About
The intelligence layer for engineering.
We’re building an AI-native engineering platform that fundamentally changes how complex physical systems are designed — and over time, evolves into a new class of foundation model for engineering itself.
The Shift
From AI as autocomplete to AI as an engineering partner.
An engineer describes a system in natural language — a drone propulsion assembly, a structural bracket under load, a thermal management loop. The platform translates that into structured design artifacts, then orchestrates the sequence: generate geometry, apply constraints, run simulations, evaluate performance, iterate.
Engineering is reasoning under constraints — every design balances strength against weight, performance against cost, efficiency against manufacturability. Most current AI-native approaches operate at the surface. We don’t.
We integrate into existing workflows — augmenting CAD, simulation, and analysis with intelligence that speaks both the language of engineering and the underlying physics.
How we’re different
From passive tools to active collaborators
CAD, simulation, control, VLSI — today's engineering software executes instructions but doesn't understand intent. We sit on top of existing tools as a domain-aware orchestration layer that translates natural-language intent into structured design artifacts.
Physics-aware latent space
Generative design today produces plausible shapes without physical grounding — Gaussian noise dressed as engineering. We represent geometry, constraints, materials, and governing equations as a single interconnected entity the system can reason about, not just sample from.
Orchestrator of specialized agents
An orchestrator decomposes engineering problems into sub-tasks and delegates to geometry, simulation, optimization, and validation agents. They maintain context across iterations and converge toward outputs that are syntactically correct and physically valid.
A horizontal intelligence layer
Mechanical, aerospace, electrical, chemical. If OpenAI and Anthropic are becoming the productivity layer for knowledge work, we aim to become the intelligence layer for engineering — starting with AI-assisted CAD and simulation, compressing design cycles from weeks to hours.
Founders
Dhruv Chaturvedi
Co-founder
Building the orchestration layer and physics-aware latent space at the core of CogniCAD. Background in computational geometry and applied ML for engineering systems.
[Co-founder Name]
Co-founder
[Replace this placeholder with your co-founder's bio — background, prior work, and the part of CogniCAD they own.]
Long-Term Vision
The Large Spatial Engineering Model.
Frontier models excel at language and code, but lack a deep understanding of space. Engineering is, at its core, a spatial discipline — wings, chips, heat exchangers, robots — geometry, topology, and constraints interacting in three dimensions.
LSEM unifies geometry, physics, and constraints in a single latent space. It enables reasoning across structure and physics simultaneously — not just generating designs, but iteratively refining them while explaining its reasoning. Engineering cognition at scale.
Mission
In the near term, we accelerate engineers. In the long term, we redefine engineering itself — the intelligence layer for the machines, systems, and infrastructure that define the real world.