Merentia predicts formulation properties from molecular structure, designs backwards from your target specs, and drafts regulatory dossiers across jurisdictions — collapsing 24-month R&D cycles into weeks.
Materials informatics raised nine figures — then aimed entirely at the giants. The 8,000 mid-market specialty chemical makers were left burning 18 to 24 months and millions in lab time on every new formulation.
Every objective passes through the same pipeline — property prediction, inverse design, and regulatory clearance — before a single beaker is touched. The lab becomes validation, not discovery.
See the platform in action →Property models map molecular structure straight to performance — gloss, VOC, glass-transition, viscosity, cure profile — so you know how a formulation behaves before it ever reaches the bench.
State the properties you need; Merentia returns candidate formulations that hit them — ranked by predicted performance, raw-material cost, and supply availability, not just chemistry.
Automated dossier generation cross-checks each candidate against REACH, TSCA, and K-REACH — then drafts the submission per jurisdiction, reclaiming the 10% of budget regulatory work quietly consumes.
A formulation is a search problem, not a guessing game. Every result Merentia returns is predicted, ranked, and regulatory-checked — before you spend a day in the lab.
A proprietary formulation dataset that compounds with every batch, a technical-service copilot trained on your history, and models tuned to the specialty chemistry your competitors can't see.
Every historical batch and QC record you ingest sharpens the models — a proprietary formulation dataset that becomes a defensible moat no competitor and no general model can replicate.
A technical-service copilot trained on your formulation history answers routine customer questions instantly — so your best chemists solve hard problems instead of clearing an inbox.
Batch-to-batch consistency stops being a manual QC chore — the platform monitors production data and flags deviation before a bad batch reaches a customer.
Forecast performance straight from molecular structure — gloss, VOC, viscosity, cure — before any lab work begins.
Set your target specs and get ranked candidate formulations that hit them, weighted by cost and supply.
Automated submission drafting across REACH, TSCA, and K-REACH — the compliance burden, mostly gone.
A service assistant trained on your data that handles routine customer chemistry so senior chemists don't.
Merentia comes from a simple conviction: the mid-market specialty chemical world is running twenty-year-old R&D cycles while a handful of giants pull ahead with materials AI. That gap isn't a technology problem — the science exists. It's a distribution problem, and closing it is the whole company.
Shriman builds at the intersection of messy operational data and AI — turning the kind of information that normally lives in a senior chemist's head and a decade of batch records into models a manufacturer can actually deploy. Merentia is that thesis aimed at an $8,000-manufacturer market everyone else ignored.
Your formulation data is your entire business. It's isolated per-tenant, never used to train another manufacturer's models, and governed by enterprise security from day one — because a shared moat is no moat at all.