Redefining
ManufacturingManufac-
turing with AI

Chemistry advances by understanding how molecules transform, predicting reaction pathways, and learning from experimental outcomes.

Molecular properties are necessary, but not sufficient. Real progress happens when we understand how reactions proceed: the mechanisms, conditions, and optimizations that turn starting materials into products. At Mstack, we are building AI that understands chemistry at the reaction level, not just the molecular level.

Until now, chemical synthesis has relied on knowledge scattered across decades of literature and trapped in the experience of senior chemists. Traditional approaches focus on what molecules are (their structures and properties). But synthesis depends on understanding what molecules do: how they react, transform, and can be efficiently produced.

Traditional R&D timelines stretch 18+ months because every synthesis pathway must be manually designed, tested, and optimized. Companies seek to mine historical data, but as any chemist knows: though reading synthesis papers provides direction, you eventually need to run the reaction to see if it works under your specific conditions.

We're a science company solving this problem.

Why reaction-level understanding?

Industrial progress is limited by our ability to synthesize molecules efficiently and at scale.

The challenge isn't knowing molecular structures. It's predicting which synthetic routes will work, understanding why reactions succeed or fail, and optimizing processes for real-world manufacturing. AI has made progress on molecular properties, but chemistry happens at the reaction level: catalysts, solvents, temperatures, work-up procedures, and the subtle interplay of conditions that determine whether a synthesis succeeds.

We're focused on specialty chemicals because synthesis experiments provide clear success metrics and reactions follow understood principles that our AI can learn from. Our Chemstack AI platform builds reaction-level intelligence through modules like LiteratureIQ (mining synthesis knowledge from scientific literature), RetroRank (predicting optimal synthetic routes), and Experimentation Assist (learning from experimental outcomes).

This isn't theoretical research. We're deploying these capabilities with industry today. We're helping Fortune 500 chemical companies compress development cycles from 18+ months to days by building AI that understands not just what molecules are, but how to make them.

Our Approach

The key is closed-loop learning: our AI proposes synthesis routes, we validate them through experimentation, and the results feed back to improve predictions. Each experiment generates insights about reaction conditions, failure modes, and optimization strategies that exist nowhere in published literature. This creates a compounding advantage: our reaction-level understanding deepens with every synthesis we run.

We're working across production chemicals for oil & gas, agrochemicals, and coatings, adhesives, sealants and elastomers (CASE). Different applications, same core challenge: turning molecular knowledge into efficient, scalable synthesis processes.

We're backed by leading investors including Lightspeed Ventures and Alpha Wave Global who share our vision for AI-first chemistry. We're a science company focused on understanding reactions, optimizing synthesis, and making chemistry faster.