Gorish Aggarwal

Sybill

Sybill is the AI co-pilot for B2B sellers. We don’t just capture your sales conversations—we automate and execute your follow-ups, CRM updates, and next steps, so you can focus on closing deals, not admin work.

💥 What Sybill Does:
✅ Auto-executes next steps: CRM updates, follow-ups, meeting scheduling
✅ AI-powered research: Pulls prospect insights from the web
✅ Auto-customizes sales collateral
✅ Works where sellers work—email, CRM, and LinkedIn

🚀 750+ sales teams trust Sybill
🔥 70%+ revenue from word-of-mouth
🤖 AI that doesn’t just track, it executes.

Started as AI that read facial expressions on Zoom. Now? The most advanced AI assistant helping sellers close more while doing less.

🚀 Hiring, Partnering, & Scaling—Let’s Connect!

Founding AE and CEO

Humans of Sales

📍 Bringing the human side of sales to life

Sales is more than quotas and cold calls—it’s about people. Humans of Sales shares real stories of sales reps: their wins, struggles, lessons, and moments that make this profession special.

Not a sales blog. No tactics. Just authentic, inspiring stories that redefine the sales narrative.

📅 New stories every Tuesday
🔹 Real Stories. Real Sales. Real People.

Follow #HumansOfSales to join the movement. 🚀

Founder 

Stanford University

Lead instructor of EE 263 in Q4 '19 and '20. Grad course on optimization theory and linear algebra

Lecturer

Samsung Electronics

Senior ML Engineer

CEO @ Sybill | Generative AI for Sales

Once upon a time, I was building AI to cure blindness, predict blood glucose levels, and even make drones land on moving vehicles. Now? I'm making sure B2B sales reps never have to update their CRM again (which, let's be honest, is a far greater cause).

At Sybill, we don’t just take notes—we execute next steps automatically. Follow-ups, CRM updates, scheduling, research—done for you. Do less, close more. 

750+ sales teams trust Sybill to do the boring stuff, so they can close more deals—and we’ve raised $15M to make it even smarter.

Also started Humans of Sales—because sales is more than quotas and cold calls. It’s about real people.

Follow for AI, sales, and spicy takes. 🚀

Member

RevGenius is a community of revenue-generating sales and marketing professionals brought together to learn, share, support, and grow with each other. Our community is remote-friendly, collaborative, and loves to help. You can consider us your alt work family!


If you're not onboarding customers in-person, can you really say you're "customer obsessed?" I kid (mostly) 😂 

Auditoria.AI signed up for Sybill recently, and Nishit Asnani (Hiring Founding AEs) and I got to onboard their team in person.

Did we actually need to be there in person? Not really.

Part of why teams love Sybill is that it's easy to get up-and-running on your own, so there's no real hand holding required.

But the chance to get their feedback, hear their questions, and watch them run Ask Sybill queries in person was worth it's weight in gold.

It also doesn't hurt that they're here in SF.

Cheers to many more in-person onboarding sessions 


Thank you to Akash Bose and David Osborne

My use of ChatGPT and Claude dropped by 99% in one day. 

Sybill has been able to answer directly from our calls and deals for a while now, but last week we shipped document upload and web search.

Now Sybill can search for answers in your calls, emails, and CRM, but if it can't find an answer, it will take to searching the web or working directly from a document I've uploaded.

So I straight up don't need ChatGPT or Claude for almost anything anymore.

It's wild.

If your teams are tired of copy+pasting transcripts from Gong into ChatGPT, let's talk.

Nishit Asnani (Hiring Founding AEs) Soumyarka Mondal Mehak Aggarwal

Yesterday, 50+ people showed up to see how Sybill is different from Gong.

We didn’t compare features.
We didn’t run a bake-off.
We didn’t take cheap shots at Gong.

We showed how Sybill and Gong are built on two completely different foundations with two completely different POVs about how revenue actually moves forward.

And those choices we made create massive differences in outcomes for revenue teams.

Recording in comments 🚀

Gong’s prices keep going up. But is anyone getting more value?

Not according to the sales leaders I talk to every day.

Not too long ago, Gong felt like the future of sales.

Clear value prop. Big campaigns. Category pioneer. Everyone wanted it.

But here's what sellers tell me now:

→ Prices are going up… again.
→ AEs pay $$$ for licenses but use it like a notetaker
→ Their AI can’t think it’s way out of a box
→ Forecasting is the only feature that works
→ CRM still needs manual updating after calls
→ No more innovation; copying competitors
→ Too many dashboards, not enough action

The real question: Has Gong become the ghost of revenue AI’s past?

We'll answer this question at next week's webinar:

Sybill vs. Gong: “The Ghost of Revenue AI's Past.”

