Blog

The internet decoupled labor from geography. Now AI is decoupling labor from humans, and George explains what that means for how organizations are built.

We built a distributed LLM request scheduler that intelligently routes billions of tokens per day across multiple providers so high-priority work always gets through, even under rate limits.

After pioneering semantic search and RAG, we found both fell short on the hardest questions so we scrapped them and built a new information retrieval system from scratch.

With OpenAI o1 now integrated into Matrix, Hebbia’s agents can draft longer outputs, parse denser legal documents, and reason through complex data extraction with greater accuracy than any prior model.

Hebbia raised a $130M Series B led by a16z to build the product layer for AI, the human-centric interface that makes the technology actually useful for the world’s leading financial institutions and law firms.

Hebbia built Matrix, an AI platform designed to handle tasks of any complexity, across any amount of data, with full transparency into how it thinks.

After years watching AI fall short for lawyers and consultants, Adi joined Hebbia because it was the first platform he’d seen that actually delivered on the promise of putting knowledge workers’ time back toward real work.

The models are already powerful enough. The reason AI hasn’t changed how we work is that nobody has built the right product around them yet.

After speaking with nearly 100 companies navigating the AI wave, Divya joined Hebbia because it was the rare team that had already moved beyond the hype and built something that actually works.

Charlie came to Hebbia as a skeptical buyside investor, became a power user, and eventually realized the only move was to join the team building the tool he couldn’t stop using.

Just as Excel fluency once separated good analysts from great ones, knowing how to prompt AI is becoming the baseline skill for knowledge workers.

After studying real search patterns across hundreds of users, Hebbia found that in five out of six cases, semantic search alone falls short of what knowledge workers actually need.