For UK beauty, cosmetics and personal-care brands
Packaging data built for beauty brands.
Model your packaging once. Assess RAM 2027. Forecast EPR fees. Keep the supplier evidence behind every decision.
Free plan for your first packs. No card required.
The problem
Beauty packaging is more complicated than a spreadsheet.
A serum is a glass bottle, a pipette, a bulb, a collar, a cap, a label with an adhesive nobody has asked about, and a carton. Forty shades share one pack. Suppliers change specifications without telling you. RAM 2027 asks questions about polymers, densities, coatings and sortability that live in PDFs and inboxes. Generic EPR tools make you rebuild every pack from unnamed boxes.
Build reusable pack architectures
Model a 30ml dropper or a 50ml airless pump once, with nested components, and let every SKU that uses it inherit the data. Change the dropper spec and see every affected product instantly.
Understand RAM results
Every assessment shows each stage, each rule that fired, the facts it used and the guidance section it came from. No black boxes, and no result you cannot explain to an auditor.
Find missing supplier data
BeautyPack knows which facts each rule needs. Data gaps are listed per component with the reason, the stage that needs it and a one-click request to the supplier.
Model packaging redesigns
Clone a pack into a scenario, swap the label adhesive or closure polymer, reassess, and compare the RAM result and indicative annual fee difference side by side.
Create audit-ready packaging records
Facts carry provenance: supplier declared, confirmed by you, or independently verified. Assessments are versioned and immutable. Seven-year audit packs come from the same data.
Built specifically for beauty and personal care
Pumps, droppers, compacts, mascara packs, sticks, aerosols and sachets are first-class citizens, not a drop-down of generic containers.
Check a pack against RAM 2027 in two minutes.
The free readiness checker walks through the facts RAM 2027 needs for a bottle, jar, tube or pump and shows what is missing. Save the result into your account when you sign up.