(Can you guess our answer? :P)

We’ll cover:
→ How sales AI has evolved since Gong's hay day
→ Why Gong’s multi-agent architecture hurts, not helps
→ How Sybill uses AI to keep deals moving forward
→ Why some rip/replace Gong and others layer Sybill
→ And why sellers at Warmly, Aligned, Deal, Drata, and Perplexity are choosing Sybill for sales AI and execution.

Thursday, 12/4 at 8am PST.

Register and get all the deets below.

The moment you win a customer is also the moment you’re most likely to lose them.

The customer handoff.

That’s when momentum is fragile.

Excitement turns to uncertainty.

And one clumsy handoff can undo months of trust built during the sale.

Case in point: Cloudingo, a Salesforce-native data quality SaaS with a lean, full-cycle sales team.

Their sellers run everything - from first touch through renewal - juggling outbound, partners, demos, and onboarding.

When every deal crosses multiple hands, it’s easy for key context to slip.
And buyers feel it instantly.

As their VP of strategic alliances puts it: “We wear a handful of hats. What we needed wasn’t a heavyweight platform; we just needed to make sure the handoff from sales to onboarding didn’t feel like starting over.”

When we looked at their handoff flow, a few friction points stood out:
➡️ Long sales cycles with multiple discovery and demo calls
➡️ Onboarding starting with “remind me what you bought again?”
➡️ Notes scattered across calls, emails, and Slack

The result: slow onboarding, frustrated buyers, and lost momentum right after the close.

Here’s how we helped them fix it with Sybill.

1. Deal insights captured automatically
Sybill summarizes every call and email. Then then it writes details like pain points, goals, integration requirements, and migration risks straight into Salesforce fields automatically.
No manual entry. No missing context for customer success.

2. Standardized handoff templates
Before onboarding begins, reps use a saved templates in Sybill to generate a concise (and standardized) summary: what was sold, who the stakeholders are, and what matters most to them.
Everyone gets the same summary format. No surprises.

3. On-demand context for the next owner
Onboarding and CS can ask Sybill:
➡️ “What did we commit to during sales?”
➡️ “Who’s the main champion?”
➡️ “What are the success criteria for this account?”

And instantly get a clear, context-aware summary spanning every interaction.

The results?
➡️ Faster onboarding kickoffs
➡️ Fewer dropped balls between teams
➡️ Stronger customer momentum after the close

Because the sale isn’t really “won” when it closes.

It’s won when the customer’s confidence carries through to value.

Want to tighten your transition between sales and CS?

We built a free customer handoff AI prompt template, similar to the recent customer, a Salesforce native. uses to keep every customer handoff consistent and fast.

Link in comments below.

Another year of using Gong has come and gone, and here’s your Gong Wrapped.

➡️ 219 transcripts you copied into ChatGPT
➡️ 646 recordings you dug through to get answers
➡️ 37 trackers you made, still missing competitors
➡️ 429 times double-checking the AI summaries
➡️ 20+% pricing increases

And all the headaches that came with your “revenue intelligence”.
 
If this Wrapped feels familiar, come to our webinar tomorrow.

👉 Sybill vs. Gong: The Ghost of Revenue AI's Past 

We’ll put Gong and Sybill head to head, covering:

• Why Gong's legacy architecture can't match Sybill's AI-native architecture.
• Intelligence: How Sybill reasons across every deal, call, email, and CRM field
• Mid-Deal Execution: Why centering the AE is the way to improve sales performance long term
• Pricing: Why many sales teams say Gong feels like an overpriced notetaker
• Switching Paths: How teams either rip-and-replace or layer Sybill on top of Gong with zero workflow change

🗓️ Thursday, Dec 4 (tomorrow)

⏱️ 8am PST

🔗 Link to register in comments

Gong Wrapped Carousel

Sales teams don’t need 100 AI agents. Sorry.

They need just ONE truly agentic AI assistant.

There’s a growing trend in AI right now: everyone’s building “multi-agent architectures.”

Sounds impressive.

Like you get a whole team of AIs, each with their own specialty.

But here’s what most people don’t realize:

Agents ≠ Assistants.

An AI agent is a narrow tool triggered by rules you manage.

An AI assistant is one unified system that can perceive → reason → plan → act 
- with shared memory and context across everything it does.

In practice?

Most “multi-agent architectures” are just a bunch of glorified workflows passing half-understood context back and forth (like Zapier automations rebranded).

Agent A pulls notes from a call.
Agent B writes a follow-up.
Agent C drafts a Slack update.
Agent D updates the CRM.

Except none of them share a global memory, so each one is working off a slightly different version of reality.

Early on at Sybill, we had the same choice: build a multi-agent platform or a single-agent assistant.

We tried multi-agent. It broke constantly.

