DIX is the India-based intelligence infrastructure arm of the SBI · SBX group. We build the production systems — agents, models, workflows, and decision platforms — that power the decision substrate across hospitals, life sciences, and pharma R&D.
DIX extends the lineage that built The Systems Biology Institute (Tokyo, 2000) and SBX Corporation (2011). Founded by a pioneer in systems biology, Dr Hiroaki Kitano, over two decades of leadership in AI, computational biology, and applied software,compounding globally from Japan, Canada and now 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.
Interfaces, agents, feedback loops, and audit trails that move intelligence from model output into real clinical, scientific, and portfolio decisions.
Diverse teams of PhDs, engineers, and designers, working alongside the partner for creating a living decision 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 foundation of everything that followed. ↗ sbi.jp
Translates SBI research into deployed solutions for drug discovery, clinical decision-making, and personalised healthcare through platforms developed across two decades of partnerships with global partners in pharma, life-sciences and healthcare. SBX BioSciences (Vancouver, 2020) extends the footprint in North America. ↗ sbx-corp.com
Building the common intelligence substrate for the group globally. The same lineage, the same mission, now creating impact at scale from India.
Strenthening the team of engineers, scientists, and creators across the
group in Tokyo and Vancouver.
We are hiring →
Discharge, escalation, capacity, documentation, and pathway adherence: the high-frequency decisions whose quality compounds into patient outcomes. DIX engineers the intelligence layer behind Disha, built for clinicians and hospital teams on shift.
Multi-omics, multi-modal, multi-models (MoMa) and molecular reasoning for compound selection, mechanism inference, trial design, and translational risk. Grounded in two decades of partnerships with global pharma.
A deep context engine for the decisions that shape a portfolio, a pipeline, a product and expands the aperture of strategic decision lens for global companies. Evidence, scenarios, and trade-offs in a single orchestrated interface developed at DIX.
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 creating the standards in biology 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 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 teams do 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 contextually 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.
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 →