Your model designed it.
Our autonomous lab tests it.
We operate a compact, fully autonomous R&D lab in Hyderabad. It is running now. We are opening selected programs to academic and industry teams while raising capital to expand its instruments, assays, and throughput.
Biology is becoming programmable
Models can now generate proteins, sequences, constructs, and experimental plans faster than laboratories can test them. The scarce resource is no longer a plausible design. It is trustworthy physical evidence.
Programmable biology needs an execution layer: software translates intent into lab operations, automation runs the experiment, and structured results flow back into the model. Our Hyderabad lab is the first compact implementation of that loop.
Design → execute → measure → learn. The product is not a robot or a report. It is a repeatable experimental loop.
From computational design to experimental evidence
Initial programs are reviewed with our team so we only accept work the current cell can execute reliably. Standardized workflows will move into transparent, self-serve ordering as the public catalog expands.
Start with a working cell. Scale into a network.
Automation already running
The core execution loop is operational in our Hyderabad R&D setup. Funding expands breadth and throughput rather than financing a first demonstration.
Price parity as a baseline
Our launch targets match published cloud-lab prices. The India cost base is intended to preserve margin while keeping experiments accessible.
Model-ready output
Each standardized assay is designed to return structured results, raw files, QC context, and execution provenance.
Clinical context is proposed
We are pursuing, but have not signed, an Apollo partnership for consented clinical and biobank access. It is an expansion path, not a current asset.
One lab, two access models
Academic R&D
Small, grant-sized experimental batches for teams whose computational output is growing faster than their bench capacity.
Industry R&D
Pilot programs now, followed by reserved capacity and API-driven design–build–test–learn loops as the fleet scales.