What we discovered was this:
Multiple agents retrieve slightly different contexts. 
They reason independently. 
They have no unified brain.

Every additional agent multiplies communication protocols, context handoffs, and error-handling headaches.

The truth: most teams building this stuff are running distributed chaos, not distributed intelligence.

So we do it differently now.

Sybill is one agentic system - not a swarm.

One reasoning brain with depth, not a collection of shallow task bots.

One shared memory across deals, not 100 disconnected mini-brains.

One autonomous assistant that takes initiative, not a stack of tools waiting for triggers.

When it learns something new about a deal, it applies that understanding everywhere, from follow-ups to CRM updates to coaching recommendations.

When we improve retrieval or reasoning, the entire system gets smarter overnight.

The tradeoff?

We give up the illusion of “more agents = more power” for something far more valuable:

Coherence. Reliability. Compounding intelligence.

Don’t buy the hype (or the headache).

Most “AI agents” out there are marketing sizzle, not real agentic AI.

Real agency isn’t about how many bots you have.

It’s about how well one system can understand, reason, and act.

I used Gong at my previous company, and I knew I didn't want to bring it here.

We hear a version of this all the time. 

Because teams aren't looking for another system to watch deals.

They want a system that actually works the deal with them.

That's the shift happening in revenue AI right now:

➡️ From dashboards ➡️ to decisions
➡️ From analysis ➡️ to action
➡️ From monitoring reps ➡️ to empowering them
➡️ From fragmented "agents" ➡️ to a single unified brain

Gong was designed for the last era.

Sybill was built for the next one.

And on Dec 4, we're breaking it all down.

Where Gong is going. Where Sybill already is.

Sybill vs. Gong: The Ghost of Revenue AI's Past
🗓️ Thursday, Dec 4 (in two days)
⏰ 8am PST

🔗 Link to register in comments

What we'll cover:
• Architecture: Why Gong's multi-agent stack can't match Sybill's unified AI brain
• Intelligence: How Sybill reasons across every deal, call, email, and CRM field
• Mid-Deal Execution: Why centering the AE is the only way to improve sales performance long term
• Pricing Reality: Why many sales teams say Gong feels like an overpriced notetaker
• Switching Paths: How teams either rip-and-replace or layer Sybill on top of Gong with zero workflow change

See you there!

Ghost of Gong

Nothing beats watching customers go to bat for you.

Thanks, Jeremy! ❤️

graphical user interface, text, application

Most sellers I've met are verbal processors.

They think out loud, talk through objections, and refine their pitch by saying it, not writing it.

But when it comes to prepping for calls or researching accounts? They're stuck typing into a search bar.

That's why we just launched Dictation in Ask Sybill.

Now you can ask questions about your pipeline the way you naturally think - out loud.

Just ask.

P.s. We'll be covering a ton of other new features in tomorrow's What's New webinar.

Link: https://lnkd.in/gq2W-b6W

I'm a CEO, but I am also a seller.

And I love and hate it.

I love the feeling of nailing a pitch, watching a prospects eyes light up when they see what Sybill can do.

But I hate, hate, hate the massive pile of mindless tasks that comes with being a seller.

The email follow-ups, the document collection, the calendar invites. They feel small when you're only doing one or two, but work 10, 20 deals and the "little" tasks add up.

It's like suffocating by 1000 small tasks.

And for my brain, it's a massive drain.

So when I say that the AI task execution our team built straight up changed my life, I'm not kidding.

I'd love to show it to you.

This Thursday Collin Mayjack and I are running What's New in Sybill, an hour live demo of all the newest stuff we've built.

No fluff. Just straight up before and afters of what our team has been building.

12/10 at 8:30am PST. Link in comments.

Oobau

Co-Founder

Stealth Mode

Building the next-gen human-assisted Intelligent CRM!

Building

Mastercard AI Garage

Building Foundational Models and LLM capabilities @ Mastercard

Data Scientist

SayF

Product and Tech

Rendezvous

Core Team Member, Marketing

- Developed an Intermittent Demand Forecasting Model, beating the current State-Of-The-Art Model by 5% in RMSSE score
- Implemented a Probabilistic, AR-based algorithm using weighted loss function; Performed benchmarking on M5 & UCI dataset
The above work got accepted in ICDM 2023 (AI4TS Workshop) - https://www.computer.org/csdl/proceedings-article/icdmw/2023/816400b591/1UjHZsbmxC8
Awarded PPO

AI Research Intern

NUS Business School

Worked with Prof. Karthik Narayan
- Performed sentiment analysis using BERT by scraping 1M+ professors' reviews of 100+ colleges from ratemyprofessor.com
- Operated extensive secondary research to analyse the Impact of Online Teaching on US-based Universities over 5 parameters

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