Antriv Labs – We Create, Preserve, and Transform.

At Antriv Labs, we believe in a future where technology is deeply personal, fiercely private, and unbelievably fast. Born with a bold vision and rooted in ancient wisdom, our name is inspired by the divine trinity – Brahma (the creator), Vishnu (the preserver), and Shiva (the transformer). Just like this cosmic balance, our mission is to create powerful AI solutions, preserve the sanctity of user data, and transform the way India interacts with artificial intelligence.

We aren't trying to do everything — we’re focused on doing one thing exceptionally well. Our task-specific language models and semantic search systems are designed to outperform general-purpose AI — delivering results up to 99% faster than OpenAI. How? Because we build with laser focus, custom-trained models, and deep understanding of the problem — not bloated, one-size-fits-all solutions.

Our core values are simple but powerful:

Built for Privacy — Your data is only yours. Our models run on your servers, with no third-party access, no leakage, and complete transparency.

Built Just for You — Custom AI solutions tailored to your specific workflows, trained on your own data, delivering unmatched speed and accuracy.

We also power semantic search for modern India. Through simple embedded code and ready-made plugins for Shopify and WordPress, we enable D2C brands to offer lightning-fast, intent-based search experiences — starting with eCommerce and expanding to every digital frontier.

And the best part? We’re democratizing this power — all at the cost of a small pizza.

Proudly made in India, made for India, Antriv Labs is here to build the future of AI — one that’s fast, focused, private, and deeply personal.

Our Approach

• Laser-focused models ≫ higher accuracy on domain tasks
• Up to 10–50× cheaper to run in production
• Predictable outputs; easier to validate & govern
• Private deployment — bring the model to your data

LLMs (General-Purpose)

• Broad reasoning, but inconsistent for narrow tasks
• Higher latency & compute costs at scale
• Output drift; harder to constrain precisely
• Often requires sending data to third parties