Our mission is to give every lawyer an edge
Built by practicing lawyers, advised by leading legal minds, and backed by a growing team of 20+ engineers, designers, and AI researchers.
The Team
People behind the platform
Irys was founded by lawyers who lived the problem — and engineers who refused to accept the usual shortcuts.
Sabih Siddiqi
Founder & CEO
BigLaw trial attorney & transaction counsel with 7+ years' experience.
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Devansh
Co-Founder & Head of AI
Builds the reasoning infrastructure behind Irys One. Public AI researcher.
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Built because the alternatives were not good enough
Irys was founded after its co-founders spent years watching legal AI products fail in practice — not in demos. The demos were impressive. The software confabulated citations, collapsed under jurisdiction complexity, and offered no audit trail a lawyer could use to satisfy their supervision obligation.
The problem was not the models. It was the architecture. General-purpose retrieval pipelines were being asked to do legal work without understanding the hierarchy of legal authority, the structure of citation chains, or the difference between finding a case and knowing what it means.
Two years of development later, Irys One launched as the first platform designed from the ground up to meet the precision standard legal practice actually demands — with matter-aware intelligence, jurisdiction-grounded retrieval, and audit provenance built into the architecture, not bolted on afterward.
Our Values
What we believe
Precision Over Speed
We build tools that meet the professional responsibility standard lawyers already carry — not tools that trade accuracy for impressiveness.
Lawyer-First Design
Every architectural decision starts with the lawyer's workflow, not the model's capabilities. AI should serve counsel, not replace judgment.
Accountability by Default
Full audit provenance is a requirement, not a feature. Every factual claim traces back to a retrievable source a lawyer can stand behind.
Honest Engineering
We surface uncertainty explicitly and refuse to produce confident-sounding output that obscures what the model does not know.