monitoring, Session Replay and Mobile Application Testing taking them from 60m-100m ARR in <1 yr.

Product Marketing Manager

Eastrivr

Direct-to-Consumer brand that makes lifestyle products ethically & sustainably. 0-200k ARR in 2yrs w/ zero funding. Designed in NYC, made in India.

Founder & CEO

AB InBev

Product Owner of MarTech's mROI Project | Beer Garage Incubator.

Product Manager

Weill Cornell Medicine

Implemented dashboard monitoring cardiovascular and mental health of migrant asylum seekers via Fitbit & Apple Watch.

True Good Essentials LLP

1st Product Manager at True Good; Complete ownership of the “True Good” Android/IOS App

Aidapp

“Saledrive” enterprise SaaS that helps SMEs onboard and real-time manage on-field sales teams.

CEO & Co-Founder

Ginesys

- Full-stack implementation of internal app to manage client compliances

Software Engineer

Building getkatalyst.app. Automate account development and get 80% of time back talking to more customers.

Sign up for early access at getkatalyst.app

AEs spend 80% of their time in account development activities (daily meeting prep, client 1 pager, account research, follow-ups, CRM scribing). Katalyst does all that on your behalf, so you can handle 10x deal flow. 

DM me if you have any questions. 

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I've founded/worked at a couple of startups (Eastrivr, Aidapp, Truegood), a couple of big co's (Datadog, AB-InBev) and currently building in the future of sales tech @Katalyst. We are a top-tier team of (tech+gtm) ex-Datadog, Google, Tesla and as outsiders rethinking enterprise sales.    

My Journey in 1 line:  SWE --> PM --> PMM --> Founder doing everything.

Follow me to stay on top of future of enterprise sales with AI.

Co-Founder & CEO

Building a Direct-to-Consumer brand that makes lifestyle products ethically & sustainably. 

If you would like to work in setting the standard on how High Achievers live and move, please reach out!

GTM doesn’t break overnight.
It breaks slowly - in the whispers, not the alarms.

It breaks when reps stop following up consistently.
When deals stall in mid-funnel for ‘no reason.’
When CRM updates lag by a week.
When the team confuses activity with progress.
When leaders keep pushing for more top-of-funnel volume instead of fixing the leaks right in front of them.

For a long time, I confused a ‘busy’ pipeline with a ‘healthy’ one.

We had deals.
We had calls.
We had demos.
We had activity.

But deals weren’t moving.

The loud failure happens when revenue misses the quarter.
The quiet failure happens months before - when deals start losing momentum silently and you don’t notice.

The turning point for us was seeing deals die not because of product fit, but because of:
→ fragmented data
→ tool overload
→ lost context
→ slow engagement
→ lack of multi-threading
→ reps stretched too thin

The fix wasn’t more tools.
It wasn’t more leads.
It wasn’t more sequences.

It was simply this:
Care more about the deals you already have than the leads you don’t.

GTM teams rarely fail because they don’t generate enough interest.
They fail because they can’t convert the interest they already earned.

When did you realize your GTM wasn’t failing loudly - it was failing quietly?

Most teams obsess over top of funnel.
Very few obsess over the middle of the funnel - and that’s exactly where most deals die.

It’s comfortable to generate more leads, buy more lists, fire off more sequences, push volume. It feels like progress.

It’s much harder to admit that your existing opportunities aren’t moving because:

Your CRM is fragmented.
Your data lives in 6 different tools.
Your reps lose context.
Your follow-ups are inconsistent.
Your pipeline signals are weak.
Your team can’t multi-thread effectively.
and only the top 20% of sellers actually know how to build relationships.

I saw this firsthand.
Back when I was running sales alone with 15+ deals in the pipeline, I’d see prospects slowly reduce replies, lose context, or disappear altogether.

Not because the deal wasn’t a good fit.
But because I failed to engage them at the right moments.

Deals don’t fall through loudly.

They leak quietly.

The root cause?
People, process, and tooling - all misaligned.

The fix?
Shift from ‘more leads’ → ‘more momentum on existing deals.’

Because optimizing the middle of the funnel requires:

Consistent value at every touch.
Better signals.
Cleaner context.
Vertical AI that understands your pipeline and moves deals forward.
And leadership willing to invest in the hard things.

It’s not glamorous.
But it’s the difference between revenue engine and revenue theatre.

P.S. What’s one GTM problem you think companies still pretend doesn’t exist?

As a founder, I spent too long solving the wrong problem.

We thought our ‘customer’ was the salesperson - the rep trying to get through their daily workflow, juggling 6 different tools, updating CRM, fighting data redundancy, trying to squeeze more output out of broken systems. 

It made sense at the time. Engineers are trained to obsess over the end user. So we followed that instinct: fix the rep’s pain, improve their daily workflow, give them superpowers.

But the more we built, the more something felt off.

Reps loved the product. Usage was high. Feature requests kept coming.

But no one wanted to pay.

Because the real customer wasn’t the user.

It was the CRO - the person responsible for revenue, funnels, team performance, and targets.

Their pain was completely different.
- Deals stuck in mid-funnel.
- 60 - 70% of sellers are underperforming.
- Reps drowning in admin work.
- No visibility, no signals, no momentum.

We were solving workflow pain when the buying power cared about revenue pain.

The turning point?
When 50+ reps from enterprise companies onboarded to our platform and not a single one had the agency - or budget - to buy what they were using.

