The slowdown hides in the handoffs. Teams have strong models and plenty of data; momentum slips when coordination lags. What helps: - A unified data plane so public and private signals work in the same model paradigm - An orchestration layer for ensembles, versions, and swaps -

At Myspace and Jam City, progress followed sound orchestration: Data consistent, interfaces standard, feedback loops tight. Health follows the same pattern. When discovery, trials, and care follow the same structure, work moves faster and the results are clearer. AI adds new

Most companies don’t lack AI tools. They lack the structure to use them well. New models launch every week. But integrating them into real systems—across data sources, teams, and compliance environments—is still the hard part. Salt provides that on-ramp. We’ve built a platform

I’ve always leaned toward high-leverage decisions. Ones that come with risk, but also meaningful outcomes. That mindset shapes how I build teams too. Across the companies I’ve helped start, the constant has been structure: – Independent leaders – Shared vision – Clear roles

At scale, the barrier isn’t intelligence. It’s integration. The models are good enough. The handoff breaks. #AIInfrastructure #LifeSciences #SaltAI

LLMs are great at language. They’re still clumsy with logic. That’s where orchestration comes in—structure that thinks in steps, not just tokens. #LLMEngineering #AgenticWorkflows #SaltAI

Most AI drug discovery runs in batches. Generate candidates → analyze outputs → adjust parameters → repeat. But what if that loop became continuous? → generation → analysis → feedback → regeneration → … With dynamic graphs, discovery can evolve in real time. You don’t

Orchestration isn’t overhead. It’s the thing that shaves 3 years off a clinical timeline. #SaltAI #ClinicalInfrastructure

Most platforms are built to validate assumptions. @getsalt_ai is built to challenge them. That means tight iteration loops—build, measure, learn—not quarterly product planning cycles. You don’t need to be “right” at the start. You need a system that lets domain experts test,

A recent arXiv study pointed out something I see often: In life sciences, AI results often can’t be reused or verified—and messy, fragmented pipelines make that worse. They also burn more compute than they need to. Open, orchestrated labs make results traceable, auditable, and

Speed in drug development doesn’t come from doing more. It comes from better alignment. Pharma Manufacturing reports that integrating R&D, manufacturing, and supply can shave up to three years off development timelines. That’s not a tooling win. It’s an synchronization win. –

The VTX3232 Parkinson’s trial had a familiar problem: too many biomarkers, not enough structure. Small sample. High noise. Conflicting signals. That’s not a science issue... It’s a systems one. In CNS and inflammation: – Biomarkers behave differently – Metadata slips through

In science, losing metadata is like running an assay blindfolded. You might think you’re measuring X—but without sample IDs, protocol versions, and parameter logs, you’re just guessing. At @getsalt_ai our orchestration pipelines preserve every detail—so your assays never lose

I’ve seen teams lose months of assay data in transit—because the pipeline wasn’t built to preserve context. In biopharma, that fragmentation shows up as degraded model outputs and stalled studies. It’s not about more AI. It’s about pipelines that: – Enforce metadata tagging for

Some trials fail. Others stall. Breakthroughs run on AI pipelines that enforce protocols, preserve context, and encrypt data end-to-end. Roche’s June 16, 2025 press release shows success after setbacks isn’t accidental—it comes from a secure, purpose-built system that

Not every team is running large-scale infrastructure. But with DeepMind’s new AlphaGenome API, high-resolution genomic predictions are now accessible—even without a full stack. The model delivers single–base-pair predictions for gene expression, splicing, chromatin features, and

Not every team is running large-scale infrastructure. But with DeepMind’s new AlphaGenome API, high-resolution genomic predictions are now accessible—even without a full stack. The model delivers single–base-pair predictions for gene expression, splicing, chromatin features, and

Imagine securely orchestrating AlphaGenome calls alongside other specialized models—then toggling your configuration to test an alternative splicing predictor or add a new chromatin-state analysis. All tracked with full lineage, like version control for your science. That’s the

We’ve seen this a lot lately: The board says, “We need an AI strategy.” And now someone—usually the CTO or R&D lead—is expected to deliver it, fast. Most platforms pick up after the strategy’s already in place. Salt starts earlier. We help teams make sense of what’s happening

Every sequencing project surfaces thousands of non-coding variants—most never make it past the noise. Google DeepMind’s new AlphaGenome API changes the game: it delivers single–base-pair resolution predictions across gene expression, splicing, chromatin structure, and contact