Imagine your organization: a collection of best-of-breed SaaS solutions, each promising to solve a specific challenge.

But as these tools accumulate, they often become a tangled mess, a Frankenstein-like architecture that's difficult to manage.

Workflows are frequently stalled by blocking processes, waiting for API responses or manual intervention. The lack of a standardized and centralized exchange formats make data sharing a herculean task.

Features overlap, leading to redundant subscriptions and unnecessary costs.

Governance becomes a nightmare, with unclear data boundaries and a lack of visibility into where critical information resides.

And the challenge of bringing data and computational power together efficiently only adds to the complexity.

Life Science Scientists are overwhelmed with the amount of available data and knowledge.

Papers are published in thousands everyday. Keeping up even in very specialized fields is difficult.

There are plenty of sources for bioinformatics data, pathways, information about diseases and their mechanisms, etc.

Data and Knowledge are produced by thousands of technological means (DNA/RNA sequencing, proteomics, metabolomics, single-cell sequencing, imaging, etc.) within companies and some become available on public data sources (e.g. GEO).

The need for AI solutions is immense and growing. That's the reason why we are building a multi-agent Graph-of-Thought set of software bricks specially targeting Life Science.

Enabling scientists to explore a domain the way they choose, providing context information, summaries, sources of truths, guidance from all possible sources, using GenAI, that's what we do for Biopharma.

But in Pharma, serendipity has to be grounded in sound science as eventually it will be used to produce a new drug or device.

Therefore all our GenAI take advantage of our T2R2™ platform to ensure ultimate traceability and reproducibility of all results.