That’s when it clicked:
Build for the buyer. Not the user.

Once we shifted to enterprise sales, long cycles, custom pricing, value-based selling - everything changed.

Because we were finally solving the pain that actually mattered.

Today, our rule is simple:
Only build features that generate measurable revenue impact. Everything else is noise.

P.S. What’s one time you realized you were selling to the wrong ‘customer’?

Deloitte

◦ Leading and Developing GenAI solutions in the Tax & Legal space
◦ Senior Data Scientist in the Data & AI team within Tax & Legal - Digital Innovation

Manager - Senior Data Scientist

Flipped.ai

◦ Leading the AI research team in the development of a state-of-the-art CV parsers and skills-based recommendation engines.
◦ Implemented cutting-edge techniques of NLP, to extract nuanced semantic and syntactic information from structured and unstructured data.
◦ Engineered and implemented NLP & LLM-backed hiring co-pilot, reducing manual efforts by xx%.

Data Science Lead

Senior Data Scientist

◦ Joined the founding team (veterans from NYU and University of Cambridge) as a software engineer. Contributed significantly to the mission of empowering the next 3 billion mobile users, enabling seamless interaction, transactions, and monetization within local content and communities.

◦ Architected and implemented a robust backend infrastructure, comprising multiple microservices, to support the hyperlocal engagement platform's ambitious vision. This involved creating scalable solutions that ensured efficient connectivity to the last mile.

◦ Innovatively developed an edge-based content-ad engine, integrating Vickrey auction principles from game theory. This led to the incubation of the solution at Meta (Facebook) London Accelerator in '19.

Data Scientist

◦ Offered PPO (Pre Placement Offer) for great work and collaboration!

Data Science Intern

Parakrama

* A short stint in my entrepreneurial journey! *

Pioneered the conceptualization of Parakrama, a groundbreaking personalized geriatric care solution powered by AI. 

Led Parakrama to :
- Incubation at WEBEL Tech Incubation Centre.
- Secured a position in the top 10% of applicants at Y Combinator's S'21 cohort.
- Finalist at the Texas Healthcare Hackathon '21, with additional support for incubation.

Co-Founder

SRM University (Sri Ramaswamy Memorial University)

Supervisor : Prof. S. Dhanalakshmi
◦ Delved into cutting-edge research on Carotid Artery Abnormalities using Computational Intelligence.
◦ Implemented novel pre-processing techniques and benchmarked results, leveraging Capsule Network to enhance predictive accuracy and overcome translational variance.

Supervisor : Prof. A. Shanthini
◦ Collaborated in the Prognosis of Microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy. 
◦ Applied Deep Convolutional Neural Networks, recommending optimal algorithms and models to achieve maximum validation accuracy within the given dataset.

Supervisor : Prof. P. Eswaran
◦ Engaged in research on Smart Water Irrigation for Industry 4.0. 
◦ Developed a microprocessor prototype managing MQTT communications and implemented an interactive web interface through Flask, contributing to the advancement of efficient and sustainable water management practices.

Skills : Python · Pandas · Numpy · skLearn · Tensorflow · CNN · MQTT Protocol · Arduino · ESP8266 · Flask · Firebase(FCM)

Undergraduate Research Assistant

Indian Institute of Technology, Kharagpur

Supervisors : Prof. Niloy Ganguly and Dr. Madhumita Mallick

◦ Investigated "Transient Anomaly Detection in Smart Homes" as a key contributor within the Complex Networks Research Group [CNeRG] at IIT Kharagpur.

◦ Applied expertise in data analytics by implementing the Apriori Algorithm and utilizing a fault injection method incorporating Weibull Distribution and a 2-state Markov Model. This approach correlated sensor data from Smart Devices to predict Transient Anomalies based on Activity of Daily Life (ADLs).

Research Intern

SkyBits Technologies Pvt. Ltd.

◦ Developed a Mood Detection model using pre-trained VGG-16 architecture which detects 6 different type of moods utilizing FER-2013 repository and deployed the model through an interactive webapp using Django.

Skills : Python · Pandas · Numpy · skLearn · Tensorflow · CNN · Django

Computer Vision Intern

◦ Researched and presented a white paper on ”Demystify the Proposal of a Smart City in Bidhannagar and Rajarhat using Machine Learning, IoT and AI”

◦ Explored innovative approaches to urban development by integrating Machine Learning, IoT, and AI technologies, providing valuable insights and strategic recommendations for the transformation of Bidhannagar and Rajarhat into Smart Cities.

Summer Intern

Arko is a Manager - Senior Data Scientist at Deloitte UK in the Data & AI team - Digital Innovation under Tax & Legal service line. 

He brings proven track record of over 6 years in the tech industry, with a strong emphasis on Natural Language Processing (NLP) and expertise in Large Language Models (LLMs) and Generative AI .

He graduated Masters with Distinction in Data Science from Queen Mary University of London (QMUL) advised by Dr. Ignacio Castro.

Previously, he led AI research and Product at flipped.ai, spin out of NYU that's revolutionizing talent intelligence capabilities through GenAI. Prior to that, he conceptualized and built Parakrama, a personalized geriatric care solution powered by AI, which was among the top 10% of applicants at Y Combinator Summer '21 and also interned at IIT Kharagpur and Hewlett Packard.

When he dosen't </>, he loves to go out for long drives and would give anything to have a quick nap :)

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