Ever had a formulation look perfect on paper, then die at the “can we even use this in the EU?” question?

R&D teams already know “it works in the lab” isn’t enough. The pain is that evidence, regulatory constraints, and commercial signals live in different systems, so teams lose weeks validating feasibility and compliance.

We wanted to solve this issue with Slate, our AI-powered R&D intelligence platform.

So in 2025, we asked: could we build judgment into the system? Could Slate evolve from a search engine into a research partner — one that understands context, not just content?

But if you’ve tried AI for research, you’ve probably seen the problem: it can sound confident and still be wrong, which costs time and trust.

We solved this by building Slate to think like a skeptic. When Slate makes a claim, it has already hunted for the studies that would contradict it.

Then we added another layer: market context.

Slate considers not just the science, but the constraints around it — regulations, consumer needs, and commercial trends — because corporate research can’t afford to chase ideas that won’t fly in the real world.

In November, we shipped version 2.0. Slate now offers calibrated opinions. It extracts what matters without forcing users to read hundreds of papers and patents.

To get a feel for it, try asking questions like:

“What new problems is L’Oréal trying to solve?”

“How is Amcor achieving its sustainability goals?”

“What patterns stand out in Nestlé’s recent R&D activity?”

You can ask questions here: https://slaternd.greyb.com

Slate was built for the kind of R&D planning where evidence, feasibility, and commercial reality all need to be checked before a team commits.