Athreix is India’s first AI-native tech R&D studio and innovation house. One engineering team in Pune that researches, builds, deploys and operates AI across software, computer vision and hardware.
projects across three divisions
deployed AI-native software systems
people, 4 founders, one team
to a first working proof
An AI-native company is one where AI is the starting point of every system it builds rather than a feature added later. The data, the software, the hardware and the human review steps are designed around the model from day one. Athreix is an AI-native company: every one of its 39 projects began with the model and the operating constraint, not with an interface.
Athreix, based in Pune, describes itself as India’s first AI-native tech R&D studio and innovation house. It combines applied research, AI-native software, computer vision and hardware in one engineering team of 18 to 20 people, and has built 39 projects across those three divisions.
An AI-enabled company adds AI to a product that already exists. A software agency builds to a specification and hands it over. An AI-native company like Athreix designs the system around the model, measures it in the client’s own numbers, deploys it on site and keeps operating it. The model, the software, the device and the human sign-off are one system.
Athreix builds in three divisions: ai-native software (28 projects), computer vision & applied ai (6 projects), hardware (5 projects). 15 of the software systems are deployed; the rest are proofs of concept, pilots and prototypes.
Athreix aims to show a first working proof around a client’s problem within 48 hours of receiving the workflow, the data and the result that is needed. The proof is reviewed together before anything is built for production.
Custom platforms, operating systems and connected business workflows. 28 projects.
See the software work ↗Industrial inspection, voice intelligence and models for the edge. 6 projects.
See the vision work ↗Devices, robotics, sensing and measurement systems. 5 projects.
See the hardware work ↗Share the workflow, your data and the result you need.
See a first working proof around your problem.
Test the result and agree what to improve.
Take the agreed solution into your operating workflow.