Tooba what's your backstory?

I grew up between cities in the East and the West, raised in an Eastern household full of teachers, poets, and engineers. It was a bit of a paradox: deeply curious by nature, but constantly told to color inside the lines. I spent much of my childhood hacking together games and reading science fiction under the covers — convinced I’d either end up building robots or writing about them — or become a WWE wrestler.

Before Amoeba, I was deep in the world of research and applied AI. I earned my PhD in Artificial Intelligence and Data Science from MIT, where my work focused on neuro-symbolic learning and non-linear modeling in high-stakes environments like healthcare and smart cities. Along the way, I worked with a mix of startups and enterprise teams, helping them make sense of messy, complex data to drive real decisions, and saw firsthand how broken the tooling was for non-technical teams.

What finally pushed me into founder life was a simple but frustrating pattern: brilliant go-to-market teams making million-dollar decisions with week-old dashboards, static spreadsheets, or blind instinct. I realized we needed a new kind of intelligence — not just faster or prettier — but something that could reason, adapt, and explain itself. Amoeba was born from that conviction.

I’ve had the chance to live in five cities and work across three continents, and every step of the way has made me more obsessed with the question: how do we make intelligence feel more human?‍

What does Amoeba AI do, and how did you come up with the idea?

Amoeba is a neuro-symbolic AI platform that acts like a data scientist embedded inside your go-to-market (GTM) team. Instead of static dashboards or spreadsheets, Amoeba uses a mix of neural networks and causal reasoning to generate proactive insights, simulate outcomes, and recommend high-leverage actions — all grounded in your actual business goals.

The idea came from years of watching brilliant marketing and revenue teams make high-stakes decisions with either too little data or too much noise. I was working on complex AI systems at MIT, but every time I interfaced with go-to-market teams — whether at startups or large organizations — I saw the same thing: tools that were either too generic, too technical, or just too slow. I wanted to build something that felt more like a thinking partner than a dashboard. That’s Amoeba.

How did your experiences influence the creation of Amoeba AI?

My work has always sat at the intersection of intelligence and action. At MIT, I studied how to apply neuro-symbolic learning in dynamic environments — like predicting health outcomes or optimizing urban systems. But it wasn’t until I started working with commercial GTM teams that I saw how urgently those same approaches were needed in business.

At the World Economic Forum, I worked on data collaboratives where stakeholders had to make policy decisions based on conflicting or incomplete data. At Qualified, I saw firsthand how real-time behavioral signals could be used to surface buying intent, but also how hard it was for RevOps or Marketing teams to connect those signals back to strategy.

Amoeba is the synthesis of that journey: applied AI that’s smart enough to understand your business, but intuitive enough to act on without a PhD.

How did you acquire your first 20 users, and what strategies worked?

We were scrappy. I started by doing live audits for marketing teams — I’d take their CRM and campaign data, run it through Amoeba, and give them a one-page strategy brief with insights, risks, and recommendations. That built immediate trust and curiosity.

From there, I leveraged my operator and founder network. RevOps leaders, growth marketers, and a few early investors helped introduce Amoeba to GTM teams who were frustrated with their BI tools or struggling to scale experimentation. We also ran a few targeted sprints using data from past campaigns, showing how Amoeba could simulate outcomes or detect early signals that would’ve otherwise been missed.
What worked wasn’t just the tech — it was showing that we understood the job of being on the hook for pipeline, retention, or ROAS (Return on Ad Spend).

What key metrics or user feedback confirmed that you were achieving product-market fit?

The turning point came when users stopped asking, “What does this tool do?” and started saying, “I use Amoeba before every pipeline review.” We saw consistent retention across marketing and RevOps personas, and the most validating signal was when users forwarded Amoeba outputs to their CFOs or Board members.

Quantitatively, we saw 60%+ weekly engagement across our early customer base and measurable business lift — things like a 25% increase in ROAS (Return on Ad Spend), faster experiment cycles, or doubling the volume of SQLs (Sales Qualified Leads) from the same budget. But more importantly, teams told us it felt like they had a strategist sitting next to them. That emotional resonance was a stronger sign of fit than any metric.

What distribution channels have been the most effective in reaching your audience, and how did you discover them?

Our most effective distribution channel has been founder-led sales with strategic content. I’d speak at events, publish tear-downs of GTM strategies, or share real insights from pilot data — and that sparked conversations. People don’t want another demo of charts; they want to see how an insight would change their QBR or campaign plan.

We also experimented with partnerships, especially platforms where GTM data already lives — like CRMs (Customer Relationship Management systems), CDPs (Customer Data Platforms), or ad platforms. That helped us meet users in their workflow rather than asking them to come to us.

Ultimately, the magic happened when we combined credibility (via my background or pilot results) with storytelling (here’s how your team could make this decision faster and better).

If you could only use one channel to reach your audience, which would it be—and why?

