DIX is an AI / ML engineering studio with deep roots in biology, biomedicine, and the software that puts them to work. We build production systems (agents, models, decision platforms) for hospitals, pharma R&D, and the strategic decisions that depend on both.
Founded by Dr Hiroaki Kitano, DIX is the India studio of the lineage that built The Systems Biology Institute (Tokyo, 2000) and SBX Corporation (2011). Twenty-five years of AI, computational biology, and applied software, now compounding from a base in India.
What we do →Foundation-model adaptation, multi-omics learning, causal inference, and agentic systems, built for real biomedical data, not benchmarks.
Production platforms for drug discovery, clinical workflows, and translational research. Reproducible, observable, hardened by use.
The surfaces, agents, and feedback loops that put a model into the hands of a clinician, a scientist, or a decision-maker, and improve with use.
Mixed teams of PhDs, engineers, and designers, working alongside the partner for the life of the system.
A non-profit research institute founded to advance systems biology and translate it into medicine. Home of the Nobel Turing Challenge: to build an AI that can make discoveries worthy of the Nobel by 2050. The intellectual upstream of everything that followed.
↗ sbi.jpTurns SBI research into deployed solutions for drug discovery, clinical decision-making, and personalised healthcare. Has hardened the Garuda, Taxila, and Gandhara platforms across two decades of partnerships with global pharma. SBX BioSciences (Vancouver, 2020) extends the work into predictive biology.
↗ sbx-corp.comA new chapter, building from India. The same lineage, the same mission, with global partners in pharma, hospitals, and health systems.
Plus PhDs, engineers, scientists, and designers across the group in Tokyo and Vancouver. We are hiring →
Discharge, escalation, capacity, pathway adherence: the high-frequency clinical decisions whose quality compounds into outcomes. Disha, our hospital initiative, is being built for the clinician on shift.
Multi-omics and molecular reasoning for compound selection, mechanism inference, trial design, and translational risk. Grounded in two decades of partnerships with global pharma.
A reasoning workbench for the decisions that shape a portfolio, a pipeline, or a five-year plan. Evidence, scenarios, and trade-offs in a single workspace.
Most teams arriving at decision intelligence today are retrofitting language models onto problems they have not lived with. We arrive from the other direction: from mechanism, from platforms hardened over years inside SBX, from peer-reviewed work the field still cites, and from a bench of PhDs, clinicians, engineers, and designers.
Biology, biomedicine, mechanism. The institute that helped pioneer systems biology and contribute to SBML is upstream of us, so the way we model a disease, a pathway, or a clinical workflow begins with how it actually works, not with the data shape it leaves behind.
Our founder pioneered massively-parallel AI, intelligent robotics, and the Nobel Turing Challenge. Agentic systems, causal reasoners, and scientific co-pilots are not a 2024 pivot here. They are the through-line of three decades of work.
Garuda, Taxila, and Gandhara have been deployed across two decades of partnerships with global pharma R&D. Reliability, observability, reproducibility, and safety are first-class concerns built in by people who have run them at scale.
We do not split “domain” from “tech”. Our biologists code. Our engineers read papers. Our designers sit with users. The unit of work is a decision, and the team is whatever it takes to engineer it well.
Connects heterogeneous tools, data, and analyses into reproducible scientific workflows.
Extracts mechanism, evidence, and contradiction from millions of papers, patents, and trial records, and feeds it back as structure.
The AI framework (predictive models, generative agents, causal reasoners) trained on the structured biology that Garuda and Taxila produce.
Biologists and biomedicine PhDs who code. Causal and ML researchers who care more about whether a model is right than whether it scores. Engineers who have shipped at scale and lived with the consequences. Designers who treat a screen as the place where a decision is taken. If that sounds like you, write to us.
The group built the engine of scientific discovery. DIX builds the engine of the decisions that depend on it.
We work in deeply-embedded teams with a handful of partners each year. If you operate in healthcare, pharma, or hospitals and have decisions worth engineering, write to us.
hr@sbx-corp.com →