see how it works
Invent faster with AI built for the bench
Albert is the first chemistry-native AI operating system for enterprise R&D. Built for chemists, by chemists.
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Instantly surface institutional knowledge with a retrieval model that’s 45x more accurate than general-purpose AI
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Works on day 1 across structured and unstructured data
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Execute actions with natural language to create new notebook entries, analyze data, or visualize trends
See Albert in action
"Using Albert’s AI capabilities, we’ve been able to cut development time in half — screening out unnecessary experiments and reaching optimized formulations much faster."
Jeremy Pasatta
CTO, Advanced Polymer Coatings
Proven at enterprise scale
Thousands of scientists across hundreds of labs in the largest and most innovative organizations.
45x
more accurate than general-purpose retrieval models at finding the correct answers to scientific queries
7,000+
7,000+ scientists using Albert daily
15M+
molecular structures Albert’s AI is trained on
10M+
regulatory endpoints across 1,200+ lists, so compliant alternatives take minutes, not weeks
FAQ
Common questions
What is chemistry-native AI?
General-purpose AI can be fine-tuned or prompted to "understand" science. But Albert's chemistry-native AI is built specifically for science from the molecule up, from its fundamental architecture and embedding model, to the harness and the way it represents and reasons about formulation concepts.
How is Albert different from our ELN or LIMS?
Most ELN and LIMS systems were designed 20+ years ago and shoehorn in AI features. Albert was built from day one as a chemistry-native AI operating system. Every experiment trains models specific to your chemistry, and the system gets smarter as you discover.
Why not use general-purpose AI like ChatGPT or Copilot?
General-purpose AI knows published chemistry. It doesn’t know yours: your raw materials, process windows and failures, none of which is on the internet. It also can’t tell a formulation from a synthesis route, or a failure from a near miss. In a recent study, Albert’s chemistry-native retrieval model was 45x more accurate than general-purpose retrieval models at finding the correct answers to scientific queries.
Does our data need to be cleaned up first?
No. Albert works on day one across structured and unstructured data, whether it’s patents, technical data sheets, legacy notebooks, PDFs or Excel. No data overhaul required.
How do you protect our data?
Zero data retention: prompts and outputs are deleted after generation. Access is permissions-aware, so scientists only see what they’re cleared to see. Full audit trails, plus SOC 2 Type II, ISO 27001 and 21 CFR Part 11.
We’ve rolled out ELN or LIMS systems before and adoption didn’t stick. How is Albert different?
We don’t implement software and leave. Our forward-deployed scientists embed with your team throughout the entire journey, helping to build AI fluency in your people and workflows. We measure ourselves on your success, not checklists.