I help small marketing teams start ABM (account based marketing)

Abhishek Ekbote

Puneet Jindal

As the CEO of Labellerr AI, my focus is on unlocking the potential of AI teams through specialized training data for computer vision and multi-modal AI. 

We provide a superior alternative to generic annotation tools, ensuring high quality, responsiveness, and customization for U.S.-based AI ventures

With a tenure surpassing 4+ years at the helm of Labellerr AI, my expertise lies in constructing robust data annotation frameworks that support the unique needs of AI developers. 

Our platform's excellence is recognized in the industry, with a reputation for quality that rivals major players like Scale AI,  Labelbox, Roboflow, Encord. 

My commitment is to empower AI teams, enabling them to concentrate on core innovation, drawing from a decade of personal experience in the field.

Chief Executive Officer

We help fast growing AI teams in United States with high quality training labeled data for computer vision,  NLP, speech AI and other Multi-modal AI

You should prefer us if
1. you are looking to setup end to end data curation and annotation and evaluation pipeline 
2. you have sporadic and quick turn around time data annotation requirements 
3. Your workflow is not much fit in a generic data annotation tool
4. You might be already using open source such as CVAT, LabelStudio and now looking for a more robust, well documented, easy to use solution.

Our quality and responsiveness is better than Scale AI, Labelbox if you are before Series C funded and based out of United States

Read more at www.labellerr.com

First Look: Our open source QC Agent Preview is Live.

Quality Assurance on large volume labeled data is one of the biggest nightmare in the life of AI teams!

Sarthak Bhardwaj and myself have been quietly building a persistent-memory powered QC Agent and this is the first solid preview of how it looks in action.

This is not just another validation script.

It understands:
 • Annotation guidelines
 • Historical correction patterns
 • Project-specific edge cases
 • Persistent memory across review cycles

It already feels solid. Persistent memory, guideline grounding, and context-aware validation baked in from day one. The idea is simple: catch issues before they escalate, and make human QA more strategic instead of repetitive.

Still early, but the foundation looks strong.

Curious what you all think, when should we open up the code for this?

Follow Labellerr AI for more such updates!

Egocentric data and annotation workflows for Robotics

Elon Musk is behind the neck pain of 1000s of AI practitioners I know!

80% of AI development cost isn't in the model. It’s in the data.

I’ve spent the last decade building AI systems at companies like Walmart and Delhivery. 

The biggest lesson? 
Even the most sophisticated architecture fails on mediocre data.

At Labellerr AI we are building for the builders.

Our Mission: To democratise access to data by automating upto 90% of the preparation workflow delivering 99.5% accurate data at 10x the speed with domain experts in the loop.

Our Vision: be the foundational data engine for all AI lead autonomous systems world.

If you believe the future of AI is data-centric, let’s connect. 🚀

#AI #MachineLearning #DataScience #Founders #VentureCapital #LabellerrAI

AI is finally stepping out of our screens :)

Whats your thoughts?

Silicon Valley thinks the world has moved to AI, but in reality, only 1% to 8% of enterprises have actually moved past the pilot phase.

Why are we taxing the "Focus" of our AI Founders? 🇮🇳🚀

Building an AI startup from India is a war of attrition. Crossing the $1M, then $5M ARR mark while managing compute costs and global competition is hard enough.

But for Indian founders with a US presence (the "Flip"), the real friction isn't the code

it’s the compliance.

Right now, an Indian founder building for the world faces:

The "Stamp" Trap: Demanding physical signatures and stamps for US entities that don't even require audits locally.

The "Founder Tax": Late Submission Fees (LSF) and rigid Form APR filings that treat a 3-person team the same as a billion-dollar legacy biz.

The Focus Drain: Every hour spent on FEMA/ODI paperwork is an hour lost to building the next LLM or solving a customer pain point.

The Ask: A "Compliance Sandbox" for Startups under $10M Revenue.

If we want India to be the AI capital of the world, we need to relax the regulatory chokehold on early-stage companies:
✅ Tiered Compliance: Subsidize or waive Late Submission Fees.

✅ Digital-First Audits: Accept Management Certified accounts or US Tax filings instead of demanding "signed and stamped" physical audits for pre-scale entities.

✅ Automatic Waivers: Create a "safe harbor" for reporting delays until a startup crosses a certain funding or revenue threshold.

We have the talent. 

We have the ambition. 

Now we need a regulatory framework that moves at the speed of a sprint, not a marathon of paperwork.

Let founders build. 

The revenue will follow and so will the taxes.

#StartupIndia #AIFounders #RBI #FEMA #EaseOfDoingBusiness #BuildingFromIndia

While the world chats with AI, China🇨🇳 is building "Robot Cities" to train it.

I’m looking for 5 Indian colleges especially in Chandigarh and surrounding regions who are ready to pivot from placements to global R&D. 🇮🇳🤖

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Abhishek Ekbote

Puneet Jindal

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The Battle

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Puneet Jindal

Followers

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Puneet Jindal's LinkedIn Performance

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