NEW RESEARCH

Your AI tools are working as designed. They just weren't designed for chemistry.

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Can your AI tell the right formulation passage from a convincing near-miss?

AI is on your R&D transformation agenda. But general-purpose AI makes your team settle for “close enough” in work where a tenth of a percent in composition can change the outcome.

Your chemists know the difference between nearly correct and actually right, and so does chemistry-native AI. Generic retrieval models often don’t.

Albert put general and domain-specific AI models to the test across 590,000+ formulation patents, 55,000+ scientific queries and 1 million passages. On the hardest questions, Albert’s chemistry-native AI model, Valence, achieved 45x greater retrieval accuracy than the leading general-purpose model.

Get the full study, and see why you can’t afford “almost" in R&D decisions.

What’s inside

  • Which leading general-purpose AI models were tested against the chemistry-native model, with the head-to-head results
  • Why general-purpose AI couldn't distinguish scuff-resistant latex polymers from calcium carbonate coatings
  • Why generic AI models look promising in pilots, but break down in enterprise-scale knowledge bases
  • What domain-specific AI trained on your chemistry can deliver

“The challenge is never a lack of data, it’s connecting it. Albert lets our teams query everything in one place, from raw materials to notebook entries, instead of hunting across systems... this is going to reshape how we manage knowledge across R&D.”

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Milena Rosso-Vasic
Program Manager
AkzoNobel

7,000+

scientists use Albert daily, across 20+ enterprise customers in 60+ countries.

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Invent faster with AI that knows your science

Albert is the first chemistry-native AI operating system for enterprise R&D. Built by chemists, for chemists, it's grounded in your data, getting smarter with every experiment your labs run. Surface accurate answers, automate tedious tasks, and accelerate discovery directly within your R&D team's workflow.