From bench to business case.

You're exploring a new process chemistry or production route and need to know if it's worth pursuing before you hire a team or raise a dime. You need rigorous competitive analysis and unit economics modeling to figure out where your technology wins and where it doesn't.

You've got a working process at bench scale and a spreadsheet held together with duct tape. It works but it's not connected to the experiment data living in Google Sheets and lab notebooks. You need cleaner structure so the real data you generate connects to the right models, and helps you tell the story.

Your startup is outgrowing Google Sheets but isn't ready for a full data team. Experiment results live in five places, nothing talks to each other, and your 'pipeline' is someone copy-pasting every Monday. You need lightweight infrastructure that grows with you, not an enterprise SaaS bandaid.

You have a techno-economic model from two years ago. It's in Excel, it's fragile, hard to use, and nobody remembers what half the assumptions mean. You need to audit and rebuild it into a tool your team actually uses to inform strategic planning and experimental decisions.

You're paying for Gemini, Copilot, Claude, and three other AI tools your team has tabs open to. They're saving time on small things and creating noise on the big ones. We set up the advanced tooling that actually pays off: agent skills, CLI code tools, automated literature review, data cleanup pipelines, and synthesis workflows. The goal is figuring out where AI earns its keep, and where it's just expensive auto-complete.