I’d bet on embedded experiences — placing Amoeba inside the systems GTM teams already use, like HubSpot, Salesforce, or campaign orchestration tools. That’s where decisions are made and urgency lives.

An AI agent is most useful when it feels like it’s right there when you need it — not when it requires switching context or opening a new tab. By embedding Amoeba into the core operating system of GTM teams, we can drive higher usage, trust, and value per interaction.

That said, I’ll always keep a direct line open to customers. The best distribution channel is still: “Hey Tooba, can you help me figure this out?

What sets Amoeba AI apart in the competitive AI/data science space?

Most tools today are either dashboards with AI lipstick or black-box models with no business context. Amoeba’s core innovation is that it fuses two worlds: symbolic reasoning (which understands goals, strategy, and causality) and neural networks (which handle patterns and noise).

This neuro-symbolic architecture gives us three advantages:

• Adaptability — we perform well even as data drifts or behavior changes.
• Explainability — every insight is tied back to a business goal or driver.
• Agency — Amoeba doesn’t just answer questions, it recommends actions.

We’re not trying to be another BI tool. We’re building a cognitive copilot — one that thinks in business terms and acts in real-time.

Since Amoeba AI replaces traditional BI tools, how does that shape your revenue model?

Most BI tools monetize access: dashboards, user seats, and exports. But that model assumes the value is in visualization — when the real value is decision confidence.

Amoeba flips this. We price based on value-generating activities — not on how many users log in, but how often teams use Amoeba to drive insight, simulate a strategy, or optimize an outcome. Our AI agents replace multiple roles: analyst, strategist, data prepper — so the ROI compounds fast, especially for lean teams.

This value-based structure reflects what we remove: the need for engineering support, manual data stitching, or weeks-long reporting cycles. And because Amoeba runs as an embedded intelligence layer, not just a standalone tool, our model scales naturally with impact.

With AI and automation on the rise, how do you see GTM strategies evolving?

The biggest shift coming is from reactive reporting to proactive orchestration. Today’s GTM teams spend hours each week aligning on what’s happening. In the near future, AI agents will not only tell you what happened but why it happened — and what to do next.

We’re moving toward a world where strategy loops are compressed: data becomes insight, insight becomes action, and action gets tested — all in near real-time.

Amoeba is built for that future. Our agents don’t just answer questions — they simulate scenarios, track causality, and help teams adapt before the metric drops. We believe GTM will become more fluid, more experiment-driven, and more intelligent — and we’re building the agentic infrastructure to power that evolution.

What’s your vision for Amoeba AI in the future?

In the long term, we see Amoeba as the cognitive engine behind every major GTM decision — from how much to spend on LinkedIn this week to which customer segment to double down on next quarter.

In terms of business model, we’re focused on ARR today, with high-value integrations and advisory features layered on top. But we're also designing the platform to extend into other verticals — customer success, product-led growth, and even finance.

Whether we continue scaling independently or partner with a larger platform depends on what accelerates our mission: giving every business team the ability to think like a data scientist without needing to hire one.

Who are some entrepreneurs or experts you recommend following for business growth?

I’ve learned a lot from people who build with both craft and velocity. A few favorites:

And honestly? Some of the most tactical lessons come from early-stage founders just a few steps ahead. I try to keep close to those conversations.

What advice would you give founders trying to pick the right distribution channel?

Don’t start with channels — start with urgency. Where is your user feeling pain right now? What moment in their workflow is so painful they’ll stop what they’re doing to fix it?

Once you find that wedge, show up in the places where they’re actively looking for solutions — not where you wish they were. That might be Slack communities, integration marketplaces, or even live strategy audits.

And remember: distribution is a conversation, not a campaign. The more you talk to users, the faster you’ll find your signal. Our early growth didn’t come from ads — it came from showing up, solving a real problem, and making it feel like magic.

What drives you to do what you do?

I’m obsessed with intelligence — not just artificial, but applied. I’ve spent years studying how humans and machines make decisions under uncertainty, and I kept seeing the same tension: we have more data than ever, but the decisions aren’t getting easier.

What drives me is this belief: the right information, at the right time, in the right hands — can change everything. I don’t think data should feel like a wall. It should feel like a superpower.

Amoeba is my way of giving that power to the teams who actually move the business forward.

Are there any guiding principles or quotes you live by?

A few that keep me grounded:

“Strong opinions, loosely held.”

It reminds me to lead with conviction but stay open to better truths.

“You can’t be what you can’t see.”

It’s why representation — especially in AI and leadership — matters deeply to me.

And one I return to often:

“Clarity is kindness.”

Whether it’s writing code, pitching investors, or building culture — being clear is one of the most powerful forms of care.

Any promotions you would like to add for Founderoo readers?

Founderoo readers can get access for $500/month for 12 months - over 50% discount. To use, you can reach out to me directly at tooba@amoeb.ai

Your links + socials

Tooba LinkedIn

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