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’?

Apple

Machine Learning Engineer

Anheuser-Busch InBev

Data Scientist

Product Management Intern

Cornell University

Degree: Master of Engineering in Computer Science
Awards: Cornell Tech Merit Scholarship

Masters Student

ServiceNow

♦ Platform UX Engineering Team: Development and maintenance of platform features
♦ Developed feature to identify labels for attachments with object recognition and machine learning (Named Entity Recognition), enabling hits in search results for image and text uploads. Framework used: OpenNLP (Java)
♦ Winner, Mini Project Hackathon, ServiceNow 2018

Associate Software Engineer

Solar Energy Research Institute of Singapore

♦ Optimized parameters for solar cell manufacture using a shallow Convolutional Neural Network(CNN)
to increase module efficiency. Trained on TensorFlow, and deployed with TensorFlow Serving.
♦ Implemented transfer learning for Silicon sheets with varying wafer properties and predicted process parameters to match efficiencies across manufacturing recipes.
♦ Predicted solar cell performance across climates. Analyzed influence on performance from weather
conditions as a part of the TruePower Project with Random Forest Regression with accuracy above 0.94.
♦ Visually detected defects in solar modules with YOLO algorithm and CNNs.

Research Intern

National Centre for Biological Sciences

♦ Simulated the Stable Matching Problem in a biological population to converge on a fitness function for Pareto efficiency.
♦ Participant of Physics of Life, Monsoon School 2016

Summer Intern

Tata Consultancy Services

Intern

SENSEI Technologies

Graduated from Cornell Tech, New York - Master's in Computer Science, with experience as a data scientist and a software engineer.

Interested in industry applications of Machine Learning.
Please reach out at: aks298@cornell.